Recommendation systems use consumers’ previous behaviour on a digital service in order to anticipate their next move. Companies such as Spotify, Google, and Amazon all base a major part of their services on algorithms that form a recommendation system. The many successful examples in today’s marketplace support Smith’s (2015) argument that the marketplace is shifting from “meet demand” to “on demand,” and the future is moving to “know demand,” which is not only about predicting what consumers desire, but also creating these needs and wants.
Based on Oestreicher-Singer et al. (2013) research findings, recommendation systems actually form a product network of the company’s offering. This network works much the same way as Google’s PageRank algorithm - recommending products that have the most links from the products that have been previously examined or consumed. Based on this logic, Oestreicher-Singer et al. (2013) suggest that the value a product creates for a company should not only be considered as the intrinsic value based on its revenue implications, but should also incorporate its ability to lead customers to other products via recommendations, i.e. the value it receives from, and contributes to, the network itself.
By considering both the product network as well as the product’s network value when developing an offering and marketing mix, companies are able to create more value for customers, and by extension for themselves. This value is created not only through broader and more insightful offerings, but also with better recommendations. This has far-reaching implications in the entire supply chain, when for instance, manufacturers’ products (for example, in the case of retailing) are more easily found by customers or just more accurately valued.
A clear range of benefits for consumers can be found in having access to a more accurate product portfolio and a personalized ‘path’ through the aisle of a digital retail store. In fact, by providing a personalized experience, a successful recommendation system helps to reinforce consumers’ identity by repeating their previous behaviour. This is the case especially in the field of digital music and books, where the product itself helps to shape and reaffirm one’s identity. Also, the algorithmic experience is such that consumers develop a relationship with it, feeling as though it knows them and their preferences very well.
However, the growing concern over data privacy cannot be overlooked. Policymakers have conducted extensive research in the last two years, looking into the advantages and disadvantages of big data analytics (Smith 2016). In the marketplace, a glaring contradiction exists among consumers who, on the one hand, are concerned about their privacy, while, on the other hand, freely give away their information on different online platforms. When examined from this perspective, companies such as Google, Spotify, and Amazon are serving as identity-enhancing mechanisms that help consumers cope with the anxieties caused by the contradiction between the benefits gained from personalized experiences versus the pitfalls of privacy-related issues. In order for a company to place itself at a central point of the customer’s life, it has to narrow the gap between these privacy concerns and the vision of algorithms enhancing our everyday lives through better online customer experiences. The rewards of doing so are boundless.
References
Smith, Lauren (2016), “Algorithmic Transparency: Examining from Within and Without,” IAPP.
Smith, J.Walker (2015), “Marketing in the Era Programmatic Consumption,” AMA.
Oestreicher-Singer, G., Libai, B., Sivan, L., Carmi, E., & Yassin, O. (2013). “The Network Value of Products," Journal of Marketing, 77(3), 1-14.
This post was originally published at ama.org. Jules Chirol, Minni Kuusisto, Lorna Liu, and Durriya Zaidi are students in Markus Giesler’s Customer Experience Design MBA elective course.
