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  • DotActiv Team
  • 14 min read

Product Update: Everything New in DotActiv This Month

Keeping store clusters accurate, shelf spaces balanced, and in-store execution tight shouldn’t require endless spreadsheets or messy email chains. This week’s release focuses on stripping the manual friction out of category management, from automated AI analysis down to single-click planogram approvals.

1. Eliminate Clustering Guesswork with AI Clustering

For category managers and space planners, grouping stores into meaningful clusters has traditionally been a time-consuming exercise. Standard mathematical algorithms crunch numbers efficiently, but they leave planners with a frustrating black box: Why were these specific 15 stores grouped together?

Without clear explanations, planners spend hours manually cross-referencing sales share, SKU performance, and store formats to justify groupings. Worse yet, selecting optimal cluster counts often relied on trial-and-error guesswork, risking operational inefficiencies or oversimplified strategies.

Introducing DotActiv’s AI Clustering

By incorporating intelligent analysis models, DotActiv now automatically calculates your optimal cluster counts and delivers plain-language narrative justifications for every single store grouping.

How It Drives Efficiency

  • Clear, Descriptive Rationales: Automatically generates concise justifications for store groupings (e.g., “Stores with high premium coffee demand but low instant coffee sales share”), saving hours of manual data interpretation.
  • Automated Cluster Count Determination: Removes guesswork by automatically identifying the target cluster count and limit based on your data set.
  • Deeper Category Context: Effortlessly incorporates rich metrics like Profit per NOD, SKUs per NOD, and Category Sales Share of Store to pinpoint over- or under-performing categories in seconds.
  • Full Human Control: Integrated technical validation tools (Silhouette Index and Linkage Distance) give planners complete control to validate and refine recommendations before saving changes to the database.

How It Works in Practice

A senior space planner preparing a bi-annual category review for Coffee across 200 retail locations. Using DotActiv’s AI Clustering, the planner sets a 12-month historical period and enables Category Sales Share of Store alongside Profit per NOD.

Within seconds, the system calculates the optimal cluster count and reveals that 25 stores, previously grouped as “STANDARD”, is actually over-index on premium espresso pods while underperforming in instant coffee. Instead of spending two days building custom Excel pivot tables, the planner reads the clear narrative rationale, validates cluster separation via Silhouette Index, and adds the stores to the “PREMIUM” cluster.

Get Started with AI Clustering

  • Existing Customers: Log into DotActiv and head to Clustering > Optimizer to try the new diagnostic tools, or check out our step-by-step PowerBase guide, AI-Optimized Store Clustering: Technical Capabilities and Operational Framework. Alternatively, book a demo with Customer Success.
  • New to DotActiv?: Discover how DotActiv can streamline your clustering. Book a demo with a DotActiv Expert.

2. Fix Range Mismatches and Cut Planning Time in DotActiv

Managing category ranges has traditionally been a tedious guessing game. Category planners spend hours juggling fragmented spreadsheets and manually cross-referencing store cluster lists, only to discover range mismatches, duplicate ranges, and dormant SKUs eating up valuable shelf space after implementation. 

Without visibility into whether a product was correctly ranged or actually physically fit on the shelf, correcting errors meant starting the tedious process all over again. This lag severely slowed down speed-to-market for new listings and drained strategic productivity.

Here’s a Smarter Way to Range

This feature was built to replace manual, spreadsheet-heavy checks with direct visual feedback. By linking your ranging data directly to your store planograms, you can make accurate range decisions faster and respond immediately to changing market demands.

How It Drives Efficiency

  • Fixes Range Mismatches: Instantly shows you which products aren’t ranged consistently across stores in the same cluster.
  • Stops Shelf Overcrowding: The Congestion Metric shows you what percentage of shelf space a range will take up at a single facing, so you know if it will physically fit on your smallest store planogram before you commit.
  • Cleans Up SKUs: Built-in filters highlight Not Ranged products (items listed but not yet ranged) and Dormant SKUs (items with zero sales over six months) so you can add or remove them quickly.
  • Interactive Editing: Turn POG On/Off with a click, use Floating Shelves to organise items by sales, and click any product in your grid to see exactly where it sits on the visual planogram.
  • Cuts Out Duplicate Work: Automatically flags clusters that have identical planograms so you can merge them instead of updating both manually.

How It Works in Practice

A category manager notices their team is maintaining 12 different planograms for a single category. By running Range Similarity Analysis, the software detects that Clusters 3, 7, and 9 share a nearly identical product mix. With a couple of clicks, the planner merges the three clusters into one, instantly reducing ongoing planogram maintenance by 25%.

Get Started with Range Maintenance

  • Existing Customers: Log into DotActiv and head to Ranging > Maintenance to try the new diagnostic tools, or check out our step-by-step PowerBase guide, Range Maintenance: Highlights & Analytical Tools. Alternatively, book a demo with Customer Success.
  • New to DotActiv?: See how easy it is to keep your store ranges and shelf plans 100% in sync. Book a demo with a DotActiv Expert.

3. Automate Planogram Refreshes in DotActiv

Keeping planograms aligned with rapidly shifting database ranges used to mean hours of repetitive, SKU-by-SKU adjustments. Every time a new product launched or a line was discontinued, category planners had to manually reconfigure product positions from scratch. Placing new items logically, like positioning a house brand directly next to a market leader while maintaining visual brand flow, required tedious calculations. Deleting old stock left awkward gaps on the planogram or forced a complete, time-consuming rebalance. Add the bottleneck of chasing down buyers for sign-offs via endless email chains, and simple range updates quickly derailed strategic planning time.

Quicker Planogram Refreshes with Luna

DotActiv’s enhanced Luna feature automates range updates by giving you granular control over product placement for new additions and seamless removal of discontinued lines. You can now set smart merchandising rules, like placing house brands next to market leaders, establishing clone SKUs, and locking protected products, while letting Luna automatically calculate optimal facings and Days of Supply. Integrated category user assignments also trigger dynamic approval workflows, ensuring every planogram refresh moves swiftly from configuration to sign-off.

How It Drives Efficiency

  • Precision New Product Placement: Set merchandising flows, like placing house brands alongside market leaders or establishing clone SKUs, to eliminate manual positioning while keeping brand standards intact.
  • Automated Discontinued Line Clearance: Instantly remove discontinued items and equalize Days of Supply (DOS) across designated fixtures to maintain optimal shelf capacity without awkward gaps.
  • Selective Product Protection: Lock cross-merchandised, high-value, or contractually bound SKUs from facing changes during refreshes, retaining complete operational control over core lines.
  • Dynamic Approval Workflows: Automatically route planogram reviews directly to designated category users based on change thresholds, eliminating email back-and-forth and accelerating sign-off times.

How It Works in Practice

Imagine a beverage planner prepping for a seasonal range refresh where ten low-performing SKUs are being phased out, and three new house-brand sodas are launching. Using Luna, the planner sets rules to clear the discontinued items and position the new house brands directly to the right of the market leader with a two-facing minimum. They lock top-selling promotional SKUs to prevent facing changes and apply clone SKU data to project demand. Luna instantly rebalances Days of Supply across affected fixtures and routes the finalized planogram straight to the assigned buyer for sign-off, turning hours of tedious setup into a quick, streamlined process.

Get Started with Luna

  • Existing Customers: Log into DotActiv and head to Automation > Setup to get started with Luna, or check out our step-by-step PowerBase guide, Luna: Planogram Refresh Automation Configuration and Implementation. Alternatively, book a demo with Customer Success.
  • New to DotActiv?: See how easy it is to set Luna up and see results. Book a demo with a DotActiv Expert.

How Range Maintenance and Luna work together:

Range Maintenance helps you clean up your product mix by eliminating dormant SKUs, resolving mismatches, and confirming physical shelf fit. Once your updated range is finalised, it feeds directly into Luna, which automatically places new items, clears old lines, rebalances Days of Supply, and routes the new planogram for approval without manual work.

4. Streamline Planogram Quality Control in DotActiv

Ever spent hours perfecting a planogram, only for the final handoff to derail into a nightmare of broken paths, mismatched file names, or silent import failures?

Before this feature, transitioning a planogram from the planning phase to the live environment was a tedious manual slog. Planners had to juggle local FLP files, navigate restrictive VPNs, and battle unstable server directories. Worst of all, tiny unseen errors, a missing image, a misspelled category name, or a misaligned shelf, would crash the import utility at the very end of the process, forcing you back to square one. It was slow, frustrating, and a constant threat to data integrity.

Introducing Publish Planogram

DotActiv’s new Publish Planogram function transforms space planning workflows by replacing manual file transfers with a direct, database-integrated push. Located in the Space Planning tab, this administrative tool runs 24 automated validation checks to identify administrative, structural, ranging, and physical spacing errors before publication. By providing built-in shortcuts and enabling the final publish button only when all critical errors are resolved, it guarantees a seamless and error-free transition to the live environment.

How It Drives Efficiency

  • Direct Database Integration: Replaces tedious manual FLP file transfers and VPN hurdles with a single, direct push to live database storage.
  • 24 Automated Validation Checks: Acts as a preemptive strike against import crashes by catching syntax, dimensional, and ranging errors before going live.
  • One-Click Remediation: Speeds up troubleshooting using built-in shortcut buttons to tools like Range Maintenance and Cluster Maintenance.
  • Traffic-Light Quality Control: Instantly distinguishes non-negotiable “Errors” (red) from flexible “Warnings” (yellow), guaranteeing data integrity.

How It Works in Practice

Imagine a category planner finalizing a promotional layout for the Snack aisle. After adding several new chips and dip SKUs, they click Publish Planogram.

The system runs its automated checks and flags a red Error for POG and Range Alignment: a featured dip SKU placed on the shelf isn’t listed in the database’s range for that store cluster. Rather than closing the planogram to navigate through separate menus, they click the built-in shortcut to jump directly into Range Planning. They quickly confirm the ranging status, update the record, and return. The Error clears, the Publish button turns blue, and the planogram is pushed to the database.

Get Started with Publish Planogram

  • Existing Customers: Log into DotActiv and head to Space Planning > Publish Planogram to try the new tools, or check out our step-by-step PowerBase guide, Publish Planogram Functionality. Alternatively, book a demo with Customer Success.
  • New to DotActiv?: See how easy it is to quality check and revolutionize the transition of planograms from the planning phase to the database. Book a demo with a DotActiv Expert.

5. Collaborate Easily on Planograms with Direct Edits in DotActiv

The traditional planogram review process has always been a bottleneck. For years, space planners and category managers have been stuck in an endless loop of static file sharing. Planners send out a PDF or PowerPoint, and category managers or suppliers reply with tedious, text-based feedback like, “Move product X to shelf Y.”

Trying to decipher these vague descriptions, track arbitrary changes across messy email threads, and manually apply them to your planograms isn’t just frustrating; it drains hours of valuable time and leaves room for costly merchandising errors. The review process needed a serious upgrade.

Say Hello to Visual Planogram Collaboration

Enter Direct Planogram Edits in the DotActiv Viewer, a dynamic feature that allows collaborators such as category managers or suppliers to make visual modifications, like adjusting facings or product positions, directly within the software instead of sending vague emails. To guarantee that every merchandising decision is data-driven, collaborators must provide a mandatory logical motivation for their edits, which planners can then instantly accept or reject with a single click. This hands-on, transparent approach completely eliminates the guesswork and accelerates your entire approval workflow.

How It Drives Efficiency

  • Visual Planogram Edits: Replaces vague email descriptions with direct visual adjustments on the shelf, eliminating confusion and saving hours of back-and-forth communication.
  • Mandatory Edit Motivations: Requires collaborators to logically justify every suggested change and attach supporting files, ensuring your merchandising strategy remains strictly data-driven and objective.
  • One-Click Approvals: Centralizes the entire review process, allowing planners to instantly accept, reject, or provide feedback on suggested edits directly within the software.
  • Multi-Supplier Management: Easily aggregates contributions from multiple collaborators into a single dropdown view, making complex category updates and range reviews easier than ever.

How It Works in Practice

Imagine you are executing a seasonal category review and need merchandising input from three major suppliers. Instead of juggling messy email threads and PDF markups, you initiate a collaboration directly in DotActiv.

Your suppliers log in and visually adjust their product facings on the digital shelf. Because the system requires a mandatory logical motivation, they type their justification and attach supporting documents right to the edit. As the planner, you get an alert in your Action Center, open the planogram, and use the dropdown menu to review each supplier’s suggestions. With a simple click, you accept or reject the changes, instantly snapping the approved products into place. A chaotic process that used to take weeks is now finalized in days.

Get Started with Planogram Collaborations using DotActiv Viewer

6. Automate In-Store Shelf Audits with TrueView Image Recognition

Retail teams spend hours crafting optimised planograms, only to lose revenue when store shelves don’t match the design. Manual audits are slow and unreliable, often leaving category managers in the dark about incorrect facings or misplaced items until sales drop. Relying on paper checklists or random photos makes it nearly impossible to maintain shelf compliance across multiple locations.

Shelf Audits At Your Fingertips

DotActiv TrueView Image Recognition (IR) is an automated image recognition feature designed to measure planogram compliance. By scanning in-store photos against your planograms, TrueView turns simple shelf photos into compliance reports.

How It Drives Efficiency

  • Automated Shelf Audits: Field reps no longer need to check every single product SKU by hand against a printed paper planogram. TrueView automatically identifies products, overlays layout maps directly onto store photos, and flags misplaced items in seconds.
  • Streamlined In-Store Execution: The mobile application prioritises tasks by urgency (Overdue, Due Soon) and guides merchandisers through a structured process that shortens in-store audit times.
  • Dual Compliance Metrics: Automatically generate two performance metrics: a Product Existence Score (confirming items are on the shelf) and a Layout Score (evaluating spatial relationships and product positioning relative to neighbors).
  • Continuous Machine Learning: Features an intuitive “Human-in-the-Loop” web dashboard that enables quick visual retrainings during setup, boosting system accuracy with every store audit.

How It Works in Practice

Space planners create data-driven shelves and upload them to TrueView to be implemented across all locations.

Once planogram implementation is complete, store merchandisers open the TrueView Mobile App, navigate to their assigned category task, and capture straight-on photos of each shelf segment.

TrueView scans the shelf and matches physical products directly against your planograms. Back at the head office, space planners can instantly review compliance scores to pinpoint low-performing stores, allowing them to resolve execution gaps before misalignments impact revenue.

Get Started with TrueView Image Recognition

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  • DotActiv Team
  • 7 min read

Floor Planning Software for Visual Merchandising

Floor Planning Software for Visual Merchandising

Retailers spend significant time optimising shelves, yet the layout of the store itself is just as important. A well-designed planogram can improve category performance, but where categories are positioned and how selling space is allocated across the store can influence how effectively those planograms perform.

Modern floor planning software helps retailers move beyond static store layouts by bringing retail data into the planning process. Teams can analyse how different areas of the store perform, allocate space more effectively, and connect macro-space decisions with planograms and wider category management processes.

