The deployment is straightforward; the discipline of matching strategy to placement and measuring conversion is where teams either win or fail. Spend the first week getting the placement decisions right and the next month tuning based on what shoppers actually do.
- Confirm Commerce Cloud Einstein is enabled
Business Manager, Administration, Site Development, Einstein Configuration. Without Einstein enabled, the recommender carousels do not work. Most editions include the bundle.
- Pick the placements for recommendation carousels
Product detail, cart, category landing, homepage, order confirmation. Most storefronts have all five active. Decide before you write the strategy assignment.
- Match strategy to placement deliberately
Customers Also Bought on PDP. Complete the Set on cart. Top Sellers on category landing. Recommended for You on homepage. Customers Also Bought on order confirmation. Document the rationale per placement.
- Add the recommender components to your storefront templates
The standard storefront templates ship with recommender components. Drop one per placement and pick the strategy in the component config. No code changes needed.
- Configure business rules per placement
Exclude out-of-stock, exclude restricted products, exclude recently purchased on PDP and cart. Pin merchandiser items where strategic. Document the rules so the merchandising team knows what the algorithm will and will not surface.
- Measure for 30 days then run one A/B test
Let the carousels run for 30 days to gather baseline data. Then pick the highest-traffic placement and A/B test an alternative strategy or rule set. Repeat quarterly.
- Build a monthly review with merchandising
Pull click-through and add-to-cart conversion per placement monthly. Review with merchandising. Adjust strategies on placements that underperform; the iteration loop is what sustains lift.
Customers Also Bought, Recently Viewed, Top Sellers, Complete the Set, Recommended for You. Per placement; not site-wide.
Out-of-stock filter, pinned items, restricted product filter, recently-purchased filter, sale boost. Per placement.
Which storefront page the carousel renders on (PDP, cart, category, homepage, order confirmation).
Which placement is currently under A/B test, with which two strategies or rule sets. Drives the conversion data the next decision is based on.
How often the underlying model retrains. Hourly for behavior-driven, daily for catalog-driven; tunable in advanced configurations.
- The same strategy on different placements converts differently. Picking one strategy site-wide leaves significant lift on the table.
- New storefronts and new products have a cold-start problem. Plan for Top Sellers as the dominant strategy until shopper behavior data accumulates.
- Business rules sit on the placement, not the strategy. The same Customers Also Bought can apply different rules on PDP vs cart; configure deliberately.
- Without A/B testing, strategy decisions become opinion battles. Run one A/B test per quarter on the highest-traffic placement to keep decisions data-driven.
- Recommender carousels without monitoring quietly underperform. The monthly merchandising review is the discipline that sustains the lift.