What should we recommend or bundle?
Measure product affinity and basket relationships to prioritise cross-sell, recommendation and bundle tests.
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GN eCommerce Decision Pack · eCommerce Decision Intelligence
Package customer and transaction data into the next commercial move.
For growth and retention teams deciding which products, offers and customer actions to prioritise. Use orders, customer behaviour and product data to decide what to recommend, who to retain, which audiences to target and where demand or basket value can move.
Decisions this pack supports
Turn digital behaviour into commercial choices.
Who is worth retaining or targeting?
Use lifetime value, churn, repeat-purchase and propensity signals to focus commercial effort.
What will sell, and where should we plan for it?
Forecast orders, revenue, categories or products at the level where inventory and marketing decisions are made.
From today's process to the next decision
Choose offers that make commercial sense.
How teams make the decision
Teams use platform recommendations, campaign reports and broad customer segments to select products, offers and retention audiences.
What this leaves unresolved
A likely click or purchase does not establish that an offer is worth sending. Discounts, low-margin products and unavailable stock can erode the value of a successful campaign.
What the GN Pack adds
Combine product affinity and customer value with margin, stock and contact rules to rank the products, offers and audiences worth testing next.
What your team receives
A prioritised customer audience, product bundle or next-best offer, with supporting evidence and the commercial rules used to select it.
See the pack in action
From your business data to a decision you can act on.
Explore what a director or manager could receive. Choose a model, see its output, then compare practical actions with costs and constraints.
Illustrative interactive demoAll figures and businesses are fictional. These are examples of potential deliverables, not actual customer results, guarantees or live predictions.
ECOMMERCE / EXAMPLE DECISION WORKSPACE
Which offers are worth sending?
See how demand, propensity and merchandising translate into profitable customer decisions.
01 / Explore a model output
EXAMPLE OUTPUT 01 / 05
1,840 shoppers
Relevant bundle audience
What we found
Recent baskets suggest performance socks may complement running-shoe purchases.
Recommended next move →
Test a shoe-and-sock bundle against a control group.
Decision-relevant output1,840 relevant shoppers
02 / Compare possible decisions
Should we discount everyone or target selectively?
Switch between the options and compare expected contribution after intervention costs. Numbers use fixed fictional assumptions.
Gross incremental contribution+€8,500
Intervention cost€2,000
Net incremental contribution+€6,500
Selected action
Target 185 shoppers
Send a 10% incentive only to selected customers.
Estimated incremental gross contribution €8,500 less €2,000 in incentives and contact costs.
Scope: 185 customersFeasibility: Within stated scenario rules
Operating constraints considered
See what this pack could recommend for your business →- Maximum 1,240 eligible customers
- Stock availability and discount cost
- Compare incremental contribution, not only attributed orders
Read every example output and decision alternative
Basket / Affinity Model: 1,840 shoppers — Relevant bundle audience
Recent baskets suggest performance socks may complement running-shoe purchases. Test a shoe-and-sock bundle against a control group. 1,840 relevant shoppers.
Customer Value + Propensity: 1,240 customers — At risk of not returning
Repeat-purchase likelihood has decreased across this audience; propensity alone does not prove an offer would help. Estimate incremental response before giving away margin. 1,240 customers to evaluate.
Demand Forecasting: €186,000 — Projected revenue next week
Approximately 2,420 orders are expected; three product categories face stock pressure. Prioritise replenishment and avoid promoting unavailable goods. 2,420 forecast orders.
Assortment / Merchandising Model: 12 products — Higher-value display candidates
Margin, demand and stock availability favour these products over eight slow-moving alternatives. Test better placement on relevant category pages. 12 product candidates.
Next-Best-Action Optimisation: 185 shoppers — Selected for a targeted offer
The rest of the candidate audience has insufficient expected incremental return at the proposed incentive cost. Offer a suitable incentive to 185 eligible shoppers; hold out a control group. +€6,500 modelled net contribution.
Decision options
Send no incentives this period. No incremental contribution and no incentive cost. Scope: 0 customers.
Send a 10% incentive only to selected customers. Estimated incremental gross contribution €8,500 less €2,000 in incentives and contact costs. Scope: 185 customers.