Why Floor Planning Is Essential for Visual Merchandising

Store layout influences how shoppers navigate a store, which categories they encounter and how effectively available selling space is used. Poor category positioning can leave valuable areas underutilised, create congestion and make the shopping experience inconsistent across locations.

Effective floor planning gives retailers a more structured way to make these decisions. Instead of simply asking where a category will fit, retailers can use category and store performance data to determine how space should be allocated and where different departments, categories and fixtures should be positioned.

This makes floor planning an important part of visual merchandising, particularly for retailers managing multiple categories, store formats and locations.

What to Look for in Floor Planning Software

If you’re evaluating floor planning software, look beyond tools that simply allow you to draw a store layout. Modern solutions should help retailers analyse, optimise and continuously improve how space is used across the sales floor.

The software should enable retailers to create floor plans using retail data, identify high- and low-performing areas through floor highlights and heatmapping, and measure category performance with floor reports. Heatmaps can use various KPIs depending on the retailer’s focus, allowing teams to analyse the measures most relevant to their specific objectives.

Floor planning should also connect directly to planograms and the retailer’s wider category management processes so that macro- and micro-space decisions remain aligned.

Capability

Why It Matters

Integrated floor planning

Build compliant shelf layouts in minutes instead of hours.

Heatmapping and floor highlights

Keep layouts updated as products change.

Floor performance reporting

Understand why shelves perform the way they do.

Category-based clustering

Support layouts suited to different local demographics.

Planogram integration

Connect shelf-level planning directly to the floor plan.

Space optimisation

Allocate selling space based on category and store performance.

Centralised database

Keep floor plans, planograms and retail data synchronised.

DotActiv’s floor planning functionality, including floor reporting, highlights, heatmapping and optimisation, is available as part of DotActiv Enterprise. Floor planning forms part of the broader Enterprise category management suite rather than being included as a standard capability across every DotActiv edition.

Use Data to Improve Store Layout and Shopper Flow

One of the biggest advantages of modern floor planning software is the ability to visualise how different areas of the store are performing.

Rather than relying solely on assumptions when deciding where categories should be positioned, retailers can use floor highlights and heatmaps based on different KPIs. The specific KPI used can depend on what the retailer wants to investigate, making it possible to focus the analysis on the objectives that matter most to the business.

For example, sales data can be used to highlight stronger and weaker areas of the store. Retailers can then use these insights alongside their merchandising strategy when deciding whether categories should be repositioned or whether space should be allocated differently. DotActiv’s floor planning tools have been demonstrated using hotspot analysis to identify highly active and colder areas of a store and inform decisions around category positioning.

This gives retailers another layer of information when making decisions intended to improve shopper flow, category visibility and the overall use of available selling space.

Make Better Decisions With Floor Performance Reporting

Store layouts shouldn’t remain static while category performance changes.

Modern floor planning software provides reporting tools that help retailers understand how space is being used across departments, categories and gondolas. Teams can compare the space allocated to an area of the store with relevant performance data and identify where adjustments may be required.

This creates a more measurable approach to floor planning. Instead of redesigning layouts based primarily on intuition, retailers can use actual retail performance to inform where space should be expanded, reduced or reconsidered.

Connect Floor Plans to Your Planograms

Floor planning becomes significantly more valuable when it connects directly to planograms.

Instead of treating shelf layouts and store layouts as separate projects, retailers can link planograms to their position on the floor plan. This creates a clearer connection between the retailer’s macro-space strategy and what shoppers eventually see at shelf level.

For category and merchandising teams, this means decisions about where a category sits within the store can remain connected to decisions about how products are ranged and positioned within that category.

DotActiv’s floor planning functionality integrates with planograms as part of its wider Enterprise category management environment, helping retailers manage macro- and micro-space planning within the same platform.

Optimise Space Allocation Across Categories

Not every category needs the same amount of space.

High-performing departments may justify additional selling space, while other categories may be able to maintain performance with a smaller footprint. The challenge is determining where these changes should happen without relying on assumptions.

Floor planning software gives retailers the data and visualisation tools needed to evaluate these decisions across the store. Teams can assess category performance alongside the space currently allocated and identify opportunities to use available floor space more effectively.

This connects visual merchandising decisions with commercial performance: the goal isn’t simply to create a store that looks organised, but to ensure valuable selling space is being used appropriately.

One Connected Retail Planning Platform

Floor planning delivers greater value when it forms part of the retailer’s wider category management process rather than operating as a standalone design tool.

DotActiv Enterprise connects floor planning with:

  • Category-based clustering
  • Assortment planning and range optimisation
  • Planograms
  • Retail analytics
  • Category management automation

DotActiv’s Enterprise environment uses a centralised database, helping retailers work from consistent product, store and merchandising information across different planning activities. This reduces the need to repeatedly move data between disconnected systems and helps keep floor plans, assortments and planograms aligned.

For example, clustering can help retailers account for differences between stores, range optimisation can determine which products should be included, planograms can determine how those products should be positioned on the shelf, and floor planning can connect those categories and fixtures to the wider store layout.

The result is a more connected approach to visual merchandising and category management.

Choosing the Right Floor Planning Software

Before investing in floor planning software, consider whether the platform can help you:

  • Design layouts using relevant retail data
  • Analyse store areas using different performance KPIs
  • Use heatmaps and floor highlights to identify opportunities
  • Connect floor plans to planograms
  • Optimise space allocation across departments and categories
  • Accommodate different store formats
  • Integrate floor planning with your wider merchandising and category management processes

If the software only allows you to draw a store layout, you’re missing much of the value modern floor planning can provide.

Retailers should also consider which software tier includes the functionality they need. Within DotActiv, floor planning and its associated reporting, highlighting, heatmapping and optimisation capabilities are available as part of DotActiv Enterprise.

Bring Data Into Your Retail Floor Planning

Modern floor planning software gives retailers a more informed way to manage store space. By bringing performance data into layout decisions, retailers can identify stronger and weaker areas, optimise category space and connect the overall store layout with shelf-level merchandising.

When floor planning, planograms, assortment planning, clustering and analytics work together, retailers gain a more complete view of how their available space is being used and where improvements can be made.

Ready to optimise your retail floor plans?

Book a demo to see how DotActiv Enterprise floor planning helps retailers analyse store space, connect floor plans to planograms and make more informed visual merchandising decisions.

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  • DotActiv Team
  • 6 min read

Planogram Software for Visual Merchandising

Planogram Software for Visual Merchandising

Visual merchandising has always been about creating attractive, shopper-friendly stores, but attractive shelves don’t improve sales. Every product placement affects availability, replenishment, shopper navigation, category performance, and ultimately profitability.

That’s why today’s best visual merchandising teams rely on planogram software to create data-driven merchandising strategies that can be implemented consistently across every store.

What Is Planogram Software?

Planogram software allows retailers and suppliers to create visual representations of how products should be displayed on shelves. A modern planogram includes far more than product positioning. It defines:

  • Product facings
  • Shelf and space allocation
  • Product sequence and hierarchy
  • Merchandising principles
  • Fixture utilization
  • Store-specific layouts

 

Rather than relying on spreadsheets or manual shelf drawings, retailers can standardise merchandising across hundreds or thousands of stores.

What to Look for in Category Management Software

If you’re evaluating visual merchandising software, look for solutions that support the complete merchandising workflow.

Capability

Why It Matters

AI-assisted planogram generation

Build compliant shelf layouts in minutes instead of hours.

Planogram maintenance

Keep layouts updated as products change.

Retail analytics

Understand why shelves perform the way they do.

Store clustering

Create layouts for different store types.

Collaboration

Collect feedback and edit suggestions from suppliers.

Image recognition

Verify stores implemented the approved planogram.

Integrated database

Keep product, sales and merchandising data connected.

These capabilities allow merchandising teams to spend less time building shelves and more time improving category performance.

Design Better Shopper Journeys With Data

Traditionally, building a planogram meant manually dragging products onto shelves before making endless adjustments.

With AI, instead of starting with an empty shelf, planners define merchandising principles, shopper flow, product hierarchy, and business rules. AI then generates multiple physically compliant planogram options in minutes, removing the repetitive work.

This allows teams to evaluate more merchandising scenarios without increasing planning time.

Nova: AI-Powered Planogram Generation

DotActiv’s Nova takes a different approach to planogram generation.

Rather than relying on complex scripting or fixed automation rules, Nova allows planners to describe their merchandising strategy using configurable merchandising rules such as shopper decision trees, product hierarchy, facings, and orientation. Nova then generates data-driven planograms based on those requirements.

Nova can:

  • Generate thousands of compliant planograms rapidly
  • Follow retailer merchandising rules
  • Apply consumer decision trees
  • Produce layouts for multiple store formats
  • Respect fixture and space constraints

 

The result is significantly faster planning without removing human oversight. Customer demonstrations consistently show Nova reducing planogram creation from several hours to just minutes while still allowing planners to refine the final layout.

Keeping Planograms Up to Date

Retailers constantly introduce new products, remove discontinued items, and update sales information. Traditionally, planners would manually rebuild each affected shelf, but modern software automates much of this maintenance.

Luna: Continuous Planogram Maintenance

DotActiv’s Luna continuously refreshes planograms using predefined business rules.

Instead of recreating layouts from scratch, Luna can add newly listed products, remove discontinued items, update facings, refresh layouts on a schedule, and highlight every change for planner review. This keeps merchandising current while dramatically reducing manual maintenance.

Using Analytics to Improve Visual Merchandising

Creating a visually attractive shelf doesn’t necessarily create the highest-performing shelf. Merchandising teams need to understand the data; therefore, analytics built directly into planogram software allow retailers to evaluate merchandising decisions before implementing them.

Lola: Ask Questions Instead of Building Reports

Instead of waiting for custom reports, DotActiv’s AI assistant, Lola, allows planners to ask questions in natural language.

For example:

  • Which products lost facings?
  • Which products contribute the most profit?
  • Which shelves are underperforming?
  • Show the lowest-performing facings.

 

Lola translates these questions into database queries and returns the relevant insights directly within the planning workflow.

Planning Only Creates Value If Stores Execute It

One of the biggest challenges in visual merchandising is ensuring stores actually implement approved planograms, because a perfectly designed shelf delivers little value if products are placed incorrectly in-store.

TrueView: Closing the Execution Gap

TrueView helps retailers verify execution using image recognition. Store staff capture shelf images using a mobile device; the system compares the physical shelf against the approved planogram and identifies compliance issues automatically.

Retailers can then monitor implementation progress, measure compliance by store or region, identify merchandising issues, and improve execution consistency, in order to close the loop between planning and execution.

Why Integration Matters

Many retailers still use separate systems for product data, planograms, floor planning, analytics, and store execution; however, disconnected systems create duplicated work and inconsistent data.

Modern planogram software should operate from a single database where product information, sales data, planograms, and reporting remain synchronised.

DotActiv’s unified SQL database allows planning, analytics, AI tools, and execution workflows to operate from the same source of truth, reducing manual data movement and supporting two-way integration with retail systems. This approach has also been highlighted during customer solution demonstrations where integrated data supports planning, ranging, reporting, and automation in one platform.

Choosing the Right Planogram Software

Questions to Ask Before Choosing Floor Planning Software

When comparing planogram software, ask whether the solution helps you:

  1. Create planograms faster
  2. Maintain layouts automatically
  3. Analyse merchandising performance
  4. Support multiple store formats
  5. Collaborate with internal teams and suppliers
  6. Verify execution in-store
  7. Scale without increasing manual work

 

How much manual rework happens after a planogram is first built?

What breaks when a product is delisted or a new SKU launches — do you rebuild the shelf, or does it update itself?

Can your team answer a performance question without waiting on a custom report?

Does the software hold up across different store formats, or just your flagship layout?

Can suppliers or field teams review and approve without needing a full licence?

Once a planogram is approved, how do you know it was actually implemented in-store?

As your store count grows, does your planning effort grow with it — or stay flat?

 

If the answer is no, you’re likely buying a shelf drawing tool rather than a modern visual merchandising platform.

Visual merchandising has evolved from designing attractive shelves to managing an ongoing planning process driven by data.

Modern planogram software should help retailers generate layouts faster, keep them current, measure performance, and ensure stores execute them correctly.

For most retailers, the shift isn’t dramatic — it’s incremental. Less time rebuilding shelves after every product change, fewer surprises when a planogram reaches the store, and a clearer answer to whether a merchandising decision actually paid off. That’s what a modern planogram platform should deliver. 

 

Ready to replace manual shelf design with data-driven merchandising?

Book a Demo to see how DotActiv’s AI platform cuts planogram creation from hours to minutes while maximising shelf profitability.

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  • DotActiv Team
  • 10 min read

AI Retail Solutions: Buyer’s Guide for Physical Stores

Retail AI Software Buyer's Guide: How to Choose the Right Platform

Physical retail is evolving rapidly. While e-commerce has reshaped how consumers shop, brick-and-mortar stores remain a critical part of the retail landscape. The challenge is no longer simply attracting shoppers into stores, but creating environments that are efficient, data-driven, and responsive to changing customer behaviour.

Artificial intelligence (AI) is helping retailers achieve exactly that. Rather than relying solely on historical reports and manual processes, retailers can now use AI to make faster, more informed decisions across inventory management, merchandising, category planning, and store execution.

The result is not a replacement for retail expertise, but a smarter way of applying it. Category managers, planners, and retail teams spend less time on repetitive manual tasks and more time making strategic decisions that improve sales, profitability, and the shopper experience.

Whether you’re evaluating your first AI-powered retail platform or replacing legacy software, understanding where AI delivers measurable value is the first step towards choosing the right solution.

The Evolution of Retail AI

Retail technology has traditionally focused on recording what has already happened. Point-of-sale systems, inventory reports, and spreadsheets have helped retailers monitor performance, but they rarely provide recommendations on what to do next.

Modern AI platforms move beyond reporting. By combining historical sales, product information, shopper behaviour, merchandising principles, and store-specific data, AI can generate recommendations that help retailers plan more effectively before problems arise.

This shift is particularly valuable in category management, where thousands of decisions need to be made across products, stores, fixtures, and formats. Instead of manually analysing large datasets, AI can identify patterns, surface opportunities, and automate time-consuming tasks while keeping the category manager in control.

 The winning formula in retail isn’t human vs. machine; it’s human plus machine. Top brands blend institutional knowledge with AI decision-making to execute faster, smarter, and more consistently

Why AI Matters in Physical Retail

Today’s retailers operate in an increasingly complex environment. Consumer preferences change quickly, supply chains remain unpredictable, and stores often serve very different shopper missions despite operating under the same brand.

AI helps retailers respond to this complexity by analysing significantly more data than would be practical through manual processes alone. Rather than making decisions based on historical averages, retailers can continuously optimise planning using current business conditions.

