In today's online world, third parties collect and store your browsing data at staggering rates. Third parties benefit greatly from this information, but do you get fair returns for sharing your data online?
New research from the University of Florida Matherly Professor Anuj Kumar and Santa Clara University's Xiang Wan has proposed a novel method to measure the value consumers get from product recommendation systems , one of the most prominent online tools that useWhile numerous studies have established that RS benefits retailers through increased sales, its benefits to consumers are unclear.
For example, imagine you are searching for a women's top on Macy's website. After searching, you purchased a red V-neck fitted top that you found organically and a maroon collared top with a loose fit that you found with RS. The relative values of the two tops are only known to you. Maybe you liked the maroon color more than red or the loose fit more than the tight fit. Or you attached a higher value to the brand/designer of the maroon top than the red top.
"On average, consumers purchase recommendations with higher affinity scores with the product they explore. Thus, affinity scores of recommended products measure their relative value to consumers," Kumar explained. In this research, Kumar and his co-authors show that consumers are more likely to purchase under RS because it helps them find higher-value products—which are either lower-priced, match their tastes better, or both. They found that the benefit of RS was higher for product categories with high price dispersions and consumer taste heterogeneities.
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