For a long time business has relied on the well-known Pareto principle for explaining their patterns of sales distribution – the rule of thumb stating that roughly 80% of events would come from 20% of the causes. In business this principle was commonly used in stating that 80% of sales would follow from 20% of clients or 20% of products. Then came along the internet and it became apparent that the 80/20 rule lost its explanatory power for certain online businesses. Due to the lower search and reach costs resulting from an online business environment the, by now well-established, theory of the long-tail was proposed to explain for the newly observed sales distribution. This longer tail of niche product sales naturally meant that a smaller proportion of sales came from the ‘head’ of the distribution graph, as is shown in the figure below.

Now we have quickly refreshed your memory on sales distributions, let’s have a look at what this study did. The authors of the study were interested to see how the shapes of the distributions were affected by the electronic word of mouth present in the product group. In this, a distinction between goods rated according to more objective criteria and goods rated according to more subjective criteria was made. The authors reasoned that consumers may apply similar evaluation standards to products with objective attributes such as USB sticks. In this sense people would show high tendency to follow the eWOM evaluation, driving consumers collectively to the most popular products. As a result, the distribution tail would be shortened while the head would be thickened.

Alternatively, for products with high levels of subjective attributes such as books or movies, positive eWOM does not necessarily mean that you as a consumer would personally like the product as well. Finding a product that may fit your personal preferences is difficult, for such highly subjective products the authors reasoned eWOM would help you find products you otherwise wouldn’t. As a result, the distribution tail would get longer while the head would get thinner. These different effects of eWOM were indeed found when studying Amazon.com sales of products with both objective as well as subjective selection criteria. In addition, it was found that for complex products with multiple attributes, eWOM had a similar effect on sales distribution as for products with subjective criteria. This can be explained as consumers’ preferences will start to diversify the more attributes have to be assessed.

Resulting from this study we can conclude that eWOM can show two very different outcomes on sales distribution, depending on the product type you are looking at. Sellers should be aware of this and can optimize their online shelf sizes based on the product type they are offering. Stimulating eWOM as a firm selling simple products with objective selection attributes could for example decrease the need to keep a large product portfolio in stock. Saving costs by letting consumers chat to each other, now who would have thought!

Lee, J., Lee, J. N., & Shin, H. (2011). The long tail or the short tail: The category-specific impact of eWOM on sales distribution. Decision Support Systems, 51(3), 466-479.