How valuable are online product recommendations to consumers?

Powered by machine learning, a product recommendation system is the technology used to suggest which products are shown to individuals interacting with a brand’s digital platforms.

While numerous studies have established that RS benefits retailers through increased sales, its benefits to consumers are unclear.

Presumably, RS algorithms are designed to help consumers find products that provide them with higher value among numerous choices. 

Consumers may value a product because of its higher quality (such as better material and craftsmanship), lower price (for a given quality), or better match with their tastes (such as their colour choice). However, how much a consumer values a product is only known to her and not observed by others.

For example, imagine you are searching for a women’s top on a retailer’s website. After searching, you purchased a red V-neck fitted top that you found organically (without RS) and a maroon collared top with a loose fit that you found with RS. 

Many facts are not apparent to the retailer

The relative values of the two tops are only known to you. Perhaps you liked the maroon colour more than red, or the loose fit more than the tight fit? Or you attached a higher value (higher quality) to the brand/designer of the maroon top than the red top? However, these facts were not apparent to the online retailer.

The difference in desirability (value) of products you found with and without RS is the true value of the RS. However, since no one observes the value you derive from these products, measuring how valuable RS is to consumers is a challenge.

University of Florida professor, Anuj Kumar, along with Santa Clara University’s Shawn Wan and Xitong Li from HEC Paris business school, propose a novel method of measuring the value of RS to consumers from the similarity scores between products (called affinity scores) computed by RS algorithms.

This will appear in a forthcoming research article in the peer-reviewed journal, Management Science, titled ‘How do recommendations help consumers search products? Evidence from a field experiment.’

“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 explains.

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. In addition, the research team found that consumers preferred to use RS over other search tools on the website, perhaps because they found RS valuable.

Kumar affirms that managers can use this method to ascertain how consumers and retailers share the surplus created by the recommendations systems. The proposed method can compare existing RS in their abilities to help consumers discover high-value products and offer guidance on design improvements.