arXiv:2209.04942v2 Announce Type: replace-cross Abstract: Problem definition: This paper studies the problem of estimating consumer preferences from bundle sales data. Product bundling is a widely used pricing strategy in retail markets. To set profitable bundle selection and prices, the seller needs to learn the distribution of consumers' valuations for individual products from the transaction data. When customers purchase bundles or multiple products, classical methods such as discrete choice models cannot be used to estimate consumers' valuations. In this paper, we propose an approach to le
The proliferation of AI and advanced analytics allows for more sophisticated processing of complex commerce data, moving beyond traditional statistical models.
Accurate consumer preference estimation is crucial for retail strategy, impacting pricing, inventory, and personalized marketing across various sectors.
The ability to infer consumer preferences from complex bundle sales data, where classical methods fail, provides a more granular and actionable understanding of purchasing behavior.
- · Retailers
- · E-commerce platforms
- · AI/ML solution providers
- · Marketing analytics firms
- · Businesses relying on outdated discrete choice models
- · Inefficient pricing strategies
Improved profitability and reduced waste for retailers due to optimized bundling and pricing strategies.
Increased consumer satisfaction from more relevant product offerings and personalized recommendations.
Potential for new business models centered around dynamic, highly individualized product and service bundles.
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