Today’s shopping seasons tend to resemble war, at least if one were a retailer. There is a lot of money spent, and there’s too much at stake. Especially in Indian retail, it’s life and death for many E-commerce retailers as a stellar performance is not enough. Winning the market share war is key.
With Indian organized retail and E-commerce still in its infancy, there is a lack of credible and complete information on market share. In this paradigm of limited information and high stakes, understanding performance becomes key. One of the biggest barriers to a holistic performance analysis is that retailers mostly have only their internal transactional data to consider. What they miss out on is a host of external consumer signals.
Today, the digitally empowered consumer is constantly generating data and leaving clues about their likes, dislikes, how much are they willing to pay for a product, etc. in the form of product search, reviews, ratings, social signals, etc. Therefore, analyzing performance by relying only on internal transactional data is incomplete. Retailers need to leverage this new source of consumer data and blend it with internal transactional data to obtain an objective, holistic and data-driven understanding of the probable causes of decline or improvement in performance.
With the Diwali season concluding, online retailers will want to know how they performed against competition. However, in the face of limited internal data, there is no way for big e-tailers to understand their market share without considering consumer and external data. For example, in a recent study conducted by Ugam, we analyzed the consumer demand signals of smartphones offered by Amazon and Flipkart. Based on Shopper Intent signals, such as reviews, ratings, search volume, etc. it was found that Amazon offered majority of the top 20 most-preferred exclusive smartphones. Surprisingly, though these smartphones were exclusive to either Flipkart or Amazon, our study showed that some smartphones were offered on both the channels, thus probably eating into each other’s market share. Moreover, Amazon won the price war by offering the lowest price for more than half of the exclusive smartphones available on both the websites. In the absence of market share data, e-tailers can make a data-driven inference on their market share by analyzing freely available data to understand how competitive was their assortment, overlapping products, who offered the best price, etc.
Let’s take another example to understand how a leading mass merchant blended external and internal data to get a holistic view of performance. The retailer was facing a decline in market share in one of their key categories, despite an increase in sales. The marketing, pricing and merchandizing teams, within their silos, could not determine what was causing the decline. To get a better understanding of the problem, they began testing various hypothesis, such as - was web traffic a problem, were they competitively priced, did the consumer have better product choices somewhere else, was inventory availability an issue, could website user experience be better elsewhere? To help answer these, they needed to gather external data on traffic, competitive pricing and assortment, reviews, ratings, etc. and blend that with internal transactional data and 3rd party data to ultimately uncover possible reasons for decline in market share. This holistic performance analysis further guided the retailer with more informed and impactful decisions.
With the market share war among Indian retailers intensifying, it has become increasingly important for them to blend external with internal data to not only understand the ‘how much’ of an increase or decline in their market share, but also the ‘why’. This, in turn, would help them in taking the necessary steps in strengthening their strategy and maybe even win this war!
Mihir Kittur is a Co-founder and Chief Innovation Officer at Ugam. He oversees sales, marketing and innovation and works with leading retailers and brands with insights and analytics solutions around their category decisions to improve business performance.