The internet, in many ways, has made the world “small” again by enabling online communities with specific interests to form. Entrepreneurs running small businesses are able to gain online followers and customers all over the world, without physical storefronts. The great irony is that these businesses are often unknown to their local markets.
Given that there will always be consumers who prefer to buy goods in person, Umer Farooq recognized these online-only sellers were losing out. For this reason he founded StoreCrossing, which allows smaller businesses to sell jewelry, clothing, beauty products, art, papercraft and other items within other stores, such as cafes, boutiques and spas, for a small fee.
Umer had a mind for building data into his platform early on: He used granular census information to identify neighbourhood trends, traffic flows and visibility. He would then share this information with customers to determine the best locations for them to set up. However, that wasn’t enough. He wanted to take the user analytics to a whole new level – the major task was to discover which products from online entrepreneurs would sell in different venues.
So Umer contacted the Communitech Data Concierge to identify datasets that would help him make the best possible matches between sellers (based on their category of product) and the types of host stores they should set up shop in, neighbourhood by neighbourhood. Communitech analysts suggested taking two proven approaches: analyzing distribution channels, and building a consumer profile. StoreCrossing could then determine the types of people likely to buy goods from the host stores, and also from the pop-up sellers. By doing so, the firm was not only considering the data from the supplier side of the problem but also from the demand side, and still solving the original issue.
Communitech helped Umer pull market intelