Every shopper sees a different store
One catalog. Five distinct strategies to tailor what each user sees. Delivered seamlessly across your channels of choice to keep product recommendations fresh, relevant, and automated
Pick a strategy. Pick a channel. Watch the store rearrange.
Emma Cole
New York · last viewed Marlow Jacket
Shopper profile used for this preview
Emma Cole
New York · last viewed Marlow Jacket
Three deliberate calls. Not three steps.
CATALOG
Your catalog, treated as live.
CSV for the bulk load, REST for everything after. Prices, stock, metadata reflect now not last night’s batch. The engine reads the latest state per request.
STRATEGIES
Five strategies, one slot.
From AI-driven Personalized to rule-based Collections. Run them in parallel, chain them with fallbacks the same slot serves the right logic to each shopper, not a one-size-fits-all rule.
DELIVERY
One config, every channel.
Author the recommendation once. Web Push, Email, In-app, WhatsApp, SMS same tokens, every surface. No per-channel rebuild when you launch a new campaign.
One slot. Five ways to fill it.
Each one reads a different signal. Set one as the primary for a slot, chain the others behind it, and the engine resolves which applies for every shopper at request time.
STRATEGY 1 · COLLECTIONS
Logic driven discovery
Use complex logic to build highly specific product groups like “Electronics under $400 with a 4+ star rating, in stock at user’s nearest fulfillment centre.”
Use it when
You're running a festive sale and want your own picks up front, minus whatever's already sold out.
STRATEGY 2 · AI PERSONALIZED
Behaviour-trained suggestions
If User A and User B share similar browsing patterns, the engine predicts the next logical item for User B based on User A’s successful conversion. No manual segments.
Use it when
The shopper has been here before and the shelf should show what shoppers like her purchase.
STRATEGY 3 · TEMPLATES
Relevant & frequently bought together
"Frequently bought together" and "customers also viewed", learned from what people actually put in the same basket, not a list someone wrote once and forgot. As buying habits change, so do the pairings.
Use it when
Someone's about to buy a camera. Show them the case and the spare battery before they check out.
STRATEGY 4 · TRENDING
Trending
A first-time visitor has no history to read, so the engine falls back to what's true for everyone such as top viewed, best-sellers across the catalog. By the time their second click lands, recommendation engine has signal to work with.
Use it when
An anonymous user arrives from an ad. You know nothing about them yet, so show what everyone's buying today.
STRATEGY 5 · FAIL SAFE
Fallback recommendations
Chain up to three strategies per slot. If the primary returns fewer products than the slot needs, the engine tops up from the next one down inside the same request.
Use it when
A brand-new product has nothing to pair with yet so shoppers see your editor's picks instead.
What can WebEngage Recommendations unlock for your role?
Find what converts, fix what doesn't
- Replacing static product grids with picks personalized to each visitor.
- See exactly which recommendations converted and which got ignored, with revenue attributed back to the picks.
- Serve bestsellers to new visitors and personalized picks to returning ones, automatically.
Find what converts, fix what doesn't
- Use one recommendation engine across email, push, WhatsApp, SMS et al. No parallel setup per channel.
- Show every recipient a different lineup in the same send, picked from their own browsing and purchase history.
- Put personalized product picks directly inside your emails and push campaigns, not just on the website.
Find what converts, fix what doesn't
- Promote an entire collection at once; the summer edit, the festive drop, without picking products one by one.
- Sold-out items drop out of recommendations the moment stock runs out, so you never push what you can't sell.
- Upload your own merchandising rules by CSV or API and serve them across every channel.
Additional Resources
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