Where will demand move?
Forecast at store, category, product or regional level so stock, staffing and planning decisions happen where the business operates. See what happens after the forecast.
GN Retail Decision Pack · Retail Decision Intelligence
Use first-party transaction, customer, product and store data to decide where demand is moving, which customers matter most, and where stock, targeting or store-level action should change.
Decisions this pack supports
Forecast at store, category, product or regional level so stock, staffing and planning decisions happen where the business operates. See what happens after the forecast.
Use CLV, churn or repeat-purchase signals to focus retention and acquisition effort where expected value is highest.
Balance local demand, substitution, cannibalisation, margin and operating constraints instead of ranking products on sales alone. See assortment optimisation.
Combine propensity, value and operational constraints to prioritise targeting, offers, inventory or store interventions.
Models inside this pack
GN Retail combines the models, business logic and constraints needed to move from your operating data to a decision-ready recommendation.
Built aroundTransactions · customers · products · stores
Models are combined only where the decision needs them. The pack returns a business-ready action with supporting evidence and expected impact.
Forecasts demand at store, category, product or regional level with uncertainty where useful.
What to stock, where to allocate it and when plans should change.
Estimates customer value, repeat-purchase likelihood, churn or response propensity.
Which customers deserve retention, acquisition or campaign priority.
Combines local demand, product value, substitution, cannibalisation and operating constraints.
Which SKUs each store, channel or customer set should carry.
Separates observed lift from credible incremental effect and models response to commercial interventions.
Which promotion or pricing action is worth repeating, changing or stopping.
Combines predictions, expected effects and real constraints into a feasible ranked action.
What stock, targeting, assortment or store-level move should happen next.
Pack anatomy
Prebuilt where the decision pattern repeats. Configured where your business is different.
Transactions, customer history, products, stores, campaigns, inventory, pricing and relevant external drivers.
Use the modelling layer needed for the question rather than forcing every use case through the same algorithm.
Encode the business objective, available interventions, budgets, capacity or policy rules that make the recommendation feasible.
Return a forecast, prioritised audience, store action, intervention or recommended allocation with evidence and expected impact.
Prebuilt + configured
Graphite Note reuses decision workflows, modelling patterns, evaluation methods and output structures. We configure the objective, constraints, data mapping and intervention set around how your retail business actually works.
Model patterns, evaluation, governance and output structure.
Objectives, interventions, constraints, thresholds and data mapping.
Your customers, products, stores, economics and operating knowledge.
Direct answers
It is a packaged Decision Intelligence solution for recurring retail decisions across demand, customer value, targeting and store-level planning. It combines reusable decision workflows with the retailer's own first-party data, objectives and constraints.
Typical inputs include transactions, customer history, products, stores, campaigns, inventory, pricing and relevant external drivers. The exact dataset depends on the decision being made.
Depending on the problem, Graphite Note can use predictive machine learning, AutoML, causal methods and optimisation. The pack is organised around the decision, not around a single model type.
The target output is a business-ready decision such as a forecast, prioritised customer or store list, recommended intervention or allocation, together with the evidence and expected impact needed to act.
We will scope the data, models, constraints and action around the outcome you need to move.