How Online Retailers Can Optimize Recommendations for Customers
3 years ago by

A recent study conducted by ‘Barilliance’, revealed that 31% of e-commerce websites’ revenues were generated through personalized product recommendations. The study took into account more than 300 e-commerce websites from around the world. Moreover, the conversion rate for customers who clicked on the recommended products was estimated to be 5.5 times higher than the conversion rate of non-clicking customers. These statistics reflect the importance of personalized recommendations for any e-commerce website, since it offers the direct benefit of increased sales and profits. This also promises to enhance customer experience.

Algorithmic Recommendations

 

Algorithm generated recommendations have been around for ages, which are primarily based on user’s search/purchase history, location and demographical information. Mainstream online retailers such as Amazon have been experimenting with various algorithms to generate personalized recommendations for users, which have been somewhat successful in enhancing the overall customer service experience. With the advent and popularity of e-commerce, businesses can’t merely offer search and browsing option for customers, the recommendation model is fast becoming an integral part of online shopping. Personalized recommendations, fundamentally, aims to bring back the leverage that conventional brick and mortar shopping experience offers, where sales staff interact with customers and make recommendations.

E-commerce websites have made inordinate enhancements in the algorithm backed technologies, which have enabled businesses to produce recommendations that are in line with what customers actually might want to buy or might be interested in. If customers take the recommendation seriously, it will expedite machine learning, which will consequently help generate even more reliable and accurate recommendations in the future. Algorithm model is an evolving process: The recommendations improve as more and more information about a customer’s buying habits become available. The software acquires the acumen to read pattern, based on the available information, and generate recommendations that encourage customers to explore and purchase more items.

 

Extension to Service Industry

 

Personalized recommendations are not only restricted to online retailers, but have been extensively deployed in service industry including banking, insurance, education and entertainment. Spotify, a popular music streaming service, recently introduced its ‘Discover weekly’ playlist. The service has refined the ability to learn the taste of the listener and accordingly recommend the right song at the right time, by generating a personally tailored playlist of two to three hours of music every week. Other examples include insurance companies, generating recommendation results based on a customer’s data and personal information. Netflix uses “Recently Watched” to generate a list of shows and movies that might interest its users.

Human Curation

 

Companies like Google and Apple have gone one step ahead and gave way to human curated recommendations. Apple music streaming service offers its human-curated playlists. The company believes that machine learning must be aided by human intervention to refine and optimize the recommendation process. The company has dedicated genre experts creating playlist for its services, which have ensured that the recommendations are on point. As Tim Cook, CEO of Apple, put it, “Beats (owned by Apple) doesn’t ask people what they want to listen to. It tells them. That’s curation.” Apple also plans to augment its human curation services to TV shows and movies, hence, effectively covering all domains of personal entertainment.

In the avant-garde consumer market, which constitutes millions of products and service, human curation model could considerably help guide and refine the algorithms. The need for “Human Touch” is fast becoming a requirement rather than a value adding feature.

Curation for Online Retailers

 

The online retail industry could learn an important lesson from tech firms: Retailers should try and replicate human curated recommendations for their websites. Along with algorithmic recommendation, companies could hire specialists who will be able to help customers find their next purchase through analysis and personal touch.

Scrupulously created recommendations for customers could be the game-changer for online retailers, not only because it promises to exponentially increase sales, but it also offers a golden opportunity for companies to enhance the customer service experience by bringing back the long lost element of personal touch.


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