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In an increasingly crowded marketplace, retailers need innovative ways of promoting products to their consumers. E-commerce retailers have utilized to great effect lists of top ranked products to promote product sales; the higher the sales rank, the more likely consumers buy that product. This influence to buy, based on observing what others bought is known as observational learning (OL). Prior OL research assumed that OL arises from observing a static outcome, such as the current sales rank of a product. However, prior research on intertemporal choice showed that people prefer outcomes with increasing trends over stable or decreasing trends. This suggests that observing an increasing sales rank, denoted as sales velocity, would have a positive effect on purchase likelihood. We conducted three studies to test the sales velocity effect. Results show that sales velocity has a significant effect on likelihood of purchases, reversing even participant preferences for a product with a higher sales rank. This effect is consistent across four broad products tested. For researchers, by joining the two previously disparate branches of research in OL and intertemporal choice, we addressed a gap in OL research which previously ignored the velocity dimension of OL. For retailers, the study demonstrated the impact of the sales velocity metric on making choices, and thus they could use sales velocity data as a cost-effective marketing tool for specific products.
Conclusion and Future Research
Although we have shown the robustness of the sales velocity effect across different products and explored its boundaries, future work should test whether the sales velocity effect is diminished in the presence of price and actual monetary loss, similar to the incentive-aligned studies (Miller et al. 2011). This can be achieved by having choices which have real monetary consequences. We also studied sales velocity's effect with regard to promoting physical products; however it may also be highly effective for services or intangible goods — for example, for vacation packages and destinations or music downloads which are rising in popularity. This should be investigated in future studies. During our study, we established the effect of sales velocity, operationalizing it as a change over two time periods. The sales velocity can also be measured over multiple time periods, and of interest to consumers might also be the longevity of a positive rank change; for how long has a product been rising? This however, would expand the scope of the research beyond sales velocity, as we now have an implied acceleration or trending component, which was first studied by Hsee, Salovey, and Abelson (1994) in a cognitive psychology experiment. Since our intention was to establish the sales velocity effect in a retail context and to evaluate which type of products a retailer could promote with this method, it was beyond our scope to deal with acceleration measures. We focused on a simple, easy to implement and understandable metric that addresses the mid-tail of sold products. We acknowledge however that an extension of the work by Hsee, Salovey, and Abelson (1994) into the area of sales acceleration would yield further studies and our understanding on the dynamics of sales velocity.