Wat wij doen
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.
Geschikt voor
E-commerce, media, financial services, and B2B SaaS organisations wanting to personalise customer experiences and increase engagement
Veelvoorkomende toepassingen
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.
Privacy-First Personalisation
Implement personalisation with consent management, data minimisation, and the right to opt out.
Hoe wij werken
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.
Wat u ontvangt
- Recommendation model and serving API
- A/B testing framework for recommendation variants
- Offline evaluation report (precision@k, recall@k)
- Consent and data minimisation documentation
- Model retraining pipeline
- Source code ownership
Klaar om te beginnen?
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