copurchases

Copurchases are instances in which multiple products are bought together within the same transaction. In retail analytics, copurchase patterns reveal relationships among products and are used to support cross-selling, product placement, promotions, and inventory planning. Copurchase data are typically derived from point-of-sale or online transaction logs and can be analyzed at the item level or for combinations of items.

Common measures describe how strongly items are associated. Support measures how frequently a set of items occurs in transactions; confidence measures the probability of purchasing an item given another is purchased; lift compares observed co-purchase frequency to what would be expected if the items were independent. Jaccard similarity and other metrics are also used. Co-purchase information can be represented in a co-purchase matrix or a network with items as nodes and edge weights reflecting co-purchase strength.

Analytical methods include market basket analysis and association rule mining, including Apriori and FP-growth algorithms. More sophisticated approaches use Bayesian networks or graph-based models to capture dependencies and conditional probabilities.

Applications include cross-selling, bundle creation, personalized recommendations, shelf-space optimization, pricing strategies, and inventory management. In online retail, copurchase signals power product recommendations such as customers who bought this item also bought, and automatic bundling.

Limitations include that copurchases reflect historical behavior and may be driven by popularity or seasonality rather than true affinity; they do not imply causation. Data quality and privacy concerns, sparsity for long-tail items, and cold-start problems for new products can challenge analysis. Interpreting measures such as lift requires contextual understanding of market, season, and promotions.