Industry
Your merchandising report shows sell-through by region every Monday. The lost sale it cannot show happened on Saturday afternoon, when a shopper in Helsinki found an empty shelf and the same item sat boxed in a stockroom twelve kilometres away.
Retail generates a torrent of data — every scan, click, basket and delivery. The winners are not the ones who report it best. They are the ones who turn a stock-out, a pricing gap or a slipping store into an action before the customer notices.
Data domains
Customers and loyalty
Members, segments, purchase history and consent — the same shopper recognised across every channel.
CRM and loyalty · e-commerce accounts · point of sale
Products and range
The catalogue — hierarchy, attributes, lifecycle and substitutes that decide what can replace what.
merchandising / PLM · master data management · supplier catalogues
Orders and fulfilment
Sales and fulfilment across store, web and collection, from basket to delivered or returned.
point of sale · order management · e-commerce platform
Inventory and availability
Stock positions, movements and true availability by store, warehouse and in transit.
warehouse management · store inventory · ERP
Stores and estate
The store network — footfall, conversion, staffing and local performance against its own potential.
store systems · workforce management · footfall counters
Pricing and promotions
Prices, markdowns, promotions and competitor position — the levers behind volume and margin.
pricing engine · promotions management · competitor feeds
Suppliers
Supplier terms, lead times, fill rates and cost — how dependable the source of stock actually is.
purchasing · supplier portals · goods receipt
Digital channels
Web and app behaviour — sessions, baskets and journeys that reveal intent before a purchase.
e-commerce platform · web and app analytics · content management
Finance and margin
Sales, gross margin, markdown cost and inventory value — what a decision is actually worth.
ERP finance · merchandise financial planning
Source systems
What the analytics estate usually looks like
Typical data flow
Enterprise data to application
The general pattern. Data the business already produces becomes a governed product, gains business meaning, and ends up in an application where someone decides and acts.
Flagship experience
One place where a looming stock-out is detected, explained and resolved — weighing a transfer, an expedite or a promotion change against what each actually costs.
Application opportunities
One place where a looming stock-out is detected, explained and resolved — weighing a transfer, an expedite or a promotion change against what each actually costs.
Decision
This line is about to sell out where it matters most. What is the least costly way to protect the sale?
Turn the weekly store league table into a short list of specific actions for each store, ranked by what they are worth.
Decision
For this store this week, what are the few actions that would move the number most?
Plan a price move or a promotion with its likely effect on volume, margin and related lines shown before it goes live.
Decision
What will this price or promotion actually do to volume and margin, and is it worth it?
Bring a customer's history across store, web and app into one view, and turn it into the right next contact — with consent respected.
Decision
What does this customer's behaviour suggest is the right, permitted next step?
See which suppliers are actually reliable — on lead time, fill rate and quality — and act on the ones that are costing you sales.
Decision
Which supplier issues are hurting availability and margin, and what should we do about them?
Impact, readiness and speed are editorial judgement offered to help prioritise a conversation. They are not research statistics and they are not market data.
Illustrative demonstration. Kaari Retail Group, its stores, products, suppliers and all data shown are fictional. The generated assessment is deterministic and requires no AI service.