Building a Recommendation Engine for Retail & E-Commerce
A proposed recommendation engine combines catalogue intelligence, machine learning, and conversational AI to serve personalized storefront recommendations and interactive shopping experiences.
Business Outcomes
Business Outcomes
<200 ms
Target p95 response time for storefront recommendations
~1–3 sec
Target response time for streamed conversations
5–10%
Customer holdout to measure recommendation impactAbout the Client
The client operates a multi-tenant retail and e-commerce platform. Its storefront needed relevant recommendations at scale, while a new conversational experience needed to answer questions and help shoppers explore products, all using consistent ranking, stock, and customer-experience rules.Retail
The Challenge
Manual merchandising made it difficult to rank products for each customer’s interests and shopping context.
The Challenge
Manual merchandising made it difficult to rank products for each customer’s interests and shopping context.
Cold Start for New Merchants and Products
New merchants had no purchase history, and new products could not rely on behavioral data to become discoverable.Speed and Rich Conversations
Storefront recommendations needed to respond in milliseconds, while conversational shopping required more time for planning and explanations.No Reliable Measure of Incrementality
Without a customer holdout, changes in revenue could not be attributed to recommendations rather than promotions, seasonality, or other factors.Cold Start for New Merchants and Products
New merchants had no purchase history, and new products could not rely on behavioral data to become discoverable.Speed and Rich Conversations
Storefront recommendations needed to respond in milliseconds, while conversational shopping required more time for planning and explanations.No Reliable Measure of Incrementality
Without a customer holdout, changes in revenue could not be attributed to recommendations rather than promotions, seasonality, or other factors.Cold Start for New Merchants and Products
New merchants had no purchase history, and new products could not rely on behavioral data to become discoverable.Speed and Rich Conversations
Storefront recommendations needed to respond in milliseconds, while conversational shopping required more time for planning and explanations.No Reliable Measure of Incrementality
Without a customer holdout, changes in revenue could not be attributed to recommendations rather than promotions, seasonality, or other factors.Cold Start for New Merchants and Products
New merchants had no purchase history, and new products could not rely on behavioral data to become discoverable.Speed and Rich Conversations
Storefront recommendations needed to respond in milliseconds, while conversational shopping required more time for planning and explanations.No Reliable Measure of Incrementality
Without a customer holdout, changes in revenue could not be attributed to recommendations rather than promotions, seasonality, or other factors.The Solution
A low-latency path retrieves candidates in parallel using product similarity, customer behavior, basket patterns, and replenishment signals. It applies stock and business filters, ranks the results, and uses fallback recommendations to keep slots populated.
The Solution
A low-latency path retrieves candidates in parallel using product similarity, customer behavior, basket patterns, and replenishment signals. It applies stock and business filters, ranks the results, and uses fallback recommendations to keep slots populated.
Fast Storefront Recommendations
A low-latency path retrieves candidates in parallel using product similarity, customer behavior, basket patterns, and replenishment signals. It applies stock and business filters, ranks the results, and uses fallback recommendations to keep slots populated.Conversational Shopping
A streamed conversational path interprets shopper intent, keeps session context, and uses scoped tools for catalogue search, recommendations, baskets, stock, substitutes, and cart actions.Catalogue Intelligence
Language and vision models extract product attributes, organize categories, group variants, and identify similar, complementary, and substitute products. This helps new merchants and new products receive relevant recommendations before they have accumulated purchase history.Machine learning
|Language and vision models
|Product embeddings
|Vector search
|Collaborative filtering
|Gradient-boosted ranking
|Event tracking
|Cloud data lakehouse
Impact and Result
Products are ranked for the shopper and placement, supporting recommendations across product pages, carts, and search.
Impact and Result
Products are ranked for the shopper and placement, supporting recommendations across product pages, carts, and search.
A Consistent Experience Across Storefront and Chat
Both request paths use the same ranking system and business controls.Recommendations from Day One
Catalogue-based signals allow new merchants and products to be recommended before behavioral history is available.Results That Can Be Measured
The holdout design supports measurement of incremental revenue, basket size, attach rate, click-through, repeat purchases, and return visits.A Consistent Experience Across Storefront and Chat
Both request paths use the same ranking system and business controls.Recommendations from Day One
Catalogue-based signals allow new merchants and products to be recommended before behavioral history is available.Results That Can Be Measured
The holdout design supports measurement of incremental revenue, basket size, attach rate, click-through, repeat purchases, and return visits.A Consistent Experience Across Storefront and Chat
Both request paths use the same ranking system and business controls.Recommendations from Day One
Catalogue-based signals allow new merchants and products to be recommended before behavioral history is available.Results That Can Be Measured
The holdout design supports measurement of incremental revenue, basket size, attach rate, click-through, repeat purchases, and return visits.Final Outcome
The proposal defines a phased recommendation engine that pairs fast machine-learning rankings with a conversational shopping experience. It starts with catalogue and data foundations, then adds basket and session recommendations, personalization, and later optimization. The proposed targets include sub-200 ms storefront responses, streamed conversations in approximately 1–3 seconds, and an 8% SKU-level upsell.
Final Outcome
The proposal defines a phased recommendation engine that pairs fast machine-learning rankings with a conversational shopping experience. It starts with catalogue and data foundations, then adds basket and session recommendations, personalization, and later optimization. The proposed targets include sub-200 ms storefront responses, streamed conversations in approximately 1–3 seconds, and an 8% SKU-level upsell.
Ready to Build a Recommendation Engine for Your Retail or E-Commerce Platform?
If generic product suggestions and slow storefront experiences are limiting discovery, a well-designed recommendation engine can help. We design recommendation systems that combine fast product rankings, conversational shopping, and measurement built in from the start.
Ready to Build a Recommendation Engine for Your Retail or E-Commerce Platform?
If generic product suggestions and slow storefront experiences are limiting discovery, a well-designed recommendation engine can help. We design recommendation systems that combine fast product rankings, conversational shopping, and measurement built in from the start.
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