Offer 15% to the entire at-risk audience. More orders can still mean lower net contribution due to unnecessary discounts. Scope: 1,240 customers.
Models inside this pack
One product bundle around a recurring business decision.
GN eCommerce combines the models, business logic and constraints needed to move from your operating data to a decision-ready recommendation.
DECISION PACKGN eCommerce
Built aroundOrders · customers · products · campaigns · inventory
Basket / Affinity ModelFinds product relationships, bundle candidates and cross-sell patterns from order-level behaviour.
Customer Value + PropensityEstimates CLV, repeat-purchase likelihood, churn and response propensity.
Demand ForecastingForecasts orders, revenue, categories or products at the planning level that matters.
Assortment / Merchandising ModelCombines demand, affinity, margin and stock constraints to compare catalogue choices.
Next-Best-Action OptimisationRanks candidate products, offers or audiences under margin, stock and contact constraints.
OUTPUTDecision support your team can use
Models are combined only where the decision needs them. The pack returns a business-ready action with supporting evidence and expected impact.
- Audience
- product
- offer
- inventory move
- Expected impact and supporting evidence
- Configured around your real operating constraints
Basket / Affinity Model
Finds product relationships, bundle candidates and cross-sell patterns from order-level behaviour.
Which product pair, bundle or recommendation should be tested.
Customer Value + Propensity
Estimates CLV, repeat-purchase likelihood, churn and response propensity.
Who to retain, target or suppress from costly outreach.
Demand Forecasting
Forecasts orders, revenue, categories or products at the planning level that matters.
Where inventory, budget and campaign capacity should be placed.
Assortment / Merchandising Model
Combines demand, affinity, margin and stock constraints to compare catalogue choices.
Which products deserve visibility, range space or bundle support.
Next-Best-Action Optimisation
Ranks candidate products, offers or audiences under margin, stock and contact constraints.
Which commercial action should happen next for each audience or product.
Pack anatomy
One repeatable path from commerce data to action.
Prebuilt decision patterns, configured around your catalogue, customers and economics.
Your commerce data
Orders, customers, products, baskets, campaigns, channels, inventory and relevant behavioural signals.
Predictive and affinity models
Propensity, CLV, churn, segmentation, basket analysis, forecasting and other models selected for the decision.
Commercial constraints
Margin, stock, audience limits, suppression rules, campaign capacity and other conditions that make an action usable.
A recommendation ready to activate
Return the audience, product pair, offer, forecast or prioritised action into the existing commerce or CRM workflow.
Prebuilt + configured
The decision pattern repeats. Your catalogue and customers do not.
Graphite Note reuses proven modelling and decision workflows while configuring the objective, data mapping, product economics, intervention set and business rules around your operation.
Example decision
Which product pair should we test next?
Use basket-level transaction data to rank candidate combinations, then measure whether the recommended bundle increases attach rate, average order value or conversion.
Direct answers
Questions about the GN eCommerce Decision Pack.
What is the GN eCommerce Decision Pack?
It is a packaged Decision Intelligence solution for recurring commerce decisions across product affinity, customer value, targeting, retention and demand.
What data does it use?
Typical inputs include orders, customers, products, basket contents, campaigns, channels and inventory. The exact input set depends on the decision.
Does it replace our eCommerce platform or CRM?
No. Graphite Note acts as a decision layer. Its outputs can be activated through the commerce, CRM, warehouse and marketing systems already in place.
What comes out?
Examples include ranked customer audiences, product affinities, retention priorities, forecasts and recommended next actions with supporting evidence.
Decision IntelligenceFrom signal and prediction to a governed business action.AutoMLAutomate repetitive model-building work without losing business context.Causal Machine LearningSeparate drivers from correlations and estimate intervention effects.Predictive AnalyticsForecast demand, score customers and anticipate business outcomes.Decision OptimisationCompare feasible actions and rank them by expected business impact.Forecasting vs OptimisationSee where prediction ends and the business decision begins.Decision APIDeliver recommendations, expected impact and evidence into workflows.Embedded Decision IntelligenceAdd a decision layer to ERP, CRM, BI, workflow and vertical software.Build vs BuyA practical framework for deciding whether to build, buy or embed.
Bring one recurring eCommerce decision.
We will scope the data, models and action around the commercial outcome you want to move.