NDLI: Real-time optimization of personalized assortments

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Real-time optimization of personalized assortments

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Real-time optimization of personalized assortments

Content Provider	ACM Digital Library
Author	Golrezaei, Negin Nazerzadeh, Hamid Rusmevichientong, Paat
Abstract	Motivated by the availability of real-time data on customer characteristics, we consider the problem of personalizing the assortment of products to each arriving customer. For an arriving customer of type z, the company must decide, in real-time, on the assortment of products to offer. Given the offered assortment, the customers make choices on which products to buy, if any, according to a general choice model that is specific to each customer type. Our goal is to develop a revenue-maximizing policy that determines the assortment to offer to each arriving customer, taking into account the customer type and the current inventories. We propose a family of simple and effective algorithms, called Inventory-Balancing, for real-time personalized assortment optimization. Each Inventory-Balancing algorithm is characterized by the penalty function that discounts the marginal revenue of each product, as the inventory level reduces. By adjusting the revenue of each product according to its remaining inventory, the algorithms hedges against the uncertainty in the types of future customers, by reducing the rate at which products with low inventory are offered. Thus, Inventory-Balancing serves as a simple mechanism that coordinates the front-end customer-facing decision with the back-end supply chain constraints. In particular, we prove that Inventory-Balancing algorithms with a strictly concave penalty function always obtain more than 50% of the optimal revenue. We also provide an Inventory-Balancing algorithm that obtains at least 1-1/e ≈ 63% of the benchmark revenue. The 63% ratio is optimal in the sense that no other deterministic or stochastic policies can achieve a higher value. In our numerical experiments, our algorithms perform even better than what is predicted by the worst-case bound, and they obtain revenues that are within 94% of the optimal. Through actual sales data from an online retailer, we also demonstrate that personalization based on each customer's location can lead to over 10% improvements in revenue, compared to a policy that treats all customers the same.
Starting Page	561
Ending Page	562
Page Count	2
File Format	PDF
ISBN	9781450319621
DOI	10.1145/2482540.2482563
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2013-06-16
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Online algorithms Personalization Choice models Assortment optimization Inventory models management
Content Type	Text
Resource Type	Article

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