Find product affinities that can grow basket value
Identify combinations that occur together more often than expected and use support, confidence and lift to prioritise cross-sell or bundle ideas.
eCommerce Decision Intelligence
Know what customers will buy, what they buy together, and which action is worth taking.
Graphite Note helps eCommerce teams combine predictive models, customer analytics and item basket analysis to make better decisions about cross-sell, retention, targeting, assortment and demand.
Use cases
Turn transaction data into commercial decisions.
Prioritise retention by future value
Estimate customer lifetime value and repeat-purchase behaviour so retention spend follows expected economics rather than intuition.
Target customers more selectively
Use conversion, churn or repeat-purchase probabilities to decide who should receive the next offer, message or sales action.
Build audiences from behaviour
Combine RFM, general segmentation and cohort analysis to create usable commercial groups from order history.
Forecast demand at the useful level
Forecast revenue, orders, categories or items at the level where inventory, marketing and operating choices are made.
Connect model output to the next move
Package the recommendation, expected impact, audience and evidence so it can be activated in the existing eCommerce or CRM workflow.
Basket analysis example
“Frequently bought together” is more useful when the relationship is measured.
Raw co-occurrence can overstate popular items. Item basket analysis uses measures such as support, confidence and lift to distinguish frequent products from combinations that show a stronger-than-expected affinity.
That gives teams a better basis for recommendation widgets, bundles, checkout cross-sell, merchandising and promotional testing.
Explore Graphite Note AutoML modelsExample 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
eCommerce decision-intelligence questions.
What is item basket analysis in eCommerce?
Item basket analysis identifies products that appear together in customer orders and measures the strength of those relationships. It can support bundles, recommendation widgets, cross-sell and merchandising decisions.
Can Graphite Note predict which customers will return?
Yes. Depending on the dataset and business question, Graphite Note can use classification, customer lifetime value, cohort and new-versus-returning customer analysis to help quantify repeat-purchase and retention behaviour.
Does Graphite Note replace our eCommerce platform?
No. Graphite Note is a decision layer. Its models and recommendations can sit alongside the commerce, CRM, warehouse and marketing systems that already run the business.
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.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 eCommerce decision that is still being guessed.
We will scope the data, model and action around the commercial outcome you want to move.