AI FOR RETAIL & CPG
AI in Retail: From the Shelf to Commercial Strategy
Field reps waste time auditing product presence in stores. AI computer vision automates shelf audits: price, facing, shelf level, out-of-stock. Rakam case study with Statigest (BEL/Materne, Mars, St Michel).
Assessment
Business Challenges
Time Wasted on Field Audits
Field reps spend hours manually recording facings, prices and out-of-stocks on shelves. A full audit takes an average of 30 minutes per store. Lengthy data entry, frequent errors, data used too late to act.
Scattered Data
Explore data, retailer Data Sharing, panel rankings, field reports: sources are multiple and rarely cross-referenced. Sales teams lack a unified view to make decisions.
Manual Sales Support
In-store sales pitches are prepared manually. Field reps don't have real-time access to data that would justify an assortment or promotion recommendation.
Inaccurate Product Recognition
Existing shelf image recognition solutions struggle to achieve sufficient accuracy on products with similar packaging. Unreliable automated audits remain a barrier to adoption.
Prioritization
ICE Matrix: Where to Start?
Quick Wins
High impact, low effort
- → Automated field data entry — Intelligent pre-filling of visit forms from visual data and store history. Over 50% reduction in data entry time.
- → Conversational BI assistant — "What are my top 10 stores by distribution on this category?" — natural language queries across sell-out, panel and field data.
Strategic
High impact, medium effort
- → Computer vision product recognition — Automatic detection of facings, prices and out-of-stocks from shelf photos.
- → Visit planning — Automatic prioritization of stores to visit based on commercial potential and out-of-stock alerts.
- → Planogram comparison — Overlay of the negotiated planogram with the actual shelf photo. Detection of placement deviations.
Long Term
Transformative impact, high effort
- → Promotion recommendations — Analysis of promotional history and price elasticity to recommend the most effective mechanics per retailer.
- → Sales forecasting — Demand forecasting models per store, integrating seasonality, promotions and external data.
To build your own roadmap, check out our AI roadmap guide.
Applications
Key Use Cases
Computer Vision Product Recognition
Automatic detection of facings, displayed prices and out-of-stocks from shelf photos. The field rep photographs the aisle and gets a structured audit in seconds.
Automated Field Data Entry
Intelligent pre-filling of visit forms from visual data and store history. Over 50% reduction in data entry time and more reliable reported data.
Intelligent Visit Planning
Automatic store prioritization based on commercial potential, out-of-stock alerts and period objectives. AI optimizes field routes.
Conversational BI Assistant
"What are my top 10 stores by distribution on this category?" — natural language queries across sell-out, panel and field data. Instant answers without Excel exports.
Promotion Recommendations
Analysis of promotional history and price elasticity to recommend the most effective mechanics per retailer and category. AI identifies high-ROI promotions.
Theoretical vs Actual Planogram Comparison
Automatic overlay of the negotiated planogram with the actual shelf photo. Detection of placement gaps, missing references and commercial agreement violations.
Benchmark
How Leaders Are Integrating AI
EasyPicky
In-store video recognition via smartphone. The field rep films the aisle and gets a complete audit (facings, prices, out-of-stocks) in real time without manual entry.
Trax
Computer vision shelf analytics at global scale. Automated analysis of shelf presence, share of shelf and planogram compliance for major CPG groups.
Planorama
Image recognition solution specialized in shelf analysis. Product detection, facing counting and merchandising compliance verification by photo.
Case Study
What Rakam Has Deployed
Statigest
Retail & CPG | Mobile Computer Vision
Statigest develops a field audit solution for CPG brand sales teams. Field reps waste time recording product presence in stores: price, facing, shelf level, out-of-stock. AI computer vision automates this audit in seconds.
Rakam designed and deployed the mobile computer vision engine that enables field reps to photograph a shelf and instantly get a structured audit: product identification, facing count, price and out-of-stock detection. An audit that averaged 30 minutes is reduced by over 50%.
The solution is deployed in production with studies conducted for brands like BEL/Materne, Mars and St Michel. Field teams use it daily to make their audits more reliable and save time in stores.
96%
Recognition Accuracy
92%
User Satisfaction
-50%
Field Audit Time
Discover our products to build your own AI retail solution.
Roadmap
Suggested AI Roadmap for Your Retail Software
Based on our experience with companies like Statigest, here is the progression we recommend.
Field Productivity
→ Automated field data entry
→ Conversational BI assistant
Goal: reduce data entry time, make data accessible in natural language
Vision and Planning
→ Computer vision product recognition
→ Intelligent visit planning
Goal: automate shelf audits, optimize field routes
Compliance and Recommendations
→ Theoretical vs actual planogram comparison
→ Promotion recommendations per retailer
→ Merchandising gap detection
Goal: ensure shelf compliance, maximize promotional ROI
Forecasting and Optimization
→ Sales forecasting per store
→ Field route optimization
→ Assortment impact simulation
Goal: anticipate demand, maximize field coverage
Each roadmap is tailored to your context. This progression is indicative and adjusts based on your priorities and technical maturity. Discover our complete AI roadmap framework.
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