Data Department helps retailers and online stores apply machine learning (ML) and AI (artificial intelligence) to everyday commercial decisions: how much to stock, what price to set, which customers to contact and what to recommend. For an e-commerce retailer, our dynamic pricing approach raised profitability by about 4%. We integrate with your POS (point of sale), ERP (enterprise resource planning) and e-commerce systems.
Where does AI pay off in retail and e-commerce?
Retail makes thousands of small decisions every day, across stores, products and customers. That volume is exactly where models beat manual rules: they make the same careful decision consistently, at scale.
| Decision | What ML or AI does | Typical data |
|---|---|---|
| How much to stock in each store | Demand forecasting by store, product and week or day, with uncertainty ranges | POS sales, stock levels, promotions, weather, holidays |
| How to split stock between warehouse, stores and online | Forecast-driven allocation and replenishment | Store and online sales, WMS (warehouse management system) stock positions |
| What price to show | Dynamic pricing that estimates how demand responds to price, within guardrails | Price and sales history, costs, competitor prices where available |
| When and how deep to mark down | Markdown optimisation that balances sell-through against margin | Stock age, sales rate, price history |
| Which customers to contact, with which offer | Churn and propensity models that rank customers by risk and likely response | Purchase history, loyalty and CRM (customer relationship management) data, campaign responses |
| What to recommend next | Recommender systems for products and content | Browsing, basket and order data |
| How to answer customer and staff questions | A RAG (retrieval-augmented generation) assistant over policies, product information and procedures | Product catalogues, policies, help-centre content |
Relevant case studies
- E-commerce dynamic pricing: for an e-commerce retailer, this approach raised profitability by about 4%.
- Outlet cooler performance: a related example from the sales channel. For a beverage bottler, our model predicted which outlets' coolers would underperform, so that field teams could act earlier.
Restaurant chains: our forecasting, staffing and supply work for restaurants is on the restaurants and food service page.
What are typical first projects for a retailer?
- Store-level demand forecasting for replenishment and waste reduction, starting with one category or region. See our demand forecasting service.
- A dynamic pricing pilot on a defined product set, measured on profit, not revenue. See our dynamic pricing service.
- A churn and retention model that ranks customers and recommends the next best offer, tested against a control group.
- Product recommendations for the online store, measured on basket value or conversion.
Each starts with a two-week Discovery: we rank use cases, check the data and agree the business metric before any model is built. The free 30-minute AI opportunity review is a quick first step.
Data and integration notes
- POS data is the foundation of most retail projects. Transaction-level data by store, product and time is ideal; daily summaries are often enough to start.
- ERP supplies stock, purchasing, costs and supplier lead times. Forecasts and recommended orders can be written back into ERP or replenishment tools.
- E-commerce platforms provide prices, sessions, baskets and orders for pricing and recommendation work.
- CRM and loyalty systems identify customers across visits, which churn and propensity models need.
- External signals such as weather, public holidays and search trends often improve forecasts, especially on unusual days.
- Infrastructure: we deliver on AWS (Amazon Web Services), Google Cloud, Azure or on-premises.
Frequently asked questions
Which retail AI project usually pays off first?
Demand forecasting for replenishment is often the strongest first project, because POS and stock data already exist and the savings in stock and waste are easy to measure. Pricing and retention are strong alternatives when margins or customer loss are the bigger issue.
Do we need customer-level data for demand forecasting?
No. Demand forecasting works on store and product sales. Customer-level data is needed for churn, propensity and personalised recommendations.
How do you make sure dynamic pricing does not damage margin or brand?
We agree guardrails with the commercial team, such as minimum and maximum prices and margin floors, and we measure success on profitability rather than revenue. For an e-commerce retailer, this approach raised profitability by about 4%.
Do you work with restaurant chains too?
Yes. Restaurant work has its own page, restaurants and food service, covering intraday sales forecasting, staffing and supply planning.