Discover which products are bought together, identify cross-selling opportunities, and create data-driven bundle recommendations
From raw rows to high‑value rules and bundles.
Convert long or wide data into transactions and compute basket stats and item frequencies.
Run Apriori with your thresholds; compute support, confidence, lift, leverage, and conviction.
Filter by min_lift; prepare top rules by lift, confidence, and support for review.
Suggest bundle candidates from strong rules with expected uplift estimates.
Direct mapping to the analysis module’s result object.
Total transactions analyzed, unique items found, average basket size, number of association rules discovered, strong rules (lift > 2), confidence and lift averages, most frequent item statistics
Top rules sorted by lift/confidence/support with "if X then Y" patterns, frequent itemsets showing popular combinations, bundle recommendations with expected uplift percentages, category cross-sell patterns
Network graph showing item relationships, confidence vs lift scatter plots, category affinity matrix, lift distribution histograms for rule quality assessment
Apriori/FP‑Growth to mine itemsets efficiently with support, confidence, and lift metrics.
Min support/confidence/lift, item/category include/exclude, closed/maximal itemsets.
Bundles for cross‑sell, aisle adjacency, and network visualizations for stakeholder buy‑in.
Your data needs a transaction_column (order/basket ID) and item_column (product/SKU). Each row represents one item in a transaction.
Data formats supported: Long format (transaction-item pairs) or wide format (binary matrix with items as columns). The algorithm automatically handles both formats.
Minimum requirements: At least 1,000 transactions recommended, ideally 10,000+ for reliable patterns. More transactions improve rule quality.
Key parameters: Support (how frequent), confidence (how reliable), lift (how much better than random). Default thresholds are optimized for retail but adjustable.
From raw transactions to deployable bundles
Construct baskets, clean items, and one‑hot encode transactions; choose dimensions for slicing.
Run Apriori/FP‑Growth with thresholds; filter rules by lift/conviction and apply business constraints.
Create cross‑sell bundles and planograms, export lists, and validate with A/B tests or backtests.
Association rules reveal purchase affinities that power cross‑sell, promotions, and store layout—boosting basket size and conversion.
Focus on high‑lift, actionable rules and validate downstream impact. Network graphs and segment cuts make findings easy to communicate and deploy.
Note: Spurious rules can arise by chance; prefer lift/conviction and holdout validation, and test important bundles experimentally.
Mine association rules and deploy cross‑sell strategies
Read the article: Association Rules