For physical retailers, AI is delivering measurable improvements across several key areas:

  • More accurate demand forecasting.
  • Improved inventory availability.
  • Better assortment decisions at store level.
  • Faster planogram creation and maintenance.
  • Improved execution of merchandising strategies in-store.
  • Greater visibility into compliance and operational performance.

Rather than functioning as separate initiatives, these capabilities become increasingly valuable when connected into a continuous planning cycle.

The Modern Retail AI Cycle

Successful retailers don’t use AI for a single task. They apply it throughout the merchandising and category management process, creating a continuous feedback loop between planning and execution.

The cycle typically follows four stages:

  1. Predict demand using historical performance, current trends, and store-level data.
  2. Optimise assortments and planograms based on local shopper behaviour, merchandising principles, and available shelf space.
  3. Verify execution by ensuring stores implement approved layouts correctly.
  4. Analyse performance to identify opportunities that improve the next planning cycle.

Each stage strengthens the next. Better planning improves execution, while better execution provides more accurate data for future planning.

Inventory, Forecasting & Assortment Optimisation

Better Demand Forecasting Starts with Better Data

Demand forecasting has always been central to retail planning, but traditional forecasting methods often rely heavily on historical sales averages. While useful, they struggle to respond to rapidly changing conditions such as promotions, seasonal demand shifts, local events, or changing shopper preferences.

AI allows retailers to move beyond static forecasting models by analysing multiple variables simultaneously. Historical sales remain important, but they are combined with additional data to produce more accurate demand forecasts.

These inputs commonly include:

  • Historical sales performance.
  • Seasonal buying patterns.
  • Promotional activity.
  • Store-specific demand.
  • Local demographic differences.
  • Product lifecycle changes.

The result is improved forecasting accuracy, helping retailers reduce both excess inventory and costly stockouts.

Inventory Optimisation

Inventory represents one of retail’s largest investments. Holding too much stock ties up working capital and increases carrying costs, while holding too little results in lost sales and dissatisfied customers.

AI helps retailers find the right balance by continuously evaluating inventory requirements at the store and SKU level.

Instead of simply reporting inventory levels, AI can recommend actions such as adjusting replenishment quantities, identifying products at risk of stockouts, highlighting slow-moving inventory, or suggesting transfers between stores.

For retailers, this translates into:

  • Improved product availability.
  • Lower inventory carrying costs.
  • Reduced waste.
  • More efficient replenishment decisions.

These recommendations become even more valuable when integrated into broader category management processes, allowing merchandising and inventory decisions to support one another rather than operate independently.

DotActiv Retail analytics and category management data via two way integration and staging tables to provide enriched data to the ERP system enabling better inventory management and stock order triggers.

Localised Assortment Planning

No two stores are exactly alike. Stores serving different neighbourhoods, income groups, or shopping missions often require different product ranges, even when operating under the same retail banner.

AI helps retailers move beyond one-size-fits-all assortments by analysing store-specific demand patterns and shopper behaviour.

Rather than creating identical assortments for every location, retailers can develop ranges that better reflect local purchasing habits, available shelf space, and store formats.

This allows teams to make more informed decisions around:

  • Which products should be listed or delisted.
  • Where additional facings are justified.
  • Which SKUs perform best in different store clusters.
  • How to balance shopper needs with limited shelf space.

Localized assortment planning turns retail locations from generic distribution points into tailored neighborhood hubs that drive both profitability for the business and relevance for the consumer.

Building the Foundation for AI

The intelligence of any AI engine is strictly bound by the quality of its input data. Clean, structured product information is the single most critical prerequisite for success. Flawed attributes, inconsistent categories, or stale data compromise AI output—no matter how sophisticated the underlying algorithms are. 

Retailers should ensure that core product information includes consistent attributes such as:

  • Product identifiers.
  • Brand.
  • Category hierarchy.
  • Product dimensions.
  • Packaging information.
  • Merchandising attributes.

Once this foundation is established, AI can begin automating many of the repetitive data management tasks that traditionally consume category management teams. For example, machine learning can assist with classifying products into the correct category hierarchy, maintaining cleaner databases, and preparing data for more advanced merchandising and planning activities.

Strong AI starts with strong data. Retailers that invest in clean, structured product information create the foundation for more accurate forecasting, better assortment decisions, and more effective category management.

DotActiv's AI Tools for Category Management

AI is most valuable when it supports the entire category management process rather than solving a single problem. DotActiv’s AI tools are designed to work together across planning, analysis, maintenance, collaboration, and in-store execution, helping retailers reduce manual effort while keeping category managers in control of the final decisions.

Creating a Connected Category Management Workflow

The real value of AI isn’t found in a single feature. It comes from connecting every stage of the category management process.

Product data feeds assortment decisions. Assortments drive planograms. Planograms are maintained as products change. Store execution is verified using image recognition, and the insights gathered from stores feed back into future planning.

Instead of treating planning and execution as separate activities, retailers create a continuous improvement cycle where every decision builds on the last.

Most retailers rely on piecemeal tech stacks—using one tool for forecasting, another for planograms, and manual spreadsheets for compliance. DotActiv unites planning, execution, and verification under one umbrella, eliminating broken data handoffs and reducing software overhead.

Choosing a Retail AI Platform

Retail AI platforms vary significantly in their capabilities. Some focus on inventory optimisation, others specialise in customer experience or pricing, while category management platforms are designed to improve merchandising, assortment planning, and store execution.

Rather than comparing feature lists alone, retailers should evaluate how well a platform supports their long-term operational goals.

Consider the Platform Architecture

The underlying architecture of an AI platform affects its performance, scalability, and ability to integrate with existing retail systems.

Most modern platforms use a combination of cloud and edge computing.

Architecture

Best Used For

Cloud Computing

Retail analytics and category management data via two-way integration and staging tables to provide enriched data to the ERP system, enabling better inventory management and stock order triggers.

Edge Computing

Processing data locally for time-sensitive tasks such as image recognition, compliance verification, and other real-time in-store applications.

Many retailers benefit from a hybrid approach that combines cloud-based analytics with edge computing for real-time execution.

Look Beyond Today's Requirements

Retail operations rarely stay the same. New stores open, categories evolve, and shopper behaviour changes over time.

When evaluating software, consider whether the platform can scale alongside your business without requiring major system changes.

Ask questions such as:

  • Can the platform support multiple store formats?
  • Does it handle store-specific assortments?
  • Will it support additional users as the business grows?
  • Can new AI capabilities be introduced without replacing the platform?
  • Does it integrate with existing ERP, POS, and business intelligence systems?

Choosing software that can grow with your organisation helps avoid costly platform changes in the future.

Questions Every Retailer Should Ask

Before investing in any retail AI platform, evaluate how well it fits your operational requirements.

Measuring Return on Investment

Like any technology investment, retail AI should be evaluated against measurable business outcomes rather than the technology itself.

While increased sales are often the most visible benefit, many of the largest returns come from improving operational efficiency and decision-making across the business.

Retailers should monitor metrics such as:

  • Inventory carrying costs.
  • Stock availability and out-of-stock rates.
  • Time spent creating and maintaining planograms.
  • Store execution and planogram compliance.
  • Labour savings through automation.
  • Category sales and profitability.
  • Approval and collaboration cycle times.

Many retailers choose to begin with a pilot project in one or two categories or store groups before expanding across the wider business. This approach allows teams to measure improvements, refine workflows, and build confidence before a larger rollout.

The Future of Physical Retail

Physical retail continues to evolve, but one thing remains constant: successful retailers make better decisions when they have better information.

Artificial intelligence is helping retailers process larger volumes of data, automate repetitive tasks, and respond more quickly to changing shopper behaviour. Rather than replacing category managers, AI gives them more time to focus on strategy, collaboration, and improving retail performance.

For retailers looking to modernise category management, the greatest value comes from connecting forecasting, assortment planning, planogram generation, maintenance, collaboration, and store execution into one continuous workflow. When these activities work together, retailers can improve efficiency, strengthen execution, and create a better shopping experience for customers.

See DotActiv's Platform in Action

DotActiv combines AI-assisted planning with proven category management processes to help retailers plan faster, execute more consistently, and maximise their return on shelf space.

From automating planograms and eliminating manual refreshes to building store clusters, optimising product ranging, powerful reports, simplifying collaboration, and verifying in-store execution—our platform provides the tools to support every stage of the category management process.

Book a personalised demo today to see how DotActiv can help your retail team make smarter, data-driven decisions.

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  • DotActiv Team
  • 14 min read

AI Strategy: Transforming Brick-and-Mortar Retail

Designing a Smart Store: How to Build an AI Retail Strategy for Brick-and-Mortar

For years, retailers viewed stores primarily as places to sell products, while digital channels became the source of customer data, behavioural insights, and continuous optimisation. Today, that distinction is disappearing. Advances in artificial intelligence (AI), computer vision, connected devices, and retail analytics are allowing physical stores to become intelligent environments that continuously generate operational insights and support better decision-making.

This shift is about far more than introducing new technology. The most successful retailers are using AI to solve practical business problems: improving on-shelf availability, reducing manual work, optimising store layouts, increasing planogram compliance, and delivering more consistent customer experiences.

In other words, becoming a smart store isn’t about adding AI for the sake of innovation. It’s about creating a retail environment where data flows continuously between planning and execution, enabling teams to make faster, better-informed decisions.

This is where a well-defined AI retail strategy becomes essential. Rather than investing in disconnected technologies, leading retailers begin with clear operational objectives before selecting the AI capabilities needed to achieve them. Whether the goal is improving inventory accuracy, optimising assortments, increasing labour productivity, or strengthening execution at shelf level, AI should support measurable business outcomes rather than operate as another standalone system.

Within this guide, we’ll explore the technologies shaping modern smart stores, where they create measurable value, and how retailers can develop an AI strategy that supports long-term operational improvement.

Where relevant, we’ll also highlight how solutions such as DotActiv’s Nova, Lola, and TrueView support category management, planogram automation, retail analytics, and in-store execution as part of a broader AI-enabled retail ecosystem.

1. Building the Operational Foundation of a Smart Store

Many retailers immediately think of cashierless checkout, interactive mirrors, or shopping apps when discussing smart stores.

In reality, the biggest opportunities often exist behind the scenes.

Customers may never notice improved inventory accuracy, better labour planning, or more efficient replenishment processes, but they certainly notice when products are unavailable, shelves are poorly maintained, or staff cannot assist them.

For this reason, successful AI strategies typically begin with operational excellence before customer-facing innovation.

AI Inventory Management: From Periodic Checks to Continuous Visibility

Inventory remains one of retail’s biggest operational challenges.

Traditional inventory management relies on periodic stock counts, manual shelf inspections, and historical sales reports. While these processes provide valuable information, they only capture a snapshot in time. Between stock checks, products may sell out, be misplaced, or never reach the shelf.

AI enables retailers to move towards continuous inventory visibility.

Using technologies such as computer vision, RFID, electronic shelf labels (ESLs), IoT sensors, and integrated inventory systems, retailers can monitor shelf conditions far more frequently than manual processes allow.

For example, computer vision systems can analyse shelf images to identify:

  • Empty facings
  • Low-stock conditions
  • Incorrect product placement
  • Pricing inconsistencies
  • Planogram deviations

Rather than replacing store teams, these technologies help prioritise work. Staff spend less time searching for problems and more time resolving them.

For category managers, this creates an important feedback loop.

Instead of assuming every approved planogram has been executed correctly, retailers gain greater visibility into what is actually happening in stores. This allows planners to distinguish between poor planograms and poor execution, two very different operational challenges.

Where DotActiv Fits

Within DotActiv’s AI ecosystem, TrueView applies image recognition to shelf photographs to help retailers verify planogram compliance, identify execution gaps, and monitor implementation across stores.

Rather than functioning as an inventory management or replenishment system, TrueView provides execution visibility that allows retailers to understand whether approved merchandising strategies have actually reached the shelf.

2. Enhancing the Customer Experience with AI

Operational excellence creates the foundation of a smart store, but customer experience is ultimately what differentiates one retailer from another.

Today’s shoppers expect physical stores to offer many of the same conveniences they experience online: relevant recommendations, product availability, knowledgeable staff, and seamless shopping journeys. While physical retail will never mirror e-commerce exactly, it can use AI to remove friction and make shopping easier without replacing the human element.

The goal is simple: give customers the information they need, when they need it, while enabling store associates to deliver better service.

Creating More Personalised Shopping Experiences

Online retailers have spent years refining recommendation engines based on browsing history, previous purchases, and customer preferences. Physical retailers can apply the same principles when customers choose to participate through loyalty programmes or retailer mobile apps.

With customer consent, AI can combine information such as:

  • Previous purchases
  • Online browsing activity
  • Loyalty programme history
  • Shopping preferences

This gives store associates greater context during customer interactions. For example, a customer researching coffee machines online may visit a store to compare options before purchasing. Rather than beginning the conversation from scratch, an associate can provide tailored recommendations, answer specific questions, and suggest complementary products based on the customer’s interests.

Personalisation should always be transparent and customer-led. Retailers should clearly communicate what information is collected, how it is used, and how customers can manage their preferences. When implemented responsibly, AI helps create shopping experiences that feel helpful rather than intrusive.

Smarter Product Discovery

Finding products quickly remains one of the biggest frustrations in large retail environments. AI-powered search capabilities are making product discovery far more intuitive.

Instead of requiring customers to know an exact product name or aisle location, shoppers can increasingly search using natural language, for example:

  • “Where can I find gluten-free pasta?”
  • “Show me shampoo for colour-treated hair.”
  • “I’m looking for size 10 hiking boots.”

AI interprets the customer’s intent and recommends the most relevant products, departments, or promotions. Whether delivered through retailer mobile apps, digital kiosks, or conversational assistants, these tools help customers find what they need faster while allowing store associates to focus on higher-value interactions.

AI-Assisted Store Associates

One of physical retail’s greatest strengths remains its people. Experienced store associates build trust, answer questions, and provide reassurance that online shopping cannot always replicate.

AI strengthens these interactions by making information easier to access. Rather than searching through multiple systems, associates can quickly retrieve:

  • Product specifications
  • Stock availability
  • Promotional information
  • Product comparisons
  • Complementary product recommendations

This enables employees to spend more time helping customers and less time searching for information.

Where DotActiv Fits

Within DotActiv’s AI platform, Lola acts as an AI-powered Category Management assistant for category managers and head office teams. Users can ask questions about reports, planograms, category performance, and product data using natural language, making it easier to analyse information and retrieve insights without manually searching through multiple reports or datasets.

Rather than replacing merchandising expertise, Lola accelerates analysis and decision-making, allowing category teams to spend less time navigating data and more time improving category performance.

This keeps it aligned with how Lola is actually used and avoids implying it’s a customer-facing or store associate tool.

AI Should Enhance, Not Replace, Human Service

The retailers delivering the strongest customer experiences are not necessarily those with the most technology. They are the ones using technology to remove friction, provide better information, and enable employees to focus on meaningful customer interactions.

