StoBasket: Joint Upsell and Cross-Sell Optimization for Contribution Margin

Key points

• Retail recommenders rank relevance, but retailers earn contribution margin: an upsell earns the margin difference over the item it replaces, whereas a cross-sell earns its full margin.1
• In real-data-calibrated simulations on five public basket datasets, StoBasket captures essentially all attainable offer margin: within 0.5% in every default-price setting and within 0.7% when the true response lies outside its model family.1
• Correct economic accounting is the broadest gain: margin-weighted popularity leaves up to 11% of attainable margin unrealized, and a published next-basket recommender leaves 11-52%.1
• The same estimates yield a decision-aware market-basket analysis whose cost does not grow with association order.1

Sources (1)

• [1]StoBasket: Joint Upsell and Cross-Sell Optimization for Contribution Margin
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 16, 12:19 AM

  We introduce joint stochastic basket editing, a formulation in which one policy selects replacement and addition offers for an existing basket, values them by expected incremental contribution margin, and couples them through displayed-offer, accepted-offer, and joint-acceptance response channels.

  Retail recommenders rank relevance, but retailers earn contribution margin: an upsell earns the margin difference over the item it replaces, whereas a cross-sell earns its full margin.

Extractive summary: sentences quoted from the sources.