Know which customers you’re about to lose — and which are ready to grow
FyndEm Retail detects behavioral shifts in transactional data weeks before churn becomes visible — and surfaces the customers primed for growth that your dashboards can’t see.
⚠️ The Problem
Retail and e-commerce teams rely on backward-looking metrics — same-store sales, RFM segmentation, and lapsed-customer reports. These tools identify churn after it happens and miss the subtle behavioral signals that precede it: declining visit frequency, shrinking basket diversity, shift in category mix, or loss of promotional responsiveness.
✓ The FyndEm Approach
FyndEm Retail applies behavioral pattern analysis to your existing transactional data — POS, e-commerce, and loyalty records — to detect the early signals of churn, identify customers migrating between value segments, and surface cross-sell opportunities that rules-based systems cannot find. No new data collection. No IT integration. Insights in days.
Customer Churn Prediction
Identify customers showing early behavioral signs of disengagement 60–90 days before they stop purchasing. Prioritize retention outreach where it will have the highest impact.
Segment Migration Scoring
Detect customers whose purchasing patterns indicate readiness to move into a higher value segment — and surface those at risk of downward migration before it happens.
Cross-Sell Propensity
Uncover basket affinity patterns and product adjacencies that reveal which customers are most likely to respond to category expansion offers — beyond standard recommendation engines.
Lapsed Customer Recovery
Not all lapsed customers are equally recoverable. FyndEm ranks lapsed customers by reactivation probability, so your win-back campaigns focus spend where it matters.
Designed For
Retail chains, e-commerce operators, DTC brands, loyalty program managers, merchandising teams, and retail analytics groups seeking predictive intelligence beyond traditional BI dashboards. Works with existing POS data, e-commerce platforms (Shopify, Magento, WooCommerce), and loyalty program exports.
🏪 Multi-Location Retail
Identify which store locations are experiencing early-stage customer attrition and which are gaining share — at the individual customer level, not just aggregate foot traffic.
💻 E-Commerce & DTC
Predict which online customers will become one-time buyers versus repeat purchasers within the first 30 days of acquisition — enabling targeted nurture sequences that improve LTV.
💳 Loyalty Programs
Move beyond points-based engagement to behavioral intelligence — identifying which loyalty members are truly engaged versus which are mechanically redeeming without deepening their relationship.
📊 Category Management
Understand which product categories are gateway purchases that lead to broader basket expansion — and which are associated with customer attrition when discontinued or repriced.
🎯 Marketing Efficiency
Replace broad-based promotional campaigns with precision targeting — reaching the customers most likely to respond with the offers most likely to drive incremental behavior change.
🔗 Omnichannel Intelligence
Unify behavioral signals across in-store and online channels to build a complete picture of customer trajectory — detecting channel migration patterns before they impact revenue.
Transaction History
POS or e-commerce order data with customer ID, date, items, and amounts
Customer Records
Basic customer demographics and account creation dates
Loyalty Data
Points, tier status, redemption history (optional but enriching)
Product Catalog
SKU-level category and pricing data for basket analysis
Powered by CentroidAI’s Behavioral Intelligence Engine
The same patent-pending AI architecture that predicts surgeon adoption, donor churn, and disease progression — applied to retail customer behavior. Every engagement begins with a retrospective on your own data.
See it applied to your customer data
Every engagement begins with a retrospective diagnostic — a run on your own historical data that shows what FyndEm Retail would have predicted before it happened. No commitment required.