Making every retail interaction more relevant
A personalization platform that combined customer history, product affinity, channel behavior and real time interactions to determine which products, offers and next actions were most relevant to each customer.
Customers were receiving broadly similar experiences even though their needs, behavior and purchase history were different.
Customer information was available across loyalty, POS, ecommerce and digital systems, but the data was not consistently brought together to drive personalized decisions at the point of interaction.
What we found
- Customer purchase history was available but was not consistently used in campaign decisions.
- Online browsing and search behavior remained separate from store and loyalty activity.
- Promotions were often selected using broad customer groups rather than individual behavior.
- Product recommendations were limited or based on simple category rules.
- Marketing teams spent time creating audiences and validating customer lists manually.
- Customer interactions across web, app, store and CRM were not measured through a common journey view.
- Business teams lacked a reusable framework to test whether personalization improved conversion and basket value.
What the business needed
- A consistent customer profile combining historical and current behavior.
- Reusable customer segments based on value, behavior and product affinity.
- Recommendation and propensity models that could support relevant offers and products.
- Rules to prevent irrelevant offers when products were unavailable or customer eligibility was not met.
- Faster creation of campaign audiences across channels.
- Measurement of conversion, basket value, response and incremental uplift.
- A scalable foundation for next best action and personalized customer journeys.
We connected customer intelligence with product and channel context to make personalization measurable and actionable.
The solution combined customer 360 data, segmentation, recommendation models and business rules so that the right customer could receive a more relevant product or offer through the right channel.
Personalization and next best action platform
The platform created a common decision layer for personalized offers, product recommendations and customer engagement.
- Integrated customer profiles, loyalty data, POS transactions, ecommerce activity, app behavior and campaign history.
- Built customer features covering recency, frequency, monetary value, category affinity, price sensitivity, channel preference and engagement.
- Created behavioral customer segments for active, high value, new, dormant and at risk customers.
- Developed product and category recommendation models using purchase patterns, affinity and customer behavior.
- Added business rules for product availability, eligibility, frequency limits and campaign priorities.
- Created next best action logic for offers, recommendations and engagement across digital and CRM channels.
- Established measurement for conversion, basket value, response and incremental campaign performance.
A connected customer intelligence layer linked customer behavior to products, offers and engagement channels
The architecture separates the governed customer data foundation from the decision and activation layer, making the capability reusable across retail use cases.
A six stage approach to move from broad campaigns to measurable personalization
The implementation started with high value customer journeys and expanded as data quality, model performance and business adoption improved.
Map
Identify customer journeys, channels, source systems, available signals and priority personalization opportunities.
Unify
Create the trusted customer profile and connect customer, product, transaction and interaction data.
Segment
Build reusable behavioral and value based customer segments for priority journeys.
Model
Develop propensity, affinity and recommendation models and combine them with business rules.
Activate
Deliver relevant offers and recommendations across digital, CRM and store supported journeys.
Optimize
Measure incremental response and continuously refine segments, models, rules and customer journeys.
Personalization created a more targeted customer experience while reducing manual campaign preparation.
Conversion uplift
More relevant products and offers improved customer response across targeted journeys.
Higher basket value
Product affinity and next best action recommendations supported stronger cross sell and basket expansion.
Faster audience creation
Reusable customer segments reduced repeated data preparation for marketing campaigns.
Lower irrelevant offer exposure
Eligibility, availability and customer context reduced poorly targeted offers.
Faster campaign analysis
Common measurement definitions reduced the time required to compare campaign response and customer segments.
Improvement in repeat engagement
Relevant follow up recommendations and offers increased engagement among targeted customer groups.
Personalization became a measurable business capability rather than a collection of campaign rules.
The solution gave marketing, merchandising, digital and customer teams a common foundation for understanding customers and improving each interaction.
More Relevant Customer Experiences
Customers received products, offers and actions based on their behavior, value and current context rather than broad customer groups alone.
Higher Commercial Value
Recommendation and next best action capabilities created opportunities to improve conversion, basket value and repeat engagement.
Faster Marketing Execution
Reusable segments and customer features reduced manual audience preparation and made campaign activation more consistent.
Foundation for Advanced Personalization
The customer intelligence layer created a reusable base for retention, churn prediction, basket optimization and real time decisioning.
Make customer data work at the point of interaction.
From Customer 360 and segmentation to recommendation models and next best action, a connected personalization platform can help retailers turn customer intelligence into measurable commercial outcomes.
Discuss your customer experience transformation