Customers rarely remember the algorithms working behind the scenes. They remember whether they found what they needed, whether staff were knowledgeable, and whether the shopping experience felt easy.

AI should enable those outcomes. When applied thoughtfully, it strengthens the connection between operational excellence and customer experience, helping retailers create stores that are more responsive, more efficient, and ultimately more enjoyable to shop in.

Source: Checkers smart trolley, mybroadband.co.za

3. Merchandising and Spatial Intelligence

Dynamic Merchandising and Pricing

Retail environments are constantly changing, and merchandising plans need to keep pace. New products are launched, others are delisted, promotions begin and end, and shopper demand shifts over time. Keeping shelves aligned with these changes has traditionally required significant manual effort.

Electronic Shelf Labels (ESLs), combined with integrated pricing systems, allow retailers to update pricing across stores quickly and consistently. When appropriate governance controls are in place, retailers can also implement rule-based pricing strategies that respond to changing business conditions.

For example, fresh food approaching its expiry date can be automatically marked down according to predefined pricing rules, helping reduce waste while protecting margins. Promotional pricing can also be activated across multiple stores simultaneously without requiring staff to replace thousands of paper shelf labels.

However, pricing is only one part of the equation. As products, assortments, and sales patterns change, the merchandising plan itself must also remain current. AI and automation help retailers keep shelf layouts aligned with evolving business conditions, reducing the need for repeated manual updates while ensuring stores continue to reflect the latest planning decisions.

Where DotActiv Fits

Within DotActiv’s AI platform, Luna automates the ongoing maintenance of planograms as products, sales data, and assortments change. Rather than requiring category managers to manually rebuild or refresh layouts, Luna updates planograms according to predefined schedules, highlighting additions, removals, and facing changes for review before implementation.

By automating routine maintenance, Luna helps retailers keep merchandising plans current across their store network, allowing category teams to spend less time on repetitive updates and more time making strategic merchandising decisions.

4. Security, Privacy, and Measuring Return on Investment

As retailers introduce more connected technologies into their stores, they must balance innovation with responsible governance.

Artificial intelligence creates opportunities to improve operational performance, but it also introduces new responsibilities around data protection, system security, and investment accountability.

Successful AI programmes are built not only on technical capability, but also on trust.

Measuring the Return on AI Investments

One of the most common questions retailers ask is whether AI delivers measurable commercial value. The answer depends less on the technology itself and more on how success is defined before implementation. Rather than evaluating AI as a standalone initiative, retailers should measure its contribution to operational outcomes.

Common performance indicators include:

  • On-shelf availability
  • Planogram compliance
  • Stockout rates
  • Sales per square metre
  • Labour productivity
  • Inventory accuracy
  • Shrink reduction
  • Customer satisfaction
  • Queue times
  • Promotion execution
  • Category growth

Different AI initiatives will influence different metrics.

For example:

  • Planogram automation may reduce planning time while improving merchandising consistency.
  • Image recognition may improve execution visibility across store networks.
  • Better forecasting may reduce stockouts and excess inventory.
  • Dynamic pricing may improve markdown performance within selected categories.

The most successful retailers establish baseline measurements before implementation and monitor improvements over time rather than expecting immediate transformational results.

AI Is an Operational Investment, Not a Technology Project

Many AI initiatives fail because organisations focus on the technology rather than the operational problem they are trying to solve.

Successful retailers approach AI differently.

They begin by asking questions such as:

  • Which operational processes consume the most manual effort?
  • Where do we lose sales today?
  • Which decisions could be improved with better data?
  • Which activities create the greatest value if automated?

Technology then becomes the enabler, not the objective.

When AI is introduced with clearly defined business outcomes, appropriate governance, and realistic expectations, it becomes a practical tool for improving retail performance rather than another disconnected technology investment.

For retailers building the smart stores of tomorrow, that operational mindset will be far more valuable than adopting every new AI capability that reaches the market.

5. From Strategy to Execution: Turning AI into Measurable Retail Performance

Many AI initiatives begin with enthusiasm but struggle to deliver lasting value, because AI is introduced without a clear operational strategy.

Retailers often pilot multiple AI solutions independently, creating disconnected systems that generate more dashboards than decisions. Over time, teams become overwhelmed by data, while the operational processes needed to act on those insights never evolve.

Successful retailers take a different approach by viewing AI as an operational capability rather than a technology project.

Instead of asking, “Which AI tools should we buy?”, they ask:

  • Which business problems are we trying to solve?
  • Which decisions would improve if we had better information?
  • Which manual processes consume the most time?
  • How will we measure success?

The answers to these questions form the foundation of a sustainable AI strategy.

Step 1: Define Clear Business Outcomes

Every AI initiative should begin with measurable operational objectives. Rather than introducing technology because it is available, retailers should identify where AI can create the greatest business impact.

Common objectives include:

  • Improving on-shelf availability
  • Increasing planogram compliance
  • Reducing manual planning time
  • Optimising assortments
  • Improving forecasting accuracy
  • Reducing stockouts
  • Increasing labour productivity
  • Improving customer satisfaction

Starting with clearly defined outcomes ensures that technology investments remain aligned with commercial priorities.

Step 2: Build a Reliable Data Foundation

Artificial intelligence is only as effective as the data supporting it.

Retailers should establish reliable, governed data across core operational systems before expecting AI to deliver meaningful recommendations.

This includes ensuring data quality across:

  • Product information
  • Sales transactions
  • Inventory
  • Store hierarchies
  • Floor plans
  • Planograms
  • Pricing
  • Promotional calendars

Strong governance also improves consistency across departments, making AI recommendations more trustworthy and easier to operationalise.

Step 3: Connect Planning with Execution

Planning alone does not improve retail performance.

Execution alone does not improve planning.

The greatest value is created when the two continuously inform one another.

A modern retail workflow should create a feedback loop where:

  1. Category strategies define assortments and merchandising principles.
  2. AI accelerates planogram development and analysis.
  3. Stores execute approved merchandising plans.
  4. Shelf conditions are monitored and verified.
  5. Insights are fed back into future planning decisions.

Rather than treating category management as a once-off project, retailers establish an ongoing cycle of continuous improvement.

Where DotActiv Fits

DotActiv’s platform supports this connected approach through complementary capabilities that span the category management workflow.

  • Nova is an intelligent planogram automation engine that transforms raw data and complex, retailer-defined merchandising rules into optimised, store-ready shelf layouts in minutes. 
  • Lola helps users access reports, category information, and operational insights more efficiently.
  • TrueView provides visibility into shelf execution by verifying in-store planogram compliance from shelf images.

Together, these capabilities help retailers connect planning, analysis, and execution while maintaining human oversight throughout the decision-making process.

Step 4: Scale Responsibly

Many retailers begin their AI journey with a single pilot store or category.

This allows teams to validate operational improvements, refine workflows, and establish governance before expanding across larger store networks.

As programmes mature, retailers should continually evaluate:

  • Model performance
  • Data quality
  • Operational adoption
  • User feedback
  • Business outcomes

Artificial intelligence should evolve alongside the business rather than becoming a static implementation. Continuous refinement is often what separates successful long-term programmes from short-lived technology initiatives.

The future of physical retail will not be defined by technology alone. It will be defined by retailers that use technology to make better decisions, improve operational consistency, and create better shopping experiences.

Artificial intelligence is already transforming many aspects of retail—from forecasting and merchandising to planogram automation, execution monitoring, and customer engagement. However, the greatest value does not come from adopting every new innovation. It comes from applying AI where it solves genuine operational challenges.

The most successful retailers share several characteristics:

  • They start with clearly defined business objectives.
  • They invest in reliable, high-quality data.
  • They strengthen operational processes before introducing automation.
  • They maintain human oversight over AI-driven decisions.
  • They measure success through tangible business outcomes rather than technology adoption.

For category managers, retailers, and suppliers, AI represents an opportunity to spend less time on repetitive manual tasks and more time making strategic decisions that improve retail performance.

Building Smarter Stores Starts with Better Category Management

While smart stores incorporate a wide range of technologies, effective retail execution still depends on strong merchandising fundamentals and remains the foundation of successful physical retail, and AI is making it faster, more scalable, and increasingly data-driven.

Retailers that combine strong operational processes with practical AI capabilities will be best positioned to respond to changing customer expectations, improve profitability, and build more resilient store networks.

If your organisation is exploring how AI can improve category management, planogram automation, retail analytics, or in-store execution, the right starting point isn’t another dashboard; it’s a clear understanding of the operational outcomes you want to achieve. The technology should support the strategy, not define it.

Learn more about DotActiv’s AI Tools here.

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  • DotActiv Team
  • 5 min read

How to Choose Visual Merchandising Software: 10 Questions Every Retailer Should Ask

How to Choose Visual Merchandising Software

Choosing visual merchandising software isn’t just about comparing feature lists.

The right platform should help your retail team make better merchandising decisions, improve planning efficiency, and create a more consistent shopping experience across every store.

The challenge is that there are dozens of software providers, each promising to optimise store layouts, automate planning, or improve execution. Looking beyond the marketing claims is essential.

Before investing in a solution, retailers should understand what problems they’re trying to solve and which capabilities will deliver the greatest value.

Here are ten questions worth asking before making a decision.

1. Does the Software Support Your Entire Merchandising Process?

The best visual merchandising platforms connect both.

A complete merchandising workflow typically includes:

  • Data
  • Floor planning
  • Planogram creation
  • Store-specific layouts
  • Space allocation
  • Retail analytics
  • Implementation support

When these activities are managed within a single platform, planning becomes more consistent and easier to scale.

Learn how DotActiv brings these capabilities together with our Visual Merchandising Software.

2. Can It Create Data-Driven Planograms?

Planograms sit at the centre of visual merchandising.

Without reliable planogram software, category managers spend unnecessary time manually creating layouts.

Instead of asking whether software can create planograms, ask how those planograms are created.

Does the software consider:

  • Sales performance?
  • Product dimensions?
  • Category strategies?
  • Shopper behaviour?
  • Store formats?

The more retail data available during planning, the more informed your merchandising decisions become.

Discover how Planogram Software helps retailers create consistent, data-driven shelf layouts.

3. Does It Include Floor Planning?

Visual merchandising starts long before products reach the shelf.

Floor planning determines where categories should be positioned throughout the store, while planograms determine how products should be presented within those categories.

These planning activities work together.

If your software only supports shelf planning, you’re missing part of the merchandising picture.

Learn more about Floor Planning Software and how it works alongside planograms.

4. Can It Support Different Store Formats?

Very few retailers operate identical stores.

You may have:

  • Large supermarkets
  • Neighbourhood stores
  • Convenience formats
  • Regional variations

Your software should make it easy to adapt merchandising plans without having to rebuild everything from the beginning.

Look for platforms that support clustering, localisation, and multiple store formats.

5. Will It Save Your Team Time?

One of the biggest reasons retailers invest in visual merchandising software is efficiency.

Ask how much manual work the software removes.

Can it:

  • Generate planograms faster?
  • Reuse existing layouts?
  • Automate repetitive planning tasks?
  • Reduce manual data preparation?

The goal isn’t simply faster planning.

It’s allowing category managers to spend more time improving category performance.

6. Does It Use AI in Practical Ways?

Artificial intelligence has become a common feature across retail software.

The important question isn’t whether AI exists.

It’s how useful it actually is.

Good AI should support planners by:

  • Generating planograms
  • Identifying optimisation opportunities
  • Automating repetitive work
  • Providing planning assistance

It should enhance the planning process without removing the expertise of category managers.

See how DotActiv uses AI for Category Management to support retail planning. Including Nova, Lola, and TrueView.

7. Can It Grow with Your Business?

Software should continue supporting your business as your retail network grows.

Whether you’re adding stores, expanding product ranges, or introducing new formats, your merchandising platform should scale without becoming more difficult to manage.

Consider:

  • Number of stores supported
  • Performance with large product databases
  • Enterprise capabilities
  • User management
  • Collaboration tools

8. Does It Integrate with Your Existing Retail Data?

Merchandising decisions are only as good as the information behind them.

Your visual merchandising software should work with the data your business already uses.

This might include:

  • Sales data
  • Product master data
  • Product dimensions
  • Inventory information
  • Category hierarchies

The easier data moves into your planning platform, the more reliable your merchandising decisions become.

Learn how Retail Analytics Software supports better merchandising decisions.

9. What Support Does the Vendor Provide?

Software is only one part of a successful implementation.

Retailers should also evaluate the expertise behind the platform.

Ask questions such as:

  • Is onboarding included?
  • Is training available?
  • Is there ongoing customer support?
  • Does the vendor understand category management?
  • Can they help improve planning processes?

Choosing a partner with retail experience often delivers more long-term value than selecting software alone.

10. Does It Align with Your Category Management Strategy?

Visual merchandising doesn’t exist in isolation.

It supports broader category management objectives.

The software you choose should complement how your business approaches:

  • Category planning
  • Assortment planning
  • Space optimisation
  • Shopper behaviour
  • Store execution

When merchandising software aligns with category management, planning decisions become more connected across the business.

Why Retailers Choose DotActiv

DotActiv combines visual merchandising, planogram software, and floor planning into a connected category management platform.

Retailers use DotActiv to:

  • Create data-driven planograms
  • Optimise store layouts
  • Improve merchandising consistency
  • Support multiple store formats
  • Reduce planning time with AI-powered tools
  • Maximise return on shelf space

Instead of managing disconnected planning activities, retailers can build a structured merchandising process supported by data and category management best practices.

Ready to Improve Your Visual Merchandising?

See how DotActiv helps retailers create data-driven planograms, optimise store layouts, and improve category performance across every store.

Book a personalised demo today.

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  • DotActiv Team
  • 6 min read

Best AI-Powered Category Management Software in 2026 | DotActiv AI

The Best AI-Powered Category Management Software

Why Retailers and Suppliers Are Choosing DotActiv

When retail executives and consumer packaged goods (CPG) brands search for the best AI-powered category management software, the search results are frequently dominated by legacy enterprise platforms. While massive supply chain suites serve their purpose for global conglomerates, modern retail teams require agile, intent-driven automation that doesn’t take months to deploy.

If your team is still manually building planograms or wrestling with static dashboards, there is a faster, smarter path forward. 

TheDotActiv Category Management Suite brings specialised AI Tools directly into the merchandising workflow that are purpose-built for planning, analysis, and in-store execution.

Proven Impact: Real-World Performance Metrics

AI is only valuable when it helps retailers make better decisions and execute them consistently. Across retailers and suppliers, DotActiv’s combination of clustering, assortment planning, planogram automation, floor planning, retail analytics, and AI-powered workflows has delivered measurable commercial results.

