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Help with post purchase product recommendations
I am in contact with a consumer brand with a rapidly expanding catalog looking for a tool that can learn in real-time as they launch new categories and products and help decide what products to recommend next. Their historical repurchase patterns are becoming…
I am in contact with a consumer brand with a rapidly expanding catalog looking for a tool that can learn in real-time as they launch new categories and products and help decide what products to recommend next. Their historical repurchase patterns are becoming obsolete. Has anyone found a good solution for this?
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The real problem is that a fast-expanding catalog means any recommendation engine is always catching up to inventory that didn't exist last month. What worked for clients I've seen in similar situations is leaning on a category-level behavior rather than a product-level one, at least while the catalog is still settling. Recommending from "you bought in this category, here's what others explored next" holds up better than SKU-to-SKU patterns that go stale fast.
Any tools you would recommend that do this well?
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For the email/SMS side of post-purchase, Omnisend handled this perfectly. The product recommendation blocks pull from your live Shopify catalog automatically, so when the brand adds a new category, it's already available in the flow without manual updates. You can easily surface bestsellers by category, recently viewed, or cross-sells, which fits the category-level logic you'd want for a catalog that's still settling.
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For a fast expanding catalog where historical data goes stale quickly, you generally want a real-time collaborative filtering or content-based recommendation engine rather than something relying purely on past purchase history. Tools like Nosto, Klaviyo's recommendation blocks, or Rebuy (if they're on Shopify) handle new product cold-start reasonably well by blending browsing behavior and product attributes instead of just repurchase patterns. Worth checking if their current stack already has one of these available before building something custom.
This is a classic problem we see in many Shopify stores - they setup product recommendations only for it to become obsolete. The best option is to go for an AI recommendation engine, or a rules based recommendation engine that will recommend from your in-stock products. You can use Shopify's own product recommendation engine if you want to cross sell similar products. Or if you want to recommend complementary products, Selleasy, and Rebuy give you such options.
The hardest part is that historical purchase data gets less useful when the catalog changes fast. I'd lean toward mixing purchase history with browsing behavior and product attributes instead of relying only on "people also bought." Otherwise every new product has the cold start problem. It doesn't have to be perfect on day one, it just has to learn quickly.
worth asking if the repurchase data is actually obsolete or just thin on the new categories. a lot of brands in this spot get more out of curated rules per collection than a model, at least until the new stuff has enough volume to learn from.
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