An AI engine embedded in a CRM or e-commerce platform that analyzes a customer's purchase history and behavior to recommend the most relevant upsell or cross-sell products at the point of sale or during follow-up communications.
Most online stores show generic 'you may also like' items or hand-picked bundles that were set up once and never updated. Customers buying a camera body are shown unrelated accessories, while the lens most buyers actually pair with it goes unmentioned. Sales teams in B2B face the same problem: they rarely know which additional products similar customers went on to buy, so upsell conversations depend on memory.
AI product recommendations learn from real purchase patterns. By analysing which products are bought together, in what sequence, and by which kinds of customers, the engine suggests relevant add-ons at the product page, in the cart, in post-purchase emails or inside the CRM for sales reps. Suggestions update as behaviour changes, and business rules keep them sensible, for example excluding out-of-stock items or respecting compatibility. The aim is helpful suggestions that customers actually want, not pushy upselling.
Order history, product catalogue data and browsing events are pulled from the e-commerce platform or CRM, such as Shopify or Salesforce.
Collaborative filtering and co-purchase analysis identify products that similar customers tend to buy together or next.
Business rules filter results for stock availability, margin, compatibility and items the customer already owns.
Recommendations are served through widgets on product and cart pages, in email flows, or as prompts in the CRM for account managers.
Click and purchase data flow back to the engine, and controlled tests compare recommendation strategies against a baseline.
During checkout, an e-commerce site's AI recommends a specific camera lens to a customer buying a camera body, based on what similar customers have bought together.
Recommendation Engines, Collaborative Filtering, CRM/E-commerce platform APIs (Shopify, Salesforce)
Purchase history and browsing data linked to identifiable customers is personal information under the Privacy Act 1988. Your privacy policy should explain that you use it to personalise recommendations, and APP 7 limits how it can be used for direct marketing. Recommendation emails are commercial electronic messages under the Spam Act 2003, so they need consent, sender identification and a working unsubscribe. The Australian Consumer Law applies to any claims shown alongside recommendations, such as 'most popular' labels or compatibility statements, which must be accurate. Avoid pressure tactics like false scarcity messages.
This solution requires careful evaluation of cost-benefit ratio. Consider implementation only after thorough analysis and risk assessment.
There is no fixed threshold, but the engine needs repeated purchase patterns across the products you want to recommend. Stores with a modest catalogue and steady order volume usually have enough. Very large catalogues with sparse sales per item may need category-level recommendations or attribute-based similarity until more history builds up.
Yes. In a B2B setting the engine surfaces likely next purchases inside the CRM, so account managers see which products comparable customers added and when. The same analysis can feed reorder reminders or quarterly review packs. Human reps decide whether a suggestion suits the customer relationship.
Time goes into cleaning product and order data, building compatibility and stock rules, integrating widgets or CRM prompts, and running a controlled test to confirm the recommendations add real sales. Many platforms include basic recommendations out of the box; a custom engine is worthwhile when those feel too generic.
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