  • InMotion Luggage Category Optimisation: Using localised clustering, space planning, and data-driven planogram optimisation, InMotion achieved a 43% increase in average sales and a 37% increase in unit movement.
  • Link Pharmacy Group Expansion: By combining automated merchandising workflows, assortment optimisation, and standardised planograms, Link Pharmacy grew revenue by 35% while increasing unit volume by 21% across its store network.
  • Independent Pharmacy Self-Medication Audit: Through category analytics, range optimisation, and data-backed merchandising recommendations, an independent pharmacy chain increased average category sales by 23% and unit movement by 12%.
  • Bath Care Category Restructuring: Applying category clustering, assortment optimisation, and store-specific planograms, a leading pharmacy chain achieved 31% regional sales growth alongside a 20% increase in unit turnover.

These results weren’t driven by a single feature. They came from combining the right planning tools with structured category management processes, allowing retailers to make better decisions before products ever reach the shelf. That’s where the strongest returns are typically realised.

How DotActiv Outperforms Traditional Competitors

Many legacy platforms rely on outdated mathematical models and complex, manual script configurations. DotActiv modernises this process by leveraging specialised AI engines designed for distinct roles within the category management lifecycle.

1. Nova AI: AI-Assisted Planogram Generation

Traditional planogram software often requires planners to spend hours manually adjusting product placement or configuring complex merchandising rules before a shelf layout can be generated.

  • The AI Advantage: DotActiv Nova helps planners build planograms faster by combining AI with proven merchandising principles. Users can select preferred product flows, merchandising rules, and planning objectives, while Nova generates an optimised shelf layout based on the retailer’s data, business rules, and fixture constraints.   
  • Built for Retail Reality: Every recommendation is grounded in real retail data. Nova validates product dimensions, fixture specifications, capacity constraints, and merchandising requirements before generating the planogram, ensuring layouts are practical, executable, and ready for implementation. 

Rather than replacing the planner, Nova reduces repetitive planning work so category teams can spend more time reviewing, refining, and improving merchandising decisions that drive better retail performance.

2. On-Demand Strategy: Lola, Your Intelligent Retail Analyst

Many platforms provide dense data dashboards that require separate database queries to decipher.

  • The AI Advantage: DotActiv Lola is an LLM-powered category management expert built directly into the software workspace.
  • Contextual Data Interaction: Category planners can interact with Lola using natural language to query internal sales performance, analyse local store trends, or generate product movement metrics automatically. Lola also functions as an on-demand industry reference to clarify merchandising terms and retail psychology on the spot.

3. Continuous Updates: Background Automation with Luna

Manually maintaining store layouts during product deletions or line extensions can consume hundreds of hours.

  • The Automation Advantage: DotActiv Luna functions as an automated server-side application that updates and schedules planograms continuously in the background. When new sales figures are uploaded or product lines change, Luna refreshes the active store network layouts automatically. This automation keeps micro-space layouts aligned with real-time demand without requiring manual user intervention.

4. Verified Execution: TrueView Image Recognition

An AI-generated planogram provides little value if the layout isn’t executed correctly on the physical store front.

  • The AI Advantage: DotActiv TrueView combines computer vision, image recognition, and continuously trained AI models to verify that store execution matches the approved planogram. As more shelf images are analysed, the AI models continue to improve their ability to identify products, detect merchandising issues, and validate compliance across different store environments.
  • Closing the Loop: Store teams simply capture a shelf image using the TrueView mobile app. The platform automatically compares the image against the approved digital planogram, identifying missing products, incorrect facings, misplaced items, pricing or display inconsistencies, and other execution issues.
  • Compliance results are reported back to head office in near real time, giving category and operations teams visibility across every store without relying on manual field audits. This creates a continuous feedback loop between planning, execution, and improvement, helping retailers resolve issues faster and train future AI models using real store data.

Side-by-Side Comparison: DotActiv vs. Legacy Enterprise Software

Feature Category

Legacy Enterprise Platforms

The DotActiv AI Suite

Planogram Creation

Manual drag-and-drop or strict script rules

Flexible Consumer Decision Tree Configuration (Nova AI)

Data Interpretation

Static reporting grids and complex SQL queries

Natural Language Data Inquiries (Lola AI)

Layout Maintenance

Manual batch updates by planners

Live Background Refreshes (Luna AI)

In-Store Audit Process

Manual drag-and-drop or strict script rules

Automated Image Recognition (TrueView AI)

Proven Value Generation

Highly opaque, multi-tier hidden fees

Documented revenue bumps up to 43%

Deployment Time

3 to 12 months with heavy IT infrastructure changes

Structured onboarding with significantly reduced IT dependency and no Hidden fees.

Feature Category

Planogram Creation

Data Interpretation

Layout Maintenance

In-Store Audit Process

Proven Value Generation

Deployment Time

Legacy Enterprise Platforms

Manual drag-and-drop or strict script rules

Static reporting grids and complex SQL queries

Manual batch updates by planners

Manual field scans or spot checks

Highly opaque, multi-tier hidden fees

3 to 12 months with heavy IT infrastructure changes

The DotActiv AI Suite

Flexible Consumer Decision Tree Configuration (Nova AI)

Natural Language Data Inquiries (Lola AI)

Choosing the Best Alternative for Modern Retail Teams

When selecting the best AI category management software, look beyond high enterprise licensing fees. DotActiv delivers an agile, unified platform that connects AI-driven planning with automated background maintenance and verified store compliance.

Whether you manage a regional pharmacy network or a national grocery estate, DotActiv scales from a single category to a full store portfolio — without the implementation overhead of legacy enterprise systems. See how DotActiv’s AI suite can improve your shelf performance by booking a demo.

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  • DotActiv Team
  • 1 min read

7 Features to Look for in Modern Category Management Software

7 Features to Look for in Modern Category Management Software

Retail category management has evolved significantly.

Modern platforms should do far more than draw shelves.

Here are seven capabilities retailers should prioritise.

1. End-to-End Category Management

Look for software supporting:

  • Assortment
  • Clustering
  • Floor planning
  • Planograms
  • Analytics
  • Compliance

2. AI-Powered Automation

Modern solutions increasingly use AI for:

  • Planogram generation
  • Recommendation engines
  • Analytics
  • Decision support

3. Flexible Integrations

Your software should connect seamlessly with:

  • ERP systems
  • POS systems
  • Inventory platforms
  • Business intelligence tools

4. Collaboration Features

Retailers and suppliers need shared visibility into category decisions.

Collaboration tools accelerate sign-off and improve alignment.

5. Retail Analytics

Analytics should answer:

  • Which products drive profit?
  • Which stores require different assortments?
  • Which shelves underperform?

6. Store Execution And Compliance

Planning only creates value when stores execute correctly.

Look for:

  • Mobile compliance
  • Photo verification
  • Store communication

7. Fast Time-To-Value

The best software delivers measurable improvements quickly without lengthy implementations.

Retailers should carefully evaluate onboarding complexity and required IT resources.

The most successful category management software combines usability, analytics, automation, and execution into a single workflow.

Before investing, compare platforms carefully and evaluate how each solution fits your operational reality.

Download our Category Management Software Evaluation Checklist.

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  • DotActiv Team
  • 1 min read

Planogram Software vs Category Management Software Explained

Planogram Software vs Category Management Software: What's the Difference?

Many retailers use the terms interchangeably.

However, planogram software and category management software are not the same thing.

Understanding the difference can prevent costly technology decisions.

What Is Planogram Software?

Planogram software focuses primarily on shelf layout creation.

Typical capabilities include:

  • Shelf drawing
  • Product placement
  • Visual merchandising
  • Space allocation

For some retailers, this functionality is sufficient.

What Is CatMan Software?

Category management software extends beyond shelf design.

It helps retailers:

  • Analyse performance
  • Optimise assortment
  • Cluster stores
  • Create floor plans
  • Automate planning
  • Measure compliance
  • Improve profitability

Comparison Table

Capability

Shelf drawing

Floor planning

Analytics

Clustering

Assortment optimisation

AI automation

Compliance

Collaboration

Planogram Software

✓

Limited

Limited

✕

✕

Sometimes

Limited

Limited

Category Management Software

✓

✓

✓

✓

✓

✓

✓

✓

Why Retailers Outgrow Basic Planogram Software

As retail complexity increases, teams often struggle with:

  • Spreadsheet-based assortment decisions
  • Inconsistent execution
  • Lack of store segmentation
  • Limited analytical insight

This drives many retailers toward broader category management platforms.

Which One Is Right For You?

Choose planogram software if:

  • You only need shelf visualisation.
  • Your categories are relatively simple.

Choose category management software if:

  • You manage multiple stores.
  • You need assortment optimisation.
  • You require analytics and automation.
  • You want end-to-end category processes.
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  • DotActiv Team
  • 2 min read

How to Choose Category Management Software: 10 Questions to Ask

How to Choose CatMan Software: 10 Questions Every Retailer Should Ask

Buying category management software is a significant investment. Yet many retailers focus primarily on feature lists rather than long-term business outcomes.

Before evaluating vendors, ask these ten questions.

1. Does The Software Cover The Entire Category Management Process?

Look beyond planograms.

Can the platform also support:

  • Assortment optimisation?
  • Floor planning?
  • Clustering?
  • Analytics?
  • Store execution?

2. How Long Does Implementation Take?

Some enterprise solutions require months or even years to fully deploy.

Ask:

  • What resources are required?
  • How soon will we see ROI?
  • How much change management is involved?

3. What Will The Total Cost Of Ownership Be?

Software costs extend beyond licences.

Consider:

  • Implementation fees
  • Consulting
  • Training
  • Ongoing support
  • Integrations

4. Does It Integrate With Existing Systems?

The platform should work with existing ERP, POS, inventory, and BI systems.

5. Is The AI Transparent?

AI should support category managers rather than replace them.

Ask:

  • Can users understand recommendations?
  • Can users adjust outputs?
  • Are decisions explainable?

6. Can Suppliers Collaborate?

Modern category management increasingly requires collaboration between retailers and suppliers.

7. How Easy Is The Software To Use?

Adoption matters.

Complicated software often results in spreadsheets becoming the hidden competitor.

8. How Much IT Support Is Needed?

Mid-market retailers should carefully evaluate ongoing IT requirements.

9. Does The Vendor Understand Retail Operations?

Evaluate whether the vendor understands day-to-day retail operations.

10. How Future-Proof Is The Platform?

Look for:

  • AI innovation
  • Automation
  • Cloud capabilities
  • Continuous development

Download our Category Management Software Evaluation Checklist.

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  • DotActiv Team
  • 9 min read

Revolutionising Retail: AI in Retail Planning

Top AI Use Cases in Retail: Modernising Physical Store Environments

Walk into any modern store, and you will see the visible parts of retail operations: endcaps, shelf labels, and staff on the floor. What you do not see is the decision layer that determines whether items are in stock, where they sit on the shelf, whether the price is competitive, and whether execution matches the plan.

That decision layer is increasingly powered by AI.

According to McKinsey, retailers using AI-driven capabilities have seen inventory reductions of up to 20% while improving forecast accuracy and product availability. The opportunity is no longer theoretical. Retailers are already using AI to improve everyday store performance.

As shopper behaviour fragments across channels and local demand becomes harder to predict, retailers are moving from intuition-driven planning to AI in retail planning, complemented by retail BI analytics and AI retail business intelligence. Instead of running periodic reports and reacting after the fact, teams can use machine learning, computer vision, and natural-language analysis to improve day-to-day store performance.

But successful AI initiatives in retail rarely begin with technology. They begin with a repeatable operating model.

The Modern Retail AI Loop

Most successful AI initiatives in retail follow the same operating cycle:

  1. Predict & Optimise: Anticipate demand and fine-tune inventory so the right stock is always on hand.
  2. Localise Assortments: Tailor product mixes and shelf plans to store-specific shopper behaviour.
  3. Verify Execution: Monitor shelf conditions to ensure real-world execution perfectly matches the plan. 
  4. Analyse Outcomes: Swiftly evaluate performance to drive continuous, data-driven improvement. 

Retailers that connect these four stages create a feedback loop where planning improves execution, and execution data improves planning.

The use cases below explore how AI supports each stage of this cycle.

Use Case 1: Clean Product Data As The Foundation For AI

Before retailers can use AI effectively, they need something far less exciting: clean, structured product data. 

Many retailers struggle because critical product information is incomplete, inconsistent, or poorly classified. AI models can only produce useful recommendations when the underlying data is accurate and standardised.

For AI to support category management effectively, retailers need complete product information, including:

  • Product status
  • Product ID
  • Barcode
  • Product description
  • Brand
  • Merchandise group
  • Department
  • Category
  • Sub-category
  • Segment
  • Sub-segment

In other words, the entire product classification hierarchy needs to be clearly defined.

Once this data foundation exists, AI can help automate classification tasks. For example, an AI model can analyse a product description and recommend where that item belongs within the hierarchy, automatically mapping products to the correct category, segment, or sub-segment.

Clean data does not guarantee good decisions. But poor data almost always guarantees bad ones. In retail AI, data quality remains the starting point.

How DotActiv Does It: Screening & Classifications

DotActiv’s Screening & Classification tool leverages supervised machine learning to take the manual grunt work out of database management. By analysing your already classified product groups, the software essentially learns the “rules” and patterns of your data structure. It then uses this predictive intelligence to automatically categorise any “blank” entries across the database. Instead of forcing your team to spend hours manually sorting through thousands of products, the tool handles the heavy lifting, ensuring your retail database remains clean, structured, and ready for advanced analytics.

Use Case 2: Demand Forecasting That Accounts For Local Reality

Classic forecasting looks mostly at last year’s sales. AI forecasting combines historical patterns with external signals and store context, helping teams anticipate what each location is likely to sell before stockouts happen.

In practice, retail BI analytics systems can incorporate:

  • Historical sales and seasonality by store, category, and promotion.
  • Weather and local events that change traffic and basket composition.
  • Promotional uplift and price elasticity to estimate true demand.
  • Emerging trends, including regional social signals, that reshape short-term velocity.

Accurate forecasting isn’t just a planning victory; it’s an operational powerhouse. It directly translates to fewer empty shelves, fresher perishables, and stable labour schedules driven by predictable replenishment.

Use Case 3: Inventory Optimisation To Reduce Overstock And Stockouts

Physical stores constantly manage the “Goldilocks” problem: too much inventory ties up cash and creates waste; too little results in lost sales and customer trust.

Machine learning helps optimise safety stock, reorder points, and replenishment frequency at store and SKU level. When integrated into store workflows, prescriptive recommendations can say what to do next, not just what happened.

Recommendations may include:

  • Ordering adjustments based on lead times.
  • Replenishment changes based on vendor reliability.
  • Inventory corrections following execution issues.
  • Suggested transfers between stores to prevent stockouts.

This is one of the most direct ways AI retail business intelligence turns data into shelf availability, a core driver of shopper satisfaction.

Use Case 4: AI Planogram Generation To Translate Strategy Into Shelf Layouts

Even the best assortment and pricing plan fails if it cannot be executed in the aisle.

