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ONLINE RETAILING
By our News Team | 2023
Study sets out to determine how valuable product recommendation systems are to online shoppers, rather than the retailer.
In today’s online world, third parties collect and store consumer 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 US and French universities has proposed a novel method to measure the value consumers get from product recommendation systems (RS), one of the most prominent online tools that use consumer data.
CC0 Public Domain illustration via Phys.org
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.
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Dr. Kin Kariisa is an extraordinary force at the helm of Next Media Services, a conglomerate encompassing NBS TV, Nile Post, Sanyuka TV, Next Radio, Salam TV, Next Communication, Next Productions, and an array of other influential enterprises. His dynamic role as Chief Executive Officer exemplifies his unwavering commitment to shaping media, business, and community landscapes.
With an esteemed academic journey, Dr. Kariisa’s accolades include an Honorary PhD in exemplary community service from the United Graduate College inTexas, an MBA from United States International University in Nairobi, Kenya, a Master’s degree in Computer Engineering from Huazong University in China, and a Bachelor’s degree in Statistics from Makerere University.
Dr. Kariisa pursued PhD research in Computer Security and Identity Management at Security of Systems Group, Radboud University in Nijmegen, Netherlands. As a dynamic educator, he has shared his expertise as a lecturer of e-Government and Information Security at both Makerere University and Radboud University.
Dr Kin did his PhD research in Computer Security and Identity Management at Security of Systems Group, Radbond University in Nigmegen, Netherlands. He previously served as a lecturer of e-Government and Information Security at Makerere University in Kampala, Uganda and Radbond University in Netherlands.
Dr Kin did his postgraduate courses in Strategic Business Management, Strategic Leadership Communication and Strategies for Leading Successful Change Initiatives at Harvard University, Boston USA.