Retail & E-commerce
Collaborative filtering and content-based models that surface relevant products — driving higher average order value and repeat purchase frequency across e-commerce and in-store channels.
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Industry overview
Product recommendation systems that combine collaborative filtering, content-based matching, and session-aware signals to surface the most relevant products for each shopper at each moment in their journey.
At a glance
Recommendation engines are one of the highest-ROI investments in retail technology — Amazon attributes approximately 35% of revenue to recommendations. But most out-of-the-box recommendation tools use basic collaborative filtering that performs poorly for new users, seasonal products, and long-tail catalogues. ArrayMatic builds recommendation systems tuned to the specific dynamics of each retailer's catalogue and customer base.
We develop hybrid recommendation models that combine collaborative filtering on purchase history, content-based matching on product attributes, and session-aware signals from current browse behaviour. Basket analysis identifies natural product pairings for cross-sell recommendations. Cold-start handling ensures new products and new users receive relevant recommendations from day one. A/B testing infrastructure measures the revenue uplift of recommendation variants — allowing continuous improvement without guesswork.
Key capabilities
Engagements are scoped to your business context — these are the core capabilities we bring to retail & e-commerce clients.
Hybrid collaborative and content-based recommendation models
Session-aware recommendations that adapt to current browse intent
Basket analysis and natural product pairing for cross-sell
Cold-start handling for new products and new customers
A/B testing framework with revenue uplift measurement
API integration with e-commerce platforms and mobile apps
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