Planograms are the blueprint for shelf presentation, but building and maintaining them across categories and store formats is resource-intensive. AI can generate layouts faster, evaluate more options, and align shelves to how shoppers actually shop through decision-tree logic and adjacency behaviours.

The result is faster planning cycles, more store-specific layouts, and greater consistency across large store networks.

How DotActiv Does It: Nova AI Planogram Automation

DotActiv supports this part of category management with AI-powered tools for planogram generation, data analysis, and in-store execution, helping retailers and suppliers plan faster, make better decisions, and ensure compliance at shelf level.

Nova generates data-driven planograms using AI based on retailer rules, store formats, and shopper behaviour. Teams can:

  • Create automated shelf layouts based on Consumer decision trees.
  • Apply merchandising rules and constraints consistently.
  • Generate multiple store formats in a single run.
  • Standardise planning outputs across evolving AI models.

Rather than replacing planners, Nova enables planners to evaluate more scenarios and deploy planograms faster.

Use Case 5: Store-Level Planogram Compliance Using Computer Vision

Retailers can invest heavily in category strategy, only to see value lost because execution varies store to store.

Computer vision modernises compliance by turning shelf imagery into measurable, repeatable checks. Instead of relying on periodic manual audits, store teams can detect issues sooner and fix them while the sales impact is still preventable.

How DotActiv Does It: TrueView Image Recognition

TrueView uses image recognition to verify planogram compliance at store level.

Teams can:

  • Upload shelf images to automatically check planogram accuracy.
  • View execution performance by region, store, or category.
  • Track implementation status across the business.
  • Report operational issues directly to head office.
  • Monitor execution metrics such as drop counts and deadlines.

This creates an audit trail between the plan in the system and the reality in the aisle.

Use Case 6: Faster Answers And Analysis For Category Teams (Without Waiting On Reports)

Retail planning and store operations generate a mix of structured data, including sales, inventory, and compliance metrics, alongside unstructured content such as documents, planograms, and training material.

AI assistants reduce time-to-insight by enabling teams to ask questions in natural language, summarise documents, and surface relevant context while working.

Instead of waiting for reports, category teams can access answers immediately and make decisions while work is still in progress.

How DotActiv Does It: Lola AI LLM Assistant

Lola helps teams analyse data, answer questions, and access insights without running reports.

Users can:

  • Ask questions in natural language instead of SQL.
  • Analyse planograms and documents for insights.
  • Access training content instantly through PowerBase.
  • Surface category and industry insights in context.
  • Navigate the platform faster through a simplified interface.

This matters during fast resets, range reviews, and execution triage, where decision speed directly impacts performance.

Use Case 7: Dynamic Pricing And Smarter Markdowns In The Aisle

In physical retail, pricing is a constant balancing act between competitiveness, sell-through, and margin.

AI-driven pricing uses real-time and near-real-time signals to recommend price moves and markdown timing, especially for categories with perishability, short seasons, or rapid trend turnover.

Inputs often include:

  • Competitor pricing.
  • On-hand inventory.
  • Store traffic patterns.
  • Historical promotional response.

For store teams, the modernisation benefit is operational: fewer blunt, late markdown waves and more targeted adjustments that protect margin while clearing inventory before it becomes waste.

Use Case 8: Shelf Monitoring And On-Shelf Availability Alerts

Even when backroom inventory exists, items can still be missing from the shelf because of delayed replenishment, incorrect placement, or execution gaps.

AI-enabled shelf monitoring uses computer vision and workflow integration to detect empty facings, misplacements, and potential availability risks.

In DotActiv’s workflow, TrueView contributes to this modernisation through image-based compliance checks and execution metrics that highlight where the shelf diverges from the planogram.

When on-shelf issues are detected earlier, teams can prioritise the right tasks, and shoppers are less likely to encounter one of retail’s most frustrating experiences: the item is technically in stock but not available on the shelf.

Use Case 9: Smarter Replenishment And Distribution To Stores

Physical store modernisation is not only about what happens in the aisle. It is also about ensuring stores receive the right inventory at the right time.

AI improves distribution decisions by predicting store requirements, accounting for lead times, and recommending transfer or allocation actions across the network.

This is especially important in omni-channel retail environments, where store inventory may also support click-and-collect, delivery, and fulfilment operations.

Bringing AI Into Physical Retail

AI is not a standalone project. Its value comes from connecting planning, execution, and analysis within everyday retail workflows.

The retailers seeing the greatest returns are not necessarily those deploying the most AI. They are the retailers building strong data foundations, operationalising insights, and creating continuous feedback loops between strategy and execution.

For retailers and suppliers evaluating AI in retail planning, the question is not whether AI can generate insights. The question is whether those insights can be acted on, measured, and continuously improved at shelf, store, and regional level.

That is where modern retail performance is won.

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  • DotActiv Team
  • 14 min read

AI in Retail Planning: Smart Inventory & Analytics

The Future of Brick-and-Mortar: The Complete Guide to AI in Retail Operations

Have you ever walked into a store specifically for one item, only to find an empty shelf where your favourite bread should be? That brief annoyance isn’t just bad for your pantry. According to global industry data, out-of-stock items cost retailers a staggering $1.1 trillion every year. Managers have historically tried to prevent this by guessing how much inventory to order using gut feeling and messy spreadsheets.

Solving this massive financial headache requires a much smarter approach. Companies are finally utilising AI in retail planning to fix the persistent problem of empty shelves. Instead of picturing a sci-fi robot roaming the aisles, imagine this technology as a tireless, 24/7 assistant for store managers. It exists solely to help them order the perfect amount of stock at exactly the right time.

Every time you shop, you leave behind “digital footprints”-subtle clues about buying habits, weather reactions, and seasonal trends. Powered by retail analytics AI, the system acts as a massive digital brain connecting millions of these footprints at once. While humans might casually notice that umbrellas sell faster during storms, this digital brain calculates precisely how many umbrellas a specific neighbourhood actually needs during a Tuesday drizzle.

Better predictions ultimately lead to fresher grocery produce and fewer wasted trips to the mall. The true everyday value of AI in retail isn’t about replacing human workers. It is simply a super-powered tool designed to ensure your favourite products are always waiting for you.

Why 'Old School' Forecasting Fails: The Trillion-Dollar Guessing Game

Finding grocery store shelves full of milk that expires tomorrow is a common consumer frustration. While stores must order stock in advance, getting the exact quantity right historically relied on human guesswork. Managers stared at massive spreadsheets, trying to balance last week’s sales against incoming deliveries. This manual approach often caused dumpsters full of spoiled food.

Older computer programs, known as legacy systems, simply cannot handle the mountain of information created by modern shoppers. Human brains and outdated software hit a hard data processing limit when tracking thousands of unique items at once. Comparing legacy planning systems vs AI platforms reveals a glaring gap: old tools cannot easily factor in a sudden local heatwave, causing a run on bottled water.

Solving this guessing game requires moving from “gut feelings” to precise, data-driven decisions. Retailers now use retail analytics AI to instantly process these overwhelming digital footprints. By upgrading to smarter AI inventory management, stores finally have a mathematical tool capable of seeing the big picture and reliably predicting future purchases.

Meet the Pattern Finders: How AI Predicts What You Will Buy

When store managers use past sales to guess next week’s grocery orders, they are performing “demand forecasting,” which is simply predicting what people will buy. However, figuring out how to automate retail demand forecasting requires stepping away from basic math and adopting tools that can actually learn from their environment.

To understand this leap, consider the key differences in machine learning vs traditional retail forecasting:

  • Traditional Forecasting: Uses basic, linear math. It assumes that because a store sold fifty umbrellas last October, it should blindly order fifty this October.
  • AI Forecasting: Acts as a super-powered pattern finder. It identifies complex, hidden shopping behaviours across thousands of daily transactions that human eyes simply cannot spot.

Rather than following a stubborn rule, this technology relies on “machine learning,” which is basically trial and error at warp speed. If the computer guesses a store will sell one hundred boxes of cereal but only eighty leave the shelf, it adjusts its math for tomorrow, getting slightly smarter every single day. By applying predictive analytics for inventory management, the digital brain constantly corrects its own mistakes without human intervention. These daily adjustments rely on environmental variables extending far beyond a simple printed receipt.

From Rain Clouds to Viral Trends: The Secret Signals AI Watches

Grocery stores often seem to magically stock extra hot dog buns right before a sudden sunny weekend. This is the result of retail analytics AI gathering hidden signals. Instead of just reading old receipts, these systems look outside to predict your daily needs.

Translating everyday life into math happens constantly through hyper-local demand sensing technology. This tool tracks five non-obvious factors altering your shopping cart: sudden rainstorms, school schedules, local sports events, traffic jams, and fast-moving internet fads. The digital brain converts a temperature drop into a data point, telling warehouses precisely how many heavy coats to send to your specific zip code.

Spotting these invisible shifts is especially vital for unpredictable fashion crazes. Utilising generative AI for retail trend analysis, computers “read” millions of public internet posts to catch a viral denim style early. The system treats buzzing online conversations about a new sneaker colour as an early warning sign, turning social media hype into concrete inventory data.

Grasping exactly what drives a sudden rush on a specific product helps stores order exactly what you want, right when you want it. This awareness permanently changes how shops manage unsold goods, ultimately killing the “clearance rack” cycle and reducing overstock with smart planning.

The 'Clearance Rack' Cycle: Reducing Overstock with Smart Planning

Scoring a huge clearance discount feels like a victory, but those deep price cuts actually signal a massive planning failure. When stores guess wrong about trends, unsold goods pile up in backrooms. This excess inventory, known as overstock, ruins store profitability and creates devastating environmental waste when items are thrown away.

Instead of relying on human guesswork, modern shops are reducing overstock with artificial intelligence. These digital systems eliminate retail waste in three key ways:

  • Accuracy: Ordering exact product quantities rather than relying on rough seasonal estimates.
  • Timing: Delivering merchandise to the sales floor exactly when local demand peaks.
  • Localisation: Matching specific products to the unique neighbourhoods that want them using AI-driven assortment optimisation tools.

By predicting precisely what shoppers will buy, retailers keep unloved merchandise out of landfills. This careful AI inventory management means a healthier planet and a more profitable business. Mastering these big-picture trends naturally solves the daily struggle of keeping everyday essentials available through automated smart replenishment systems.

Neighbourhood-Specific Shopping: AI Category Clustering and Range Optimisation

Notice that your neighbourhood grocery store carries different snacks than the exact same chain twenty miles away? That variety is not an accident. Retailers are moving away from blanket, one-size-fits-all inventory models by leveraging AI category-based clustering and range optimisation.

Instead of treating every store the same, deep learning algorithms analyze millions of data points—including local demographics, purchase histories, and subtle environmental shifts—to group store locations into distinct, behaviour-based clusters.

Once these clusters are established, AI range optimisation software takes over to curate the ideal product mix for each specific store bucket. The system automatically calculates the most profitable and high-demand product assortment, learning that a college-town cluster requires single-serve microwaveable meals, while a suburban cluster requires bulk family sizes.

Matching shelf space to specific community clusters ensures that stores only ship what will actually sell. This hyper-targeted approach optimises retail supply chains, dramatically reducing distribution costs and carbon emissions by preventing trucks from hauling unwanted goods across the country.

The Store with Eyes: Why Catching Out-of-Stocks is Just a Patch Job

Shoppers frequently ask for a missing item only to hear, “Let me check the back.” A store’s inventory system might state your favourite cereal is in stock, but it hasn’t actually reached the aisle. Historically, stores have tried to fix this by installing ceiling cameras equipped with computer vision technology. Think of these real-time inventory visibility solutions as digital eyes designed to spot an empty shelf space and alert a worker to go fetch stock from the backroom.

While computer vision improves immediate shelf availability, relying on it to fix empty shelves is ultimately an old-school, reactive patch job. Spotting an out-of-stock item after it happens means the planning and execution pipeline has already failed. It treats the symptom rather than curing the disease.

To truly eliminate the empty shelf problem, retailers must shift from reactive scanning to predictive, end-to-end automated replenishment execution.

The Digital Grocery List: How Smart Replenishment Systems Automate Restocking

To move past reactive patch jobs, modern retail operations seamlessly tie forecasting directly to shelf execution through AI-powered planogram generation and smart replenishment systems. True operational efficiency requires a closed-loop system: the AI generates a data-driven planogram (the visual map of where products sit on a shelf) tailored to shopper behaviour, the replenishment engine automatically orders stock to fulfil that specific planogram layout, and AI image recognition continuously verifies that the physical shelves match the digital plan.

This synchronisation is critical. If a smart replenishment engine calculates and orders stock perfectly based on a planogram, but store employees fail to implement that planogram accurately on the floor, a massive stock ordering bottleneck occurs. 

Ultimately, this seamless coordination relies on AI inventory management to keep operations running perfectly without human guesswork.

Why Prices Change at Midnight: The Logic Behind AI Dynamic Pricing

Everyone understands that a winter coat costs less in March due to basic supply and demand. AI takes this further using dynamic pricing strategies for e-commerce and local storefronts, acting as an advanced analytical engine that adjusts costs instantly based on:

  • Competitor prices nearby
  • Current stock levels in the backroom
  • Approaching expiration dates for fresh food

Bargain hunters actually benefit directly from these digital brains. Stores rely on automated markdown optimisation algorithms to gradually lower the cost of slow-selling items, ensuring you discover great deals exactly when the retailer needs to clear physical shelf space.

When prices fluctuate, you might ask about the benefits of algorithmic merchandising versus unfair price gouging. Smart pricing simply balances demand fairly to prevent empty shelves without exploiting temporary consumer shortages. This continuous, localised balancing act naturally extends to how stores stock their physical aisles for specific communities.

DotActiv's AI Tools for Category Management: Nova, Trueview, and Lola

In practical category management workflows, the question is not whether AI exists, but whether it is operationally embedded across planning, analysis, and execution. DotActiv provides AI-powered tools for planogram generation, data analysis, and in-store execution, helping retailers and suppliers plan faster, make better decisions, and ensure compliance at shelf level.

TrueView: Image Recognition for Planogram Compliance

TrueView uses image recognition to verify planogram compliance at store level, converting shelf photos into structured, auditable execution signals.

  • Upload shelf images to check planogram accuracy automatically
  • View performance by region, store, or category
  • Track implementation status (implemented vs overdue)
  • Report operational issues directly to head office
  • Monitor execution metrics such as drop counts and deadlines

Nova: AI-generated Planograms Aligned to Rules, Formats, and Shopper Behaviour

Nova generates data-driven planograms using AI, based on your rules, store formats, and shopper behaviour, enabling scalable shelf planning without sacrificing governance and merchandising intent.

  • Automated shelf layouts based on shopper decision trees
  • Works across multiple AI models (model-agnostic)
  • Respects your merchandising rules and constraints
  • Generates multiple store formats in a single run

Lola: Self-Serve Analysis and Contextual Insights

Lola helps teams analyse data, answer questions, and access insights without running reports, lowering the friction between a business question and a defensible decision.

