Finding the products that belong together
A basket intelligence solution that analyzed transaction patterns, product relationships, customer behavior and purchase context to identify cross sell opportunities and improve the value of each shopping basket.
Retailers knew what customers bought, but had limited visibility into what they could buy together.
Transaction data contained strong product relationship signals, but teams relied on manual analysis, category knowledge and static merchandising rules to identify cross sell opportunities.
What we found
- Product associations were often identified through manual category analysis and merchant experience.
- Cross sell rules were static and did not adapt quickly to changes in customer behavior.
- Basket behavior differed by customer segment, store, season and channel, but was not consistently reflected in recommendations.
- Merchandising teams could see product sales but not the full network of products frequently purchased together.
- Online recommendations and store merchandising decisions were often based on different logic.
- Low frequency product combinations were difficult to distinguish from meaningful recurring relationships.
- There was limited measurement of incremental basket value generated by cross sell actions.
What the business needed
- A data driven view of product relationships across transactions.
- Basket analysis that considered customer, store, channel and time context.
- Product affinity scores that merchants could understand and act on.
- Recommendations that considered availability, price and business rules.
- Clear cross sell opportunities by category, customer segment and channel.
- A measurement framework for basket size, items per transaction and incremental revenue.
- A reusable capability that could support ecommerce recommendations and store merchandising.
We built a basket intelligence engine that connected product relationships with customer and shopping context.
The solution combined market basket analysis with recommendation models and business rules to identify practical cross sell opportunities rather than simply producing lists of product pairs.
Basket intelligence and cross sell platform
The platform created a reusable product relationship layer for merchandising, ecommerce and customer engagement teams.
- Integrated transaction level basket data with product hierarchy, pricing, promotions, customer segments, store and channel information.
- Applied market basket analysis to identify product combinations using support, confidence and lift measures.
- Created product affinity features at category, SKU, customer segment, store and channel level.
- Developed recommendation logic to rank complementary products based on basket context and customer behavior.
- Added business rules for inventory availability, product eligibility, pricing and merchandising priorities.
- Created opportunity views showing where customers were buying a core product without a commonly associated complementary product.
- Measured incremental basket value, cross sell conversion and recommendation driven revenue using controlled tests.
A connected product intelligence layer that turns transactions into cross sell decisions
The architecture provides one analytical foundation for merchandising, ecommerce and customer engagement teams while keeping recommendation logic measurable and governed.
A six stage approach to turn transaction data into practical cross sell opportunities
The implementation focused on high volume categories and measurable customer journeys before expanding the capability across the wider product catalog.
Prioritize
Select categories, customer journeys and channels where basket expansion can create material value.
Integrate
Connect transaction, product, customer, inventory, pricing and promotion data.
Analyze
Identify product relationships using basket metrics and validate meaningful associations.
Recommend
Build affinity scores and recommendation logic using customer and basket context.
Activate
Expose recommendations to ecommerce, merchandising, CRM and store teams.
Optimize
Run controlled tests and refine recommendations based on incremental commercial impact.
Basket intelligence helped identify practical opportunities to increase the value of each transaction.
Increase in average basket size
Relevant complementary products helped customers discover products that naturally fit their existing purchases.
Higher cross sell conversion
Contextual recommendations improved conversion compared with broad product promotion.
Faster relationship discovery
Automated basket analysis reduced the time required to identify meaningful product associations.
Increase in recommendation driven revenue
Targeted recommendations created measurable incremental revenue opportunities across selected journeys.
Faster merchandising analysis
Product relationship views gave merchants a consistent basis for category and assortment decisions.
Improvement in complementary product attachment
Identifying missing complementary purchases increased attachment across targeted product groups.
The retailer gained a practical way to grow basket value without relying only on discounts.
Product relationships became visible across customers, stores and channels, allowing merchandising and customer teams to focus on relevant complementary products.
Higher Basket Value
Complementary product recommendations created opportunities to increase items per basket and average transaction value.
Better Merchandising Decisions
Merchants gained evidence based visibility into product relationships that could inform displays, bundles and category decisions.
More Relevant Recommendations
Recommendations reflected customer and basket context instead of applying one product pairing rule across every customer.
Measurable Commercial Impact
Controlled measurement connected recommendation activity to incremental conversion, basket value and revenue.
Turn every basket into an opportunity for growth.
From transaction analysis and product affinity to recommendation models and controlled measurement, a connected basket intelligence platform can help retailers improve cross sell and grow basket value with greater precision.
Discuss your retail basket optimization program