E-commerce & Digital
Personalised product recommendations
Surface relevant next-best products based on browse history, purchase history, and customer segment. Displayed on product pages, basket, and post-purchase confirmation.
5–12%
uplift in units per order
Opportunity assessment
Moderate impact. Meaningful cost or revenue improvement, likely noticeable at function level but modest relative to total business scale.
Moderate effort. Requires configuration, prompt engineering, and testing. A capable team can get there but expect several months.
Demanding data requirements. Relies on clean, integrated data across multiple systems. Significant data preparation work typically needed.
Low risk. Limited external exposure. A human reviews output before it reaches anyone outside the team.
Moderate people impact. Part of someone's working day changes. Requires training and some adjustment time, but roles remain broadly the same.
Tooling required
Things to consider
Amazon's recommendation engine drives an estimated 35% of total revenue. The system uses collaborative filtering and content-based filtering across homepage, product pages, checkout, and email (McKinsey).
Requires a sufficient volume of interaction data (browse, purchase, add-to-basket) to generate meaningful recommendations. Businesses with fewer than 10,000 active customers per month may not have enough signal.
Specialist recommendation engines (Algolia, Klevu, Nosto, Dynamic Yield) exist for this use case and are usually faster to value than custom builds. Evaluate before deciding to build.
Recommendation placement matters as much as recommendation quality. Test placements (product page vs. basket vs. post-purchase) before optimising algorithm performance.
A common failure mode is 'you might also like' recommendations that are completely obvious — matching socks with trainers when the customer is already buying socks. Focus on surfacing non-obvious but highly relevant items that the customer might not have found independently.
Experiment starter: If you have any recommendation capability already live (even manual 'frequently bought together' rules), A/B test it against a collaborative filtering model built on your purchase history data. Measure click-through rate on recommendations and units per transaction. A measurable uplift in either metric justifies investing in a more sophisticated engine.
Go deeper in the playbook
Section — Opportunity Identification, including the AI Opportunity Assessment ScorecardSection — The Tooling LandscapeThis is an automation opportunity — see the relevant playbook section for how to approach it.More in E-commerce & Digital
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AI Transformation Playbook
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