  • Ask questions in natural language instead of SQL
  • Analyse planograms and documents for insights
  • Access training content instantly from PowerBase
  • Get category and industry insights in context
  • Navigate the platform faster with a simplified interface

Workforce Planning And Task Execution: Using AI to Staff Smarter and Act Faster

Even when planning is perfect on paper, outcomes break down at the store level if the right people are not in the right place at the right time. AI-driven workforce management applies demand signals to labour planning, connecting predicted traffic, promotional events, and delivery schedules to store rosters and shift plans.

Operationally, AI is increasingly used to translate a long list of store tasks into an ordered queue that reflects business impact. Instead of treating every task as equal, systems can prioritize shelf replenishment, price changes, and compliance actions based on lost-sales risk, perishability, or promotional deadlines.

Shrink And Loss Prevention: Detecting Risk Before It Becomes A Write-Off

Retail operations are constrained not only by demand variability but also by shrink, scanning errors, and fraud. AI supports loss prevention by finding anomalies across transactions, returns, and inventory movements that are difficult to spot with manual audits alone.

In physical stores, computer vision can complement traditional loss-prevention methods by flagging suspicious patterns, high-risk zones, and recurring operational breakdowns that lead to avoidable losses. The goal is not surveillance for its own sake, but earlier detection and tighter process control.

Omnichannel Fulfillment: Making BOPIS And Ship-From-Store Operationally Reliable

As consumers blend digital and physical shopping, store operations increasingly include picking, packing, and staging orders. AI helps retailers decide when to fulfil from a warehouse versus a store, which store to select, and how to allocate inventory so an online promise does not create an empty shelf for walk-in customers.

At execution time, accuracy matters as much as speed. Inventory visibility, shelf-availability signals, and substitution rules can be coordinated so BOPIS, curbside, and ship-from-store workflows remain predictable during peak periods.

Data Foundations And Implementation: How To Make AI In Retail Operational, Not Theoretical

AI systems only perform as well as the data and processes around them. Successful deployments typically start with clear definitions of master data (products, stores, and planogram rules), then ensure that sales, inventory, and execution data are captured consistently enough to support learning and measurement.

From an operating model perspective, retailers often establish a practical “human-in-the-loop” structure: algorithms propose decisions, teams review exceptions, and feedback is captured to improve future recommendations. To keep initiatives grounded, many organisations track a small set of operational KPIs, such as on-shelf availability, forecast accuracy, waste reduction, fulfilment accuracy, and planogram compliance, then link those to ROI through fewer lost sales and lower operating costs.

Will Robots Replace Store Clerks? The Future of Human-AI Retail Teams

Walking into a store run entirely by robots sounds like a sci-fi movie, but the reality is deeply collaborative. Instead of replacing workers, forward-thinking stores use “augmented intelligence,” where technology acts as an administrative assistant. The computer handles the tedious, data-heavy computations, leaving clerks free to focus on human-centric customer service.

This structural shift becomes obvious when comparing machine learning vs traditional retail forecasting. Previously, managers spent hours in back offices guessing what would sell using basic spreadsheets. Today, retail analytics AI processes weather and sales data instantly. Academic research highlights that offloading these repetitive, analytical tasks to algorithms vastly improves employee job satisfaction and performance, as detailed in studies on human-AI collaboration from the Harvard Business Review.

Yet, even the smartest algorithms need a “human-in-the-loop” to navigate the complexities of the real world. While AI in retail planning predicts seasonal baseline demand with incredible precision, it cannot foresee an anomalous, hyper-local disruption—like a localised viral TikTok trend selling out a specific brand overnight. Store managers must step in to adjust the software’s math using vital local intuition.

When human empathy and contextual awareness pair with algorithmic processing speed, it creates a shopping experience that is both highly efficient and deeply personal. This hybrid workforce model ensures shelves stay full without turning your favourite shop into a soulless, automated warehouse.

Your 2030 Shopping Trip: How AI-Driven Planning Makes Retail Better for You

Empty shelves and spoiled produce used to feel like an inevitable part of the shopping experience. Now, you know those frustrations are actively being solved behind the scenes. The shift toward AI in retail planning means stores are finally trading guesswork for precision, transforming how your favourite products make it from the warehouse to your cart.

You don’t need to look at a computer screen to see this technology in action. You can actually spot the benefits of algorithmic merchandising on your very next grocery run. Look for these three signs your local store is already using AI inventory management:

  • Fewer “sold out” signs on your go-to pantry staples, even during busy holiday weeks.
  • Consistent product placement where items perfectly match local community tastes. 
  • Personalised deals in your app that magically align with what you normally buy.

This invisible technology is doing much more than just keeping the milk stocked. As these digital assistants get smarter, we are moving toward a retail future with near-zero food waste and perfectly timed inventory, which ultimately keeps prices lower for everyone.

Next time you walk down a perfectly stocked aisle, take a moment to look around. You now understand the complex data choreography making it happen. Start noticing which stores consistently have exactly what you need—you’ll quickly realise it isn’t just good luck, but a brilliant system working just for you.

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  • DotActiv Team
  • 16 min read

Understanding the Benefits of Planogram Tools

In the fast-paced world of retail, optimizing store layouts is crucial. Planogram tools are essential for achieving this goal. They help retailers strategically place products to boost sales and enhance customer experiences.

These tools offer a digital approach to visual merchandising. They allow retailers to design and test store layouts before implementation. This reduces errors and saves time.

Planogram software provides insights into customer behavior and sales trends. It helps retailers make informed decisions about product placement. This leads to increased visibility and higher conversion rates.

With planogram automation, updating store layouts becomes seamless. It ensures consistency across multiple locations, maintaining brand identity.

Explore the benefits of planogram tools and transform your retail strategy.

A digital representation of a retail store layout on a computer screen

What Is a Planogram Tool?

A planogram tool is a software solution designed for retail. It helps arrange products in a store for optimal results. By using digital representations, it streamlines the planning process.

These tools assist in visual merchandising through virtual layouts. Retailers can easily modify plans, saving significant time and effort. Planogram tools make it possible to see how different layouts look before making any physical changes.

Key functions include:

  • Visualizing space to improve product placement.
  • Simulating shopper flow to enhance navigation.
  • Analyzing sales data to refine strategies.

Planogram tools are vital for effective space management. They create a balance between aesthetics and functionality. With these tools, retailers can enhance the shopping experience and drive sales.

Moreover, they support compliance with retail standards. Such compliance ensures that stores consistently meet brand guidelines. Overall, planogram tools are an invaluable asset for modern retail environments.

A retail manager using planogram software to design a store layout

Key Features of Modern Planogram Software

Modern planogram software offers a myriad of features tailored to meet retail demands. These tools have evolved to incorporate user-friendly interfaces. Retailers can now design layouts without needing specialized skills.

One standout feature is real-time analytics, which provides data-driven insights. This enables retailers to adjust their strategies dynamically. By leveraging such analytics, retailers can boost sales and enhance customer experiences.

Another critical feature is integration capability with existing retail systems. This ensures seamless operations across platforms. Planogram software easily interacts with inventory and point-of-sale systems for cohesive data management.

Key features include:

  • 3D visualization for realistic store representations.
  • Inventory integration for accurate stock levels.
  • Customizable templates for different retail formats.

Additionally, remote accessibility through cloud-based solutions enhances teamwork. Retail teams can collaborate from anywhere, ensuring uniformity across locations. This flexibility is essential for modern retail environments striving to maintain consistency.

Moreover, planogram automation is a major highlight. It drastically reduces manual labor and errors in store planning. Thus, modern planogram software empowers retailers to effectively meet and adapt to evolving retail needs.

Digital representation of a retail store layout designed using planogram tools

Types of Planogram Tools: From Virtual to Automated Solutions

Planogram tools vary in types and functionalities to suit diverse retail needs. Virtual planograms are increasingly popular for their digital precision. They allow retailers to visualize store layouts before making physical changes.

Automated planogram solutions take layout management to the next level. These tools use artificial intelligence to recommend optimal product placements. Automation minimizes manual intervention, ensuring accuracy and efficiency.

Retailers also benefit from merchandising planogram software that focuses on space management. This software helps in organizing products effectively to maximize sales potential. Such tools streamline the merchandising process, making it more strategic.

Different types of planogram tools include:

  • Virtual planograms for digital visualization.
  • Automated software for real-time adjustments.
  • Customizable merchandising programs for unique layouts.

Cloud-based solutions provide another layer of flexibility and accessibility. Retailers can access their plans remotely, facilitating team collaboration. These varied planogram tools help retailers stay competitive by adapting to changing market demands and customer preferences.

Illustration of strategic product placement in a retail store using planogram tools

How Planogram Tools Enhance Merchandising Strategies

Planogram tools transform merchandising by enabling precise product placement. They provide insights into customer behavior, allowing for strategic decisions. This helps in arranging products to boost visibility and sales.

Merchandising planogram software aids in creating store layouts that maximize space utility. It ensures that high-demand products are placed in high-traffic areas. This strategic placement enhances the overall shopping experience.

Retailers can use planogram tools to test different merchandising techniques. By simulating customer flow, they predict how consumers interact with displays. This testing helps in refining strategies for better engagement.

Planogram tools offer several advantages:

  • Enhanced product visibility.
  • Strategic placement based on data.
  • Efficient use of store space.

These tools also assist in aligning product displays with marketing campaigns. Consistent brand storytelling across all platforms is crucial in modern retailing. Planogram software supports this by maintaining uniformity in store presentations.

In today’s competitive environment, employing these tools ensures that retailers meet consumer expectations. Planogram tools help create cohesive and engaging shopping experiences. This enhances customer satisfaction and drives repeat business.

Graph showing increased sales due to effective use of planogram tools

Benefits of Using Planogram Tools in Retail

Planogram tools offer numerous advantages for retailers. They optimize product placement, leading to increased sales and improved customer experience. By strategically arranging products, retailers can capture shopper attention more effectively.

These tools also foster brand consistency across multiple locations. Retailers can ensure that each store has a uniform layout. This consistency boosts brand identity and recognition among consumers.

Planogram programs are invaluable for analyzing sales data. They provide insights that help merchants make informed decisions. Understanding sales trends ensures more profitable merchandising strategies.

Key benefits of planogram tools include:

  • Increased product visibility.
  • Improved inventory management.
  • Enhanced data analysis capabilities.

Planogram automation simplifies the process of updating displays. It significantly reduces manual labor and errors. By cutting down on time and effort, staff can focus on other essential tasks.

Integration with inventory systems ensures stock levels are optimal. This reduces the chances of overstocking or running out of popular items. Planogram tools are vital for enhancing operational efficiency.

In addition, retailers benefit from rich visual presentations of stores. Visual layouts help in planning promotions and seasonal events. This leads to better customer engagement and satisfaction.

Retail employee using planogram automation software on a tablet

Planogram Automation: Saving Time and Reducing Errors

Planogram automation transforms how retailers manage store layouts. It eliminates the need for manual updates, reducing errors significantly. This streamlining of processes allows staff to focus on customer service.

Automation tools ensure rapid and precise updates. This keeps displays fresh and aligned with current trends. By minimizing manual labor, stores can operate more efficiently.

With automated systems, consistent brand presentation is easily achieved. This supports brand recognition and customer trust. Automated planograms also allow for quick adaptation to market changes.

Key advantages of planogram automation:

  • Faster updates and execution.
  • Reduced manual errors.
  • Enhanced brand consistency.

Through automation, retailers can react swiftly to inventory changes. This flexibility is critical in today’s dynamic retail environment. Planogram automation is not just a tool but a strategic asset for competitive advantage.

Graph showing integration of planogram software with retail systems

Integrating Planogram Software with Retail Systems

Integrating planogram software with existing retail systems boosts operational efficiency. It provides seamless access to crucial inventory data, enhancing merchandising strategies. This integration ensures information flows smoothly across platforms.

Planogram software often connects with point-of-sale (POS) systems. This link provides real-time insights into sales performance. By understanding what’s selling well, retailers can adjust product placements quickly.

Key benefits of integration include:

  • Real-time inventory updates.
  • Streamlined operations.
  • Enhanced data accuracy.

A well-integrated system supports better decision-making. It offers a comprehensive view of sales trends and stock levels. As a result, stores can maintain optimal product visibility and align with customer demands. Integration is the gateway to smarter, data-driven retail management.

Choosing the Best Planogram Software for Your Business

Selecting the right planogram software is crucial for retail success. Every business has unique needs and constraints. Therefore, it’s essential to evaluate software features that align with your specific goals.

Consider user-friendliness when choosing software. A complex interface can hinder productivity. Ensure the software is easy to navigate. It’s equally important to assess the level of customer support provided.

Key factors to consider include:

  • Ease of use
  • Integration capabilities
  • Customer support

Another critical aspect is scalability. Your choice should grow with your business. Choosing wisely ensures lasting benefits, supporting both current and future merchandising strategies. Aligning software capabilities with business needs will maximize operational efficiency and boost sales.

Successful retail store using planogram tools

Real-World Examples: Success Stories with Planogram Tools

In the retail sector, some businesses have significantly benefited from implementing planogram tools. Several companies have seen improvements in sales and store efficiency.

One grocery chain increased its annual sales by strategically placing high-demand products. A fashion retailer enhanced its layout using planogram software, improving customer flow. These examples highlight the potential impact of effective merchandising tools.

Success stories include:

  • Grocery chain optimizing product placement
  • Fashion retailer improving store layout
  • Electronics store enhancing customer experience

These real-world examples showcase the transformative power of planogram tools in retail environments.

Conclusion: The Future of Retail with Planogram Tools

The future of retail will undeniably be shaped by planogram tools. These tools enable retailers to strategize more effectively and enhance customer experience.

With continuous advancements, planogram software is becoming more intuitive and powerful. The integration with data analytics is setting new standards in store management.

Retailers who invest in these technologies are likely to see significant improvements. Increased efficiency, better product placement, and higher sales are some of the expected outcomes. Embracing planogram tools today sets the stage for future success in the retail industry. The evolution of these tools promises a more organized and profitable retail landscape.

Understanding the Benefits of Planogram Tools

In the fast-paced world of retail, optimizing store layouts is crucial. Planogram tools are essential for achieving this goal. They help retailers strategically place products to boost sales and enhance customer experiences.

These tools offer a digital approach to visual merchandising. They allow retailers to design and test store layouts before implementation. This reduces errors and saves time.

Planogram software provides insights into customer behavior and sales trends. It helps retailers make informed decisions about product placement. This leads to increased visibility and higher conversion rates.

With planogram automation, updating store layouts becomes seamless. It ensures consistency across multiple locations, maintaining brand identity.

Explore the benefits of planogram tools and transform your retail strategy.

What Is a Planogram Tool?

A planogram tool is a software solution designed for retail. It helps arrange products in a store for optimal results. By using digital representations, it streamlines the planning process.

