Use AI product recommendations where manual curation can’t keep up: automated emails that go out around the clock, stores with more products than you can hand-pair, and customers whose interests you can’t track one by one. Practically, that means letting an algorithm choose the products inside your post-purchase, abandoned cart, browse abandonment, and win-back emails […]
Category Archives: Personalization and Product Recommendations
The product a customer is most likely to buy next is found by working down a priority stack of signals, from strongest to weakest: something they’ve viewed repeatedly but not bought, then a complement to what they already own, then a repeat of a consumable that’s due, then the top product in their preferred category, […]
To recommend complementary products after purchase, wait until the order has actually arrived, then send an email built around the product they bought: accessories that improve it, consumables it needs, or the natural next item in how they’ll use it. The recommendation rides along with genuinely useful post-purchase content — care tips, how-to guidance — […]
Personalize subject lines with one signal at a time — a first name, a category they browse, a product they left behind — and only when the email body actually delivers on it. That’s the whole rule. Overdoing it looks like this: a name in every subject, tokens that break into “Hi ,”, or specificity […]
You personalize an offer without a discount by changing what you offer, when you offer it, and how you frame it — instead of changing the price. The four levers that work: show the specific products a customer is most likely to want next, time the message to their buying rhythm, wrap the offer in […]
With a large catalog, personalization stops being a nice-to-have and becomes the only way to market at all — you physically cannot write campaigns for 5,000 SKUs by hand. The fix is a change of unit: stop marketing products and start marketing to affinities. Clean your category and attribute data, group customers by the categories […]
Personalizing emails for returning customers comes down to using three things you already hold: what they bought, what they’ve browsed since, and how much they’re worth to the store. In practice that means recommendation blocks that build on past purchases instead of repeating them, send timing that respects their buying rhythm, and a different depth […]
You can personalize emails to first-time visitors even though you know almost nothing about them — because “almost nothing” isn’t nothing. A first-time visitor who joins your list gives you three usable signals: where the signup happened (which page, which popup), what they browsed before and after subscribing, and how they arrived (an ad for […]
Personalization stops feeling creepy when the email talks about what the customer did with your store, not what your store knows about the customer. “Still thinking about the walnut desk?” is helpful. “We saw you looking at the walnut desk at 11:42 last night” is surveillance. The working rule: use behavioral data to decide what […]
You measure the revenue impact of personalization by comparison, not by reporting. The dashboard number that says “personalized emails generated €4,300 last month” answers the wrong question — some of that money would have arrived anyway. The right question is incremental: how much more did the personalized version earn than a non-personalized version sent to […]