Hyper‑Personalization Engine for B2B Commerce
Use signals + rules to deliver 1:1 recommendations, search and content that lift revenue with full control.
Signals we combine
We build a compact feature store mixing user, session and catalog context.
Profile & segment
Account, industry, price tier, lifecycle stage, consented traits.
Behavior
Views, searches, carts, purchases, dwell and recency.
Context
Device, location, time, campaign, referrer, channel.
Catalog graph
Attribute vectors, compatibility and co‑view/co‑buy signals.
Feedback
Clicks, saves, hides; implicit and explicit ratings.
Consent & policy
Purpose‑bound usage with opt‑out and region rules.
Decision modes
Combine rules, ML and experimentation to balance control and performance.
Business control
Merch rules, pinning and overrides to keep strategy intact.
GovernanceSimilarity & embeddings
Vectors + metadata for cold‑start and precise matches.
Cold startNext‑best action
Session sequences for fast PDP/listing impact.
SpeedExplore / exploit
Multi‑armed bandits to learn faster with guardrails.
OptimizationLLM‑assisted ranking
Re‑rank and explain results with controllable prompts.
ExplainabilityRecommendation types — quick guide
Pick the approach that fits data availability, control needs and time‑to‑value.
Content‑based
Similar items by attributes and vectors; great for cold‑start and controllability.
Collaborative
Learn from aggregate behavior (co‑view/buy) to surface crowd wisdom.
Session‑based
Sequence‑aware “next” picks for short journeys; fast impact on PDP/listing.
Hybrid
Blend rules + content + collaborative for robust coverage and guardrails.
Reference architecture
Stream events, derive features, train or configure models, store vectors and decisions, then deliver to web and APIs with evaluation loops.
Start with one KPI and one surface
Pick PDP recs or search ranking, set a KPI (CTR, AOV, conversion), and iterate weekly with tight governance.
Discuss your needs