Stop recommending bestsellers.
Bundle what moves your dead stock.
Most recommendation engines pair the cart with whatever's already selling. That's a confidence trick, bestsellers would sell anyway. MBA mines your real co-purchase patterns + scores by profit lift, so the recommendations move the inventory you actually need to move.
14-day money-back guarantee · Shopify · BigCommerce · WooCommerce · Magento · OroCommerce
Why most recommendation engines don't grow margin
"Customers also bought" pairs bestsellers
Black-box ML models learn that bestseller X and bestseller Y often appear together, because everyone buys them. Your engine recommends X with Y. Margin: unchanged. Bestsellers sell either way.
Profit-blind scoring
Most engines optimize for click-through or conversion rate. Both are vanity metrics if the click was on something that would have sold anyway. You want lift on the products that wouldn't have sold without the prompt.
No inventory awareness
The slow-movers sitting in your warehouse don't get surfaced because they don't have the volume signal. By the time the ML notices, you've already discounted to clear them.
Profit-aware scoring built into the engine
HUI (High-Utility Itemset Mining) engine
Pro tier ships an HUI engine that weights itemsets by profit contribution, not raw frequency. Bundles where the COMBINED margin justifies the recommendation surface, even if the individual items move slowly.
Inspectable rules, not a black box
Every rule has support / confidence / lift / profit-lift columns in the admin. If a recommendation looks wrong, you can see WHY it was generated, suppress it, or override it per opportunity.
Cost-aware scoring
Cost / scoring modes let you optimize for revenue lift, profit lift, or a hybrid. Pulls cost from your existing product attribute (WC) or sales_order_item.base_cost (Magento), no separate cost-of-goods config.
Operator overrides
Pin specific bundles you WANT surfaced (overstock, end-of-life SKUs, high-margin items). Suppress the ones you don't. The math gives you a baseline; the merchandiser gets the final say without retraining anything.
What you skip
- ✓Monthly SaaS that charges a revenue share on bestseller-recommendation impressions
- ✓Black-box ML models that can't tell you why a pair was suggested
- ✓Discounting overstock to clear when bundling could have moved it at full margin
- ✓Manually picking bundles by gut when your order data already knows the answer
- ✓Sending your order history to a third-party cloud to get a recommendation widget
Ready to turn your order data into revenue?
Install on your platform in under 10 minutes. Or book a consulting call and we'll do the launch for you.