Personalization for measurable lift
CTR +15–25% AOV +20–30% Conversion +15–25%

Hyper‑Personalization Engine for B2B Commerce

Use signals + rules to deliver 1:1 recommendations, search and content that lift revenue with full control.

Calculate your revenue potential See implementation plan
Signal layer

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.

Decisioning

Decision modes

Combine rules, ML and experimentation to balance control and performance.

Business control

Merch rules, pinning and overrides to keep strategy intact.

Governance

Similarity & embeddings

Vectors + metadata for cold‑start and precise matches.

Cold start

Next‑best action

Session sequences for fast PDP/listing impact.

Speed

Explore / exploit

Multi‑armed bandits to learn faster with guardrails.

Optimization

LLM‑assisted ranking

Re‑rank and explain results with controllable prompts.

Explainability
Recommendation types

Recommendation types — quick guide

Pick the approach that fits data availability, control needs and time‑to‑value.

Best for cold‑start

Content‑based

Similar items by attributes and vectors; great for cold‑start and controllability.

Best for scale

Collaborative

Learn from aggregate behavior (co‑view/buy) to surface crowd wisdom.

Best for speed

Session‑based

Sequence‑aware “next” picks for short journeys; fast impact on PDP/listing.

Best for control

Hybrid

Blend rules + content + collaborative for robust coverage and guardrails.

System flow

Reference architecture

Stream events, derive features, train or configure models, store vectors and decisions, then deliver to web and APIs with evaluation loops.

  1. Events Web + app signals
  2. Features Feature store
  3. Models ML + rules
  4. Storage Vectors + cache
  5. Decisioning Ranking API
  6. Delivery PDP, search, email
  7. Measure KPIs + evals
Next step

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