Building an AI Product Recommendation Engine
Build a personalised recommendation system that increases conversion rates by 15-25% and average order value by 10-20%.
Transformation
Before & After AI
What this workflow looks like before and after transformation
Before
Product recommendations rely on simple rules: "customers who bought X also bought Y" or popularity-based lists. Personalisation is limited to basic segments (new vs. returning, geography). Recommendation click-through rate sits at 2-4%. The same recommendations are shown to very different customers, missing individual preferences.
After
AI learns individual customer preferences from browsing, purchase, and interaction data. Real-time personalisation adapts recommendations as customers browse. Contextual signals (time, device, season) further refine suggestions. Recommendation CTR increases to 8-15%, driving measurable revenue lift.
Implementation
Step-by-Step Guide
Follow these steps to implement this AI workflow
Collect & Prepare Interaction Data
3 weeksInstrument your platform to capture: product views, searches, add-to-cart, purchases, ratings, and dwell time. Build a unified customer profile combining behavioural data with demographic and transactional history. Address cold-start for new users and new products.
Build Recommendation Models
6 weeksImplement multiple recommendation approaches: collaborative filtering (users with similar behaviour), content-based filtering (similar product attributes), and hybrid models. Use deep learning embeddings for complex pattern recognition. Build A/B testing framework for model comparison.
Build Real-Time Serving Infrastructure
3 weeksDeploy recommendation API capable of sub-100ms response times. Implement feature store for real-time customer context. Build caching layer for frequently requested recommendations. Design fallback strategies for cold-start scenarios.
Integrate Across Touchpoints
3 weeksAdd recommendations to product pages, homepage, search results, email campaigns, and app push notifications. Design UI components that maximise recommendation visibility and click-through. Implement business rules (inventory, margin, promotion) as recommendation filters.
Optimise & Personalise
OngoingRun continuous A/B tests on recommendation algorithms, placement, and UI. Implement contextual bandits for real-time optimisation. Add diversity and serendipity controls to avoid filter bubbles. Track revenue attribution to recommendations.
Tools Required
Expected Outcomes
Increase recommendation click-through rate from 2-4% to 8-15%
Boost conversion rate by 15-25% on pages with recommendations
Increase average order value by 10-20%
Improve customer engagement metrics (session duration, pages/session)
Generate measurable incremental revenue attributable to recommendations
Solutions
Related Pertama Partners Solutions
Services that can help you implement this workflow
Frequently Asked Questions
Basic collaborative filtering needs at least 1,000 users with 5+ interactions each. Content-based recommendations can work immediately using product attributes. For new platforms, start with popularity and content-based approaches, then layer in collaborative filtering as interaction data accumulates. You'll see meaningful personalisation within 2-3 months of data collection.
For new users: start with popular items, trending products, and contextual signals (geography, device, referral source). As they interact, quickly learn preferences. For new products: use content-based similarity to existing products. Most modern recommendation systems handle cold-start gracefully through hybrid approaches.
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