MBA vs Super Recommendations
A BigCommerce-only widget app that places attribute-based and manual product recommendations across the home, product, collection, cart, and thank-you pages.
When MBA wins
If you want recommendations grounded in actual co-purchase evidence (support/confidence/lift) rather than catalog attributes, real-SKU bundles with discount formats and A/B testing, or the same engine running across BigCommerce plus Shopify, WooCommerce, Magento, and OroCommerce with an API and MCP server, MarketBasketAnalysis wins.
When Super Recommendations wins
If you run a single BigCommerce storefront and just want simple, set-it-and-forget-it recommendation widgets driven by product type, brand, keyword, top-sellers, or hand-picked picks across all the standard pages, Super Recommendations is a focused, native fit with nothing extra to learn.
Feature comparison
Color-striped rows favor MBA, Super Recommendations, or are even. We mark each so you can scan for the trade-offs that matter to you.
| Feature | MBA | Super Recommendations |
|---|---|---|
| Recommendation method | Explainable co-purchase mining across six engines (sql_pairs, FP-Growth default, seasonal_cohort, return_aware, profit-aware HUI, ai_catalog cold-start), plus a co_occurrence fallback emitting support/confidence/lift on real order history. | Attribute-based automatic logic (same/related product type, same brand, same/related keywords, top-selling, new arrivals) plus manual hand-picked recommendations. |
| Explainability of recommendations | Every rule is inspectable in-admin with its support, confidence, and lift, so merchants can see why two products are paired. | Recommendations are driven by catalog attributes or manual selection; no per-pair statistical rationale is exposed. |
| Placement across storefront pages | Recommendations and bundles render on product, cart drawer, and post-purchase surfaces, plus Klaviyo/email recs. | Configurable widgets on home, product, collection, cart, and thank-you pages, with freely chosen widget locations. |
| Bundles and offer formats | Real-SKU bundles with BYOB, volume, BOGO, and virtual formats, substitutions, and post-purchase upsell. | Recommendation widgets only; no bundle builder or discounted bundle/offer formats surfaced. |
| A/B testing and measurement | Built-in A/B testing with lift and confidence-interval reporting on recommendation performance. | No native experimentation or lift/CI measurement is described. |
| Platform coverage | One API contract serving identical intelligence across Shopify, BigCommerce, WooCommerce, Magento, and OroCommerce. | BigCommerce only, and may not be fully compatible with multi-storefront. |
| API / agent surface | Public REST API, a 19-tool MCP server (18 off Shopify), and ACP/agentic-commerce endpoints for programmatic and AI-agent access. | Dashboard-configured widgets; no public API, MCP, or agentic-commerce surface advertised. |
| BigCommerce-native simplicity and onboarding | MBA-hosted BigCommerce app (Fly.io, the same architecture as our Shopify app), but a broader feature set with mining engines and offer formats means a steeper initial setup. | Single-purpose, native BigCommerce app with a simple dashboard and minimal configuration to get widgets live. |
| Data privacy / where it runs | On BigCommerce, MBA is an MBA-hosted app (Fly.io) running mining on per-store-isolated infrastructure, the same model as our Shopify app; order data is never shared between stores, and AI is bring-your-own-key direct to your provider. The first-party, on-store model applies to our Magento and WooCommerce installs. | Native BigCommerce app; specific data-handling and on-store vs hosted processing details are not publicly documented. |
Pricing snapshot
MarketBasketAnalysis
Flat, transparent plan pricing: Free ($0) and Plus $99/mo flat (or ~$79/mo on annual). One paid plan, everything in it. These are product plans, not per-platform prices, and the same plans apply across every platform. Metered on capability and compute, never on GMV. No revenue share, no add-ons.
See plansSuper Recommendations
Pricing is not publicly surfaced in search results and is shown on the BigCommerce app listing itself (typical for this class of single-vendor BigCommerce widget app, usually a low flat monthly fee with a free trial); confirm the current plan on the listing. By contrast MarketBasketAnalysis is flat and transparent: a free $0 tier, then Plus $99/mo (or ~$79/mo on annual), with no GMV or revenue-share component.
Visit Super RecommendationsThe honest verdict
Super Recommendations is a clean, BigCommerce-native widget app: if you want attribute-based or manual recommendations on the standard storefront pages with almost no setup, it does that job well.
Its recommendations come from catalog attributes (type, brand, keyword, top-sellers) or manual picks, not from mined co-purchase evidence, so there is no support/confidence/lift or in-admin rationale behind each pairing.
It is single-platform (BigCommerce, with possible multi-storefront limits) and has no bundle/offer formats, A/B testing, or API/MCP/agent surface, where MarketBasketAnalysis is built around all of those.
MarketBasketAnalysis is the stronger pick for merchants who want explainable co-purchase intelligence, real-SKU bundles and post-purchase upsell, experimentation, and the same engine across five storefronts on one API.
Public pricing for Super Recommendations was not visible in search and lives on the BigCommerce listing; MarketBasketAnalysis is flat and transparent (free $0 tier, Plus $99/mo, no GMV or revenue-share).
Want the full buyer's deep-dive?
This page is the per-vendor short version. For the long version, read “MBA vs Bloomreach” (8 pages on the enterprise-vs-mid-market trade-off) or “MBA vs the widget cluster” (4 pages of pricing math vs Glood, Rebuy, and PickyStory).
Try MBA on your own store
14-day money-back guarantee on all paid platforms. WP.org free tier on WooCommerce. No credit card required for the free path.