ds@mcpanalytics.aiA data scientist you can email your data and question to, and get a reliable response. Not AI slop.A data scientist you can email. Not AI slop.
Upload a transaction log, map your order ID and item columns, and get the full market basket analysis — most-bought items, product pairs ranked by lift, and cross-sell recommendations. Free.
Free analyses run on up to 10,000 rows. Larger files are randomly sampled to that size, so sign up to analyze your full dataset.
Reconstructing baskets and mining product associations...
Sent to . Inside: most-bought items, product pairs ranked by lift, cross-sell recommendations, R code, and AI insights.
Analyze another fileThe practical guide behind this tool: when it applies, how to read the output, and the traps that make it say the wrong thing.
The analysis groups the item rows into baskets by order ID, counts how often each item and each pair of items appears, and scores every pair three ways: support (the share of orders containing both), confidence (of the orders with the first item, the share that also have the second), and lift (how many times more often the pair occurs than if the two items were bought independently). Pairs are filtered to a minimum support and ranked by lift, and the strongest directional rules become cross-sell recommendations.
Use it on any transaction log — retail orders, cart contents, menu tickets — when you want to know which items sell together for bundling, cross-sell, or layout.
Not for a single numeric outcome (use regression) or for testing two categorical columns for association (use the chi-square tool). It needs transactions in long format, not a wide one-row-per-order table.
Built for: Retail, e-commerce, and hospitality operators deciding what to bundle, cross-sell, or place together
Typical data source: A transaction or order-line export with an order ID and an item/product column
A transaction log in long format — one row per item in each order:
Minimum 10 rows · Best with 200-100,000 order lines across 20-40 distinct products
Standard-library analysis: which products are bought together? Give it a transaction log — one row per item in each order — and it mines the classic retail associations: the most-bought items, every product pair ranked by lift (how much more often they co-occur than chance), and the directional cross-sell rules (buy A, recommend B) with their confidence. Built for cross-sell, bundling, and store or menu layout. Works on any dataset: map an order/basket ID column and an item column.
The items that appear in the most orders — the popularity baseline the pairings are judged against.
Every strong product pair ranked by lift, with support and confidence — a lift above 1 means they sell together more than chance.
Buy-this-recommend-that rules ranked by confidence — the shortlist for a 'frequently bought together' prompt or a bundle.
Plain-English interpretation of what the numbers mean, what's significant, and what to do next.
Which products should I bundle or cross-sell?
Map your order ID and item columns. You get the most-bought items, every product pair ranked by lift, and directional cross-sell rules — buy this, recommend that — so bundling and 'frequently bought together' prompts rest on what shoppers actually do.
See our FAQ for details on pricing, data privacy, and how the analysis works. Every report includes a Methodology section showing the statistical test, assumptions checked, and diagnostics run.
Run any analysis on your own data: R analyses, interactive reports, AI insights, and PDF export.
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CympleData Scientist Send me your data and question, I’ll send you the analytics. ds@mcpanalytics.ai
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