Recommendation Engines

Recommendation Engines

Recommendation systems that increase engagement, conversion, and average order value — personalised without feeling creepy or forced.

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25+
Recommendation Systems Built
20%+
Avg Conversion Lift
15%+
Avg AOV Increase
100%
A/B Tested Deployments
Recommendation Engines

Recommendation Engines Built for Real Business Impact

A recommendation engine's value is measured in engagement, conversion, and revenue lift — not algorithmic sophistication for its own sake. We build recommendation systems matched to your catalogue size, data volume, and business goals, and validate impact through rigorous A/B testing rather than assuming the model works.

  • Collaborative filtering and content-based recommendation models
  • Hybrid recommendation strategies for cold-start scenarios
  • Real-time personalisation based on session behaviour
  • A/B testing framework for measuring recommendation impact
  • Recommendation diversity and business-rule integration
  • Integration with e-commerce, content, or app platforms
Our Approach

Why Our Recommendation Engines Delivery Works

We explicitly address the cold-start problem — new users and new products with no history — since this is where most naive recommendation systems fail, using hybrid approaches that blend behavioural and content signals.

Cold-Start Handling

Hybrid strategies that still recommend well for new users and products.

Real-Time Personalisation

Recommendations that adapt to session behaviour, not just historical data.

Rigorous A/B Testing

Every recommendation strategy validated against real conversion impact.

Business Rule Integration

Recommendations respect inventory, margin, and merchandising rules.

Delivery Process

How We Deliver Recommendation Engines

We build and test recommendation strategies iteratively, using A/B tests to validate real business impact before fully rolling out any given approach.

  • Analyse catalogue size, data volume, and user behaviour patterns
  • Select and build appropriate recommendation model(s)
  • Design A/B testing framework to measure real impact
  • Integrate into your platform with business rule constraints
  • Monitor performance and iterate based on test results
FAQs

Frequently Asked Questions

We use hybrid recommendation approaches that combine content-based signals (product attributes, category) with behavioural data, so cold-start users and products still get reasonable recommendations from day one.

We build A/B testing directly into the rollout, comparing recommended experiences against a control group on real business metrics like conversion rate and average order value, not just click-through rate.

Yes, we integrate business rule constraints — such as excluding out-of-stock items or prioritising certain margins — into the recommendation logic rather than relying purely on the algorithmic output.

We integrate with e-commerce platforms, content and media apps, and custom-built products via API, and can also work within existing platforms like Shopify where a native integration exists.

Explore Recommendation Engines for Your Business

Book a free consultation to discuss a recommendation engine for your platform.

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