Product Affinity Analytics

Turn casual browsers into loyal buyers by uncovering true in-store intent. Our AI continuously models shopper affinity at the individual level by analyzing dwell time, movement patterns, and proximity to products, and then fusing these behavioral signals with live purchase history. Deliver personalized mobile coupons, targeted promotions, and product recommendations that boost conversion and repeat purchases.

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Harness machine learning and computer vision to bring ecommerce-level product affinity intelligence to the physical store floor — converting casual or exploratory shoppers into high-value repeat customers while continuously refining predictions for even greater accuracy over time.

Solution Overview
Individual-Level Affinity Intelligence at Every Touchpoint

Solution Capabilities

Behavioral Signal Capture

Dwell time, traffic flow, and product co-location signals captured with product-level precision.

Late-Fusion RFM Modeling

Behavioral intent fused with continuously updated RFM profiles from POS, facial recognition, and mobile device identification.

Closed-Loop Engagement Engine

Hyper-personalized coupons, promotions, and recommendations delivered through our app or integrated systems.

Computer Vision at the Product Level

Behavioral Signal Capture

Our platform measures customer behavior with product-level precision by capturing dwell time, traffic flow, and co-location signals, combining them through a late-fusion model with continuously updated Recency-Frequency-Monetary (RFM) profiles derived from POS data, facial recognition, and mobile device identification.

By separating behavioral intent from transactional value and recombining them with adaptive weighting, we generate an accurate, identity-level view of each shopper’s product affinity and purchasing potential. This approach eliminates blind spots — so purchase history alone doesn’t miss in-store intent, dwell time alone doesn’t overlook buying power, and traffic flow alone doesn’t confuse exposure with genuine interest.

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Behavioral Signal Map
Heat-map overlay showing dwell time, traffic flow, and product co-location signals across store floor

Identity-Level Affinity Intelligence

Late-Fusion RFM Modeling

Behavioral intent is separated from transactional value and recombined with adaptive weighting — generating an accurate, identity-level view of each shopper’s product affinity and purchasing potential.

Recency

How recently a shopper engaged with or purchased a product category.

Frequency

How often behavioral and transactional signals repeat across visits.

Monetary

Purchase value derived from POS data, enriching intent with buying power context.

RFM profiles are continuously updated from POS data, facial recognition, and mobile device identification. This eliminates the critical blind spots of single-signal approaches: purchase history alone misses in-store intent, dwell time alone overlooks buying power, and traffic flow alone confuses passive exposure with genuine interest. The late-fusion architecture ensures each signal type is properly weighted before being combined, yielding predictions that are far more accurate than any single data stream could produce.

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Late-Fusion RFM Architecture
Diagram showing behavioral signals and POS data streams merging into an adaptive RFM fusion model

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End-to-End Product Affinity Intelligence Pipeline
Full-width architectural diagram: in-store camera feed → behavioral signal capture → late-fusion RFM model → identity-level affinity scores → personalized coupon & recommendation delivery

Personalized Engagement at Scale

Closed-Loop Engagement Engine

Using affinity insights, retailers can deliver hyper-personalized mobile coupons, personalized promotions, and product recommendations through our app or integrated systems.

The result is a powerful closed-loop engagement engine that converts casual or exploratory shoppers into high-value repeat customers, while continuously learning and refining predictions for even greater accuracy over time. Each completed purchase cycle feeds back into the model — tightening affinity scores, improving coupon relevance, and ensuring recommendations become sharper with every visit.

Continuously Self-Improving: Every engagement outcome feeds back into the affinity model — so predictions get sharper, coupons get more relevant, and conversion rates improve with each passing visit.

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Engagement Engine Dashboard
Screenshot showing personalized coupon delivery, recommendation feed, and conversion tracking in the Nexus dashboard

Product Affinity Analytics

Solution Capabilities

Behavioral Signal Capture

Late-Fusion RFM Modeling

Closed-Loop Engagement Engine

Behavioral Signal Capture

Late-Fusion RFM Modeling

Closed-Loop Engagement Engine

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