These tools assist in visual merchandising through virtual layouts. Retailers can easily modify plans, saving significant time and effort. Planogram tools make it possible to see how different layouts look before making any physical changes.

Key functions include:

  • Visualizing space to improve product placement.
  • Simulating shopper flow to enhance navigation.
  • Analyzing sales data to refine strategies.

A digital representation of a retail store layout on a computer screen

Planogram tools are vital for effective space management. They create a balance between aesthetics and functionality. With these tools, retailers can enhance the shopping experience and drive sales.

Moreover, they support compliance with retail standards. Such compliance ensures that stores consistently meet brand guidelines. Overall, planogram tools are an invaluable asset for modern retail environments.

Key Features of Modern Planogram Software

Modern planogram software offers a myriad of features tailored to meet retail demands. These tools have evolved to incorporate user-friendly interfaces. Retailers can now design layouts without needing specialized skills.

One standout feature is real-time analytics, which provides data-driven insights. This enables retailers to adjust their strategies dynamically. By leveraging such analytics, retailers can boost sales and enhance customer experiences.

Another critical feature is integration capability with existing retail systems. This ensures seamless operations across platforms. Planogram software easily interacts with inventory and point-of-sale systems for cohesive data management.

Key features include:

  • 3D visualization for realistic store representations.
  • Inventory integration for accurate stock levels.
  • Customizable templates for different retail formats.

A retail manager using planogram software to design a store layout

Additionally, remote accessibility through cloud-based solutions enhances teamwork. Retail teams can collaborate from anywhere, ensuring uniformity across locations. This flexibility is essential for modern retail environments striving to maintain consistency.

Moreover, planogram automation is a major highlight. It drastically reduces manual labor and errors in store planning. Thus, modern planogram software empowers retailers to effectively meet and adapt to evolving retail needs.

Types of Planogram Tools: From Virtual to Automated Solutions

Planogram tools vary in types and functionalities to suit diverse retail needs. Virtual planograms are increasingly popular for their digital precision. They allow retailers to visualize store layouts before making physical changes.

Automated planogram solutions take layout management to the next level. These tools use artificial intelligence to recommend optimal product placements. Automation minimizes manual intervention, ensuring accuracy and efficiency.

Retailers also benefit from merchandising planogram software that focuses on space management. This software helps in organizing products effectively to maximize sales potential. Such tools streamline the merchandising process, making it more strategic.

Different types of planogram tools include:

  • Virtual planograms for digital visualization.
  • Automated software for real-time adjustments.
  • Customizable merchandising programs for unique layouts.

Digital representation of a retail store layout designed using planogram tools

Cloud-based solutions provide another layer of flexibility and accessibility. Retailers can access their plans remotely, facilitating team collaboration. These varied planogram tools help retailers stay competitive by adapting to changing market demands and customer preferences.

How Planogram Tools Enhance Merchandising Strategies

Planogram tools transform merchandising by enabling precise product placement. They provide insights into customer behavior, allowing for strategic decisions. This helps in arranging products to boost visibility and sales.

Merchandising planogram software aids in creating store layouts that maximize space utility. It ensures that high-demand products are placed in high-traffic areas. This strategic placement enhances the overall shopping experience.

Retailers can use planogram tools to test different merchandising techniques. By simulating customer flow, they predict how consumers interact with displays. This testing helps in refining strategies for better engagement.

Planogram tools offer several advantages:

  • Enhanced product visibility.
  • Strategic placement based on data.
  • Efficient use of store space.

Illustration of strategic product placement in a retail store using planogram tools

These tools also assist in aligning product displays with marketing campaigns. Consistent brand storytelling across all platforms is crucial in modern retailing. Planogram software supports this by maintaining uniformity in store presentations.

In today’s competitive environment, employing these tools ensures that retailers meet consumer expectations. Planogram tools help create cohesive and engaging shopping experiences. This enhances customer satisfaction and drives repeat business.

Benefits of Using Planogram Tools in Retail

Planogram tools offer numerous advantages for retailers. They optimize product placement, leading to increased sales and improved customer experience. By strategically arranging products, retailers can capture shopper attention more effectively.

These tools also foster brand consistency across multiple locations. Retailers can ensure that each store has a uniform layout. This consistency boosts brand identity and recognition among consumers.

Planogram programs are invaluable for analyzing sales data. They provide insights that help merchants make informed decisions. Understanding sales trends ensures more profitable merchandising strategies.

Key benefits of planogram tools include:

  • Increased product visibility.
  • Improved inventory management.
  • Enhanced data analysis capabilities.

Graph showing increased sales due to effective use of planogram tools

Planogram automation simplifies the process of updating displays. It significantly reduces manual labor and errors. By cutting down on time and effort, staff can focus on other essential tasks.

Integration with inventory systems ensures stock levels are optimal. This reduces the chances of overstocking or running out of popular items. Planogram tools are vital for enhancing operational efficiency.

In addition, retailers benefit from rich visual presentations of stores. Visual layouts help in planning promotions and seasonal events. This leads to better customer engagement and satisfaction.

Planogram Automation: Saving Time and Reducing Errors

Planogram automation transforms how retailers manage store layouts. It eliminates the need for manual updates, reducing errors significantly. This streamlining of processes allows staff to focus on customer service.

Automation tools ensure rapid and precise updates. This keeps displays fresh and aligned with current trends. By minimizing manual labor, stores can operate more efficiently.

Retail employee using planogram automation software on a tablet

With automated systems, consistent brand presentation is easily achieved. This supports brand recognition and customer trust. Automated planograms also allow for quick adaptation to market changes.

Key advantages of planogram automation:

  • Faster updates and execution.
  • Reduced manual errors.
  • Enhanced brand consistency.

Through automation, retailers can react swiftly to inventory changes. This flexibility is critical in today’s dynamic retail environment. Planogram automation is not just a tool but a strategic asset for competitive advantage.

Integrating Planogram Software with Retail Systems

Integrating planogram software with existing retail systems boosts operational efficiency. It provides seamless access to crucial inventory data, enhancing merchandising strategies. This integration ensures information flows smoothly across platforms.

Planogram software often connects with point-of-sale (POS) systems. This link provides real-time insights into sales performance. By understanding what’s selling well, retailers can adjust product placements quickly.

Graph showing integration of planogram software with retail systemsby Luke Chesser (https://unsplash.com/@lukechesser)

Key benefits of integration include:

  • Real-time inventory updates.
  • Streamlined operations.
  • Enhanced data accuracy.

A well-integrated system supports better decision-making. It offers a comprehensive view of sales trends and stock levels. As a result, stores can maintain optimal product visibility and align with customer demands. Integration is the gateway to smarter, data-driven retail management.

Choosing the Best Planogram Software for Your Business

Selecting the right planogram software is crucial for retail success. Every business has unique needs and constraints. Therefore, it’s essential to evaluate software features that align with your specific goals.

Consider user-friendliness when choosing software. A complex interface can hinder productivity. Ensure the software is easy to navigate. It’s equally important to assess the level of customer support provided.

Comparison chart of planogram software features

Key factors to consider include:

  • Ease of use
  • Integration capabilities
  • Customer support

Another critical aspect is scalability. Your choice should grow with your business. Choosing wisely ensures lasting benefits, supporting both current and future merchandising strategies. Aligning software capabilities with business needs will maximize operational efficiency and boost sales.

Real-World Examples: Success Stories with Planogram Tools

In the retail sector, some businesses have significantly benefited from implementing planogram tools. Several companies have seen improvements in sales and store efficiency.

One grocery chain increased its annual sales by strategically placing high-demand products. A fashion retailer enhanced its layout using planogram software, improving customer flow. These examples highlight the potential impact of effective merchandising tools.

Successful retail store using planogram tools

Success stories include:

  • Grocery chain optimizing product placement
  • Fashion retailer improving store layout
  • Electronics store enhancing customer experience

These real-world examples showcase the transformative power of planogram tools in retail environments.

Conclusion: The Future of Retail with Planogram Tools

The future of retail will undeniably be shaped by planogram tools. These tools enable retailers to strategize more effectively and enhance customer experience.

With continuous advancements, planogram software is becoming more intuitive and powerful. The integration with data analytics is setting new standards in store management.

Retailers who invest in these technologies are likely to see significant improvements. Increased efficiency, better product placement, and higher sales are some of the expected outcomes. Embracing planogram tools today sets the stage for future success in the retail industry. The evolution of these tools promises a more organized and profitable retail landscape.

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AI in category management
  • Kyle Dorfling Jason J Dorfling
  • 6 min read

Our AI Category Management Tools: Nova, Lola, and TrueView

By Kyle & Jason Dorfling, CEO & CFO of DotActiv

The retail landscape doesn’t just change; it accelerates. For years, DotActiv has been at the forefront of category management innovation, but what we are announcing today isn’t just an update—it is a fundamental shift in how retailers and suppliers will interact with data, shelf space, and each other.

We are entering a new era of intelligence. Today, I am proud to unveil a suite of capabilities that leverages the most advanced Artificial Intelligence to solve the oldest problem in retail: getting the right product, in the right space, at the right time, with zero friction.

1. Nova: Planogramming at the Speed of Thought

The days of manually dragging and dropping every single SKU are coming to an end. We are introducing Nova, our new AI-driven engine designed to automate the heavy lifting of category planning. Nova features Zero-Base Planogram Generation using AI.

Nova is able to leverage AI intelligence to recommend the flow of product groupings at each level of your consumer decision tree with the click of a button, whilst still allowing you to make changes based on your retail landscape.

The Evolution of Automation Nova is built to learn and scale. We are releasing the next leap forward with Fine-Tuned Planogram Learning, which will allow the engine to adapt to your specific category nuances and merchandising DNA rather than just following generic rules. 

Batch Generation capabilities will empower you to instantly scale your strategy, automating the creation of multiple size variants in a single run to eliminate manual rework.

Future-Proofing with Model Agnostic AI: Crucially, we have built Nova to be AI model agnostic. This is a massive strategic advantage for your business. In the rapidly evolving world of AI, locking yourself into a single proprietary model is a risk.

Instead, Nova allows you to connect to the latest and greatest models—such as Gemini and OpenAI—as they are released. This means your software doesn’t just stay current; it gets smarter every time the tech giants release an update. You are not buying a static tool; you are investing in a platform that evolves with the cutting edge of global AI development.

Please Note: The initial release of Nova is optimized strictly for shelf-based fixtures. Support for hanging bars and pegboards will be rolled out in future updates.

2. Lola: Your Intelligent Data Analyst

We have completely revamped the user interface and capabilities of Lola, an AI assistant that lives within the application. Lola is not a generic chatbot; she is a retail specialist who is contextually aware of what you are working on.

Lola offers:

  • Deep Context Awareness: Lola provides document-specific feedback and intelligence on your Planograms and DotActiv Report Templates (DRTs).
  • Database Interaction: You can ask Lola questions directly about the data in your database.
  • Instant Support: Connected to our PowerBase, Lola can answer support and training questions instantly, acting as an always-on expert sitting right next to you.

 

3. TrueView: Closing the Compliance Loop

A planogram is only as good as its execution. On February 16th, we are releasing TrueView, our image recognition solution.

TrueView allows for shelf space compliance reporting using image recognition. By simply analysing images of the shelf, we can now close the loop between the planogram and the physical reality in-store, ensuring compliance and maximising ROI.

 

4. DotActiv Viewer: Bridging the Gap

Collaboration is the heartbeat of successful category management. Also arriving on February 16th is the DotActiv Viewer.

This tool is designed specifically for collaboration with suppliers and internal users who need to sign plans off. It ensures that trade partners can view, comment on and approve plans without requiring the complexity of a full editing license.

 

Unbeatable Value: Competitive, All-Inclusive Pricing

We believe advanced AI shouldn’t be gated behind obscure pricing models. We have designed our new pricing tiers to be the most competitive in the market, bundling high-value capabilities like Image Recognition directly into your license volume.

Here is how we are delivering more value than anyone else, with clear estimates on what your credits can produce:

Flexible Tiers for Every Need: We offer a transparent structure based on “Credits” for AI usage per user. Because Nova is model agnostic, usage depends on the complexity of the AI model you choose:

  • Free Tier: Perfect for testing. Includes 20 credits (approx. 10 planograms per month, limited to Basic AI models).
  • Lite ($800/yr): For small independent stores. Includes 100 credits (approx. 6–50 planograms/month).
  • Pro ($2000/yr): For Category Managers. Includes 300 credits (approx. 18–140 planograms/month).

The Enterprise Advantage: Where We Stand Apart: Where DotActiv truly separates itself is in our Enterprise tiers. We don’t just sell you a tool; we equip your entire ecosystem.

  1. Enterprise ($4500/yr) Designed for Retail Teams, this tier provides 700 credits (approx. 40–330 planograms/month).
  • Included Collaboration: Every license includes 2 DotActiv Viewer licenses at no extra cost.
  • Included Image Recognition: If you hold 7 or more licenses, we include TrueView with Image Recognition (Image recognition for shelf compliance) completely free.
  1. Enterprise AI ($7500/yr) This is our flagship offering for Retail HQ and Batch Jobs, featuring a massive 4000 credits (approx. 230–1900 planograms/month).
  • Maximum Collaboration: Every license includes 5 DotActiv Viewer licenses.
  • Full TrueView Inclusion: If you purchase 4 or more licenses, we include the full TrueView and Image Recognition solution for planogram communication and compliance at store-level.

For those needing even more flexibility, the DotActiv Viewer is also available as a standalone license for just $300 – requires one or more licenses of DotActiv Enterprise or Enterprise AI. This way, no stakeholder is left behind.

 

Our Philosophy: Battle-Tested by Experts First

Finally, I want to address how we are delivering this. In the software world, there is often a rush to ship “beta” features that look good in a pitch deck but fail in practice.

At DotActiv, we operate differently. We are unique because we don’t just build software; we have an internal team of category planners who deliver end-to-end services for major clients globally.

Therefore, our release roadmap is deliberate:

  • Internal Release: These new AI capabilities are currently being deployed to our internal service teams. They are being battle-tested by category planners, for category planners.
  • Public Demos: We will begin demonstrating these capabilities publicly starting February 16th.
  • Client Release: Once we are satisfied that these tools meet our rigorous standards, Nova and Lola will be released to our software clients at the end of March.

We don’t use our clients as guinea pigs. We use our own experts to ensure that when this software reaches your hands, it is polished, powerful, and ready to perform.

We look forward to showing you what Nova, Lola, and TrueView can do.

See Nova, Lola, and TrueView in action! Schedule a personal online demo starting February 16th.

[Book Here]

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  • LeAnn Pieterse
  • 4 min read

PowerBase Updates For June 2025: What’s Updated

We’re committed to continuously enhancing your DotActiv experience, and this July brings another round of valuable updates to our PowerBase. During June, we updated five articles. These updates ensure you have the most accurate and up-to-date information, helping you navigate the software more efficiently and maximize its potential in your business.

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