Nowadays, we almost can’t imagine online shopping without recommendations systems. Popular electronic commerce websites like Amazon, Bol.com, Asos.com and so on all have a section with products they personally recommend to their customers. This is often displayed as: ‘You may also like…’ showing multiple products related to the ones you have recently viewed.
Integrating social networks like Facebook and Instagram into the world of electronic commerce is on the up and can contribute to the personalized recommendation systems of online retailers. In this way, customers get personalized recommendations based on what friends in their networks bought. This makes the less popular products, which customer normally not have looked for, more visible and stimulates consumers to buy products that they normally wouldn’t have found. These products are known as ‘the Long Tail’ products and are often presented as ‘Customers like you also bought…’.
To put it differently, if consumers get e-commerce recommendations based on their networks, the level of awareness for less popular products will increase. This means that the distribution of revenue and demand is influenced and shifts more towards a long tail distribution and away from selling primarily the most popular products. Simply by peer-based recommendations, customers will buy more and different products than they would normally have.
Research done by Oestreicher-Singer & Sundararjan (2012), investigates the impact of peer-based recommendations on the demand and revenue distribution. They research the influence of network-based recommendations on the online sales of 250.000 books from online retailer Amazon.com. The research shows that by recommending books based on what friends in customers’ networks bought, the distribution of demand and revenue is highly influenced. The researchers focused on the top 20% most popular and top 20% most unpopular products.
Categories of unpopular books that were displayed based on peer-recommendations experienced a 50% increase in revenue whilst the commonly unpopular books experienced a 15% decrease in revenue. This meant that the unpopular books suddenly became more visible to customers which led to an 50% increase in sales.
That all sounds quite impressive, but one could not say that this 50% increase was only caused by the visibility of products through recommendations. Different other contemporary factors also contribute to the redistribution of demand and revenue of consumers. Lower search costs and higher product variety for instance, have a great influence on the long tail of e-commerce.
All things considered, e-commerce is highly influenced by the power of social networks. The influence of recommendation networks positively affects to phenomenon of the long tail demand. Selling less of more rather than more of less is going to characterize the e-commerce demand curve in the future. The implementation of ‘what other customers like you bought’ will continue to impact our online shopping behavior. If companies implement the right recommendation systems to influence consumer demand, the opportunities are endless!
Anderson, C. 2006. The Long Tail: Why the future of Business Is Selling Less of More. Hyperion Press.
Brynjolfsson, E., Hu, Y., and Smith, M.D. 2006. From Nichees to Riches: Anotomy of the Long Tail. Management Review 47(4) 67-71.
Oestreicher-Singer, G., & Sundararajan, A. (2012). Recommendation networks and the long tail of electronic commerce. MIS Quarterly