Recommendation Engines
Show Every Customer Exactly What They Need Next
What we do
Recommendation systems drive 35% of Amazon's revenue and are increasingly standard across B2C and B2B digital experiences. We build custom recommendation engines — personalised product recommendations, content suggestions, and next-best-action systems — tailored to your customer base and integrated with your platform.
Ideal for
E-commerce, media, financial services, and B2B SaaS organisations wanting to personalise customer experiences and increase engagement
Common applications
E-Commerce Product Recommendations
"Customers also bought" and personalised homepage recommendations that increase basket size and return purchase rate.
Content Personalisation
Recommend relevant articles, documentation, or training content based on user behaviour and profile similarity.
Next-Best-Action for Financial Products
Recommend the next financial product (insurance, investment, savings) to customers based on life stage and behaviour signals.
B2B Cross-Sell Recommendations
Identify cross-sell opportunities for account managers based on purchase patterns and peer account comparisons.
Collaborative Filtering
Build collaborative filtering models that leverage collective user behaviour to personalise recommendations without needing explicit ratings.
GDPR-Compliant Personalisation
Implement personalisation with consent management, data minimisation, and the right to opt out — fully GDPR-compliant.
How we work
Interaction Data Assessment
Evaluate your clickstream, purchase, and engagement data quality and volume for recommendation modelling.
Algorithm Selection
Select the right approach: collaborative filtering, content-based, hybrid, or contextual bandit — based on your data and use case.
Build & Offline Evaluation
Train the recommendation model and evaluate offline with precision@k, recall@k, and novelty metrics.
Online Deployment & A/B Test
Deploy to your platform and run A/B tests to measure the business impact of recommendations before full rollout.
What you receive
- Recommendation model and serving API
- A/B testing framework for recommendation variants
- Offline evaluation report (precision@k, recall@k)
- GDPR consent and data minimisation documentation
- Model retraining pipeline
- Source code ownership
Ready to get started?
Let's discuss your requirements. No commitment, no sales pitch — just a focused conversation about your situation.
Book a free discovery call