MBA vs WooCommerce Product Recommendations
The official Automattic-built WooCommerce extension for smart, data-driven upsells and cross-sells using rule-based engines plus automated Frequently Bought Together recommendations.
When MBA wins
If you need explainable co-purchase math (support/confidence/lift) you can inspect, the same recommendations served across multiple storefronts on one API, agent/MCP access, or B2B-aware mining on OroCommerce, MarketBasketAnalysis is the better fit.
When WooCommerce Product Recommendations wins
If you run a single WooCommerce store and want the first-party, Automattic-maintained extension with guaranteed core compatibility, a polished merchant UI, 20+ built-in placement slots, and rule-based engines you can tune with filters and amplifiers, it is the safest native choice.
Feature comparison
Color-striped rows favor MBA, WooCommerce Product Recommendations, or are even. We mark each so you can scan for the trade-offs that matter to you.
| Feature | MBA | WooCommerce Product Recommendations |
|---|---|---|
| Automated Frequently Bought Together / co-purchase mining | Six mining engines (sql_pairs, fp_growth default, seasonal_cohort, return_aware, profit-aware HUI, ai_catalog cold-start), plus a co_occurrence fallback producing real association rules. | Lightweight automated Frequently Bought Together algorithm that analyzes order history with near-zero training. |
| Explainable metrics (support, confidence, lift) inspectable in admin | Every rule emits support, confidence, and lift and is inspectable in the admin so you can see why a pairing was chosen. | Surfaces revenue and conversion analytics per placement, but does not expose underlying support/confidence/lift rule metrics. |
| Rule-based / manual recommendation engines (filters, amplifiers, conditions) | Supports real-SKU bundles, substitutions, and conditions, though the visual filter/amplifier engine builder is less mature than Woo's. | Mature rule-based engine builder with category/attribute/tag/price filters, amplifiers (popularity, rating, conversion rate), and visibility conditions. |
| Built-in placement slots and native WooCommerce theme integration | Integrates with WooCommerce but offers fewer turnkey placement slots and relies on its own widgets/API rather than deep native theme hooks. | 20+ ready-made placements (shop, category, product, cart, checkout, thank-you) built by Automattic, though not yet optimized for block themes. |
| Multi-platform support on one API contract | Same intelligence across Shopify, BigCommerce, WooCommerce, Magento, and OroCommerce on one shared API contract. | WooCommerce only; no support for other storefronts. |
| Agent-native access (REST API + MCP + agentic commerce) | Public REST API, a 19-tool MCP server (18 off Shopify), and ACP/agentic-commerce endpoints. | No public recommendation API or MCP server; configured and consumed through the WooCommerce admin and store front end. |
| Revenue and conversion reporting per placement | Reports rule performance and supports A/B testing of recommendation strategies. | Detailed revenue and conversion analytics filterable by date, converted product, and location. |
| A/B testing of recommendation strategies | Built-in A/B testing to compare recommendation strategies and placements. | No documented built-in A/B testing; optimization is done by reading analytics and editing engines manually. |
| B2B readiness | B2B-aware mining on OroCommerce (per-organization context, cross-store insights), reorder-prediction data through the REST API, and a recommendation API for building custom B2B flows. | Focused on B2C upsell/cross-sell; no dedicated RFQ, quote-bundle, or reorder-prediction features. |
| Privacy / where data is processed | Mining runs locally inside the merchant's WooCommerce install; AI is bring-your-own Anthropic or OpenAI key. | Recommendation algorithm runs locally within the WooCommerce/WordPress install, keeping order data on the store. |
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 plansWooCommerce Product Recommendations
Flat annual subscription, roughly $99/year for a single site (about $158 for two years), no GMV or revenue share. WooCommerce only.
Visit WooCommerce Product RecommendationsThe honest verdict
This is the official, Automattic-maintained extension, so for a single WooCommerce store it is the lowest-risk pick for compatibility and polish, and its rule-engine builder (filters, amplifiers, conditions) and 20+ native placements are genuinely better turnkey than ours.
Both products keep order data on-store and process recommendations locally, so on the core privacy story they are roughly even; the difference is our optional bring-your-own AI key versus their purely local algorithm.
If you ever plan to sell on more than just Woo, MarketBasketAnalysis runs the same engine across Shopify, BigCommerce, Magento, and OroCommerce on one API, while their extension is WooCommerce-only.
Our edge is explainability and programmatic access: you can inspect actual support/confidence/lift rules, hit a REST API or 19-tool MCP server (18 off Shopify), run A/B tests, and use B2B-aware mining on OroCommerce that the Woo extension does not offer.
Honest take: if you just want clean native cross-sells on one WooCommerce store and never need an API or B2B, their extension is hard to beat at ~$99/year; choose us when explainability, agents, multi-store, or B2B matter.
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.