Recommendation Engine

Recommendations that know the buyer

Generic recommendation engines suggest products based on "customers who bought X also bought Y." They ignore context - buyer role, purchase intent, current need, timing. B2B buyers get consumer product suggestions. First-time visitors see recommendations for returning customers.

Recommendation Engine provides contextual recommendations that understand role, history, intent, and business context. Works across product merchandising, content suggestions, upsell/cross-sell guidance, and next-best-action scenarios for both customer-facing and internal applications.

Designed for businesses facing high catalogue complexity, diverse customer segments, and situations where generic "you might also like" falls flat.

What you get

Recommendation logic that adapts to context - who's asking, what they need, why they're here. Our customers use it to drive product discovery, guide content journeys, suggest relevant upsells, and provide next-best-action guidance that actually fits the situation.

Core capabilities

Product Recommendations

Suggests relevant products based on browsing behaviour, purchase history, role, and current context. Goes beyond 'also bought' to understand intent and timing.

Content Suggestions

Surfaces relevant documentation, articles, guides, or resources based on user role, current task, and knowledge gaps. Helps users find what they need without searching.

Upsell & Cross-Sell Guidance

Identifies appropriate upsell or cross-sell opportunities based on customer value, purchase patterns, and compatibility, not just revenue maximisation.

Next-Best-Action

Recommends what customers or internal teams should do next, complete profile, review order, contact support, explore category, based on journey stage and behaviour signals.

Role-Based Personalisation

Adapts recommendations to buyer role (purchasing manager vs. end user), account type (new vs. established), and relationship stage (prospect vs. customer).

What it connects to

Recommendation Engine pulls from commerce platforms, CRM, browsing behaviour, purchase history, content engagement, and product catalogues via Bytebard Data Mesh.

Works with:

  • Commerce platforms for product recommendations
  • CMS for content suggestions
  • CRM for account context and relationship data
  • Analytics for behaviour signals
  • PIM for product attributes and compatibility

Recommendations improve as the engine learns from user interactions, conversions, and feedback.

How it works

Recommendations are contextual, not just collaborative filtering. The engine considers:

  • Role context, B2B purchasing managers see different recommendations than end users
  • Intent signals, browsing behaviour, search queries, and journey stage inform relevance
  • Business rules, margin requirements, inventory levels, and compatibility constraints guide suggestions
  • Feedback loops, clicks, conversions, and rejections refine future recommendations

This isn't a black box. Rules are transparent, editable, and aligned to business priorities - not just algorithmic correlation.

Talk about recommendations.

Let's discuss how contextual recommendations can improve discovery and conversion.

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