AI can analyze which products are suitable for bundle sales.

Key methods include co-occurrence analysis across transactions and association rule mining. Analysis requires sufficient historical sales data covering diverse customer behaviors. Factors like complementary product functions, shared customer segments, and margin compatibility are evaluated. Regular updates account for changing buying trends.

Implementation involves collecting detailed sales transaction records, applying algorithms like FP-Growth or Apriori to uncover high-confidence associations. Product pairs are then scored based on lift and support metrics. Suitable bundles undergo validation through A/B testing before full launch. This approach optimizes inventory turnover and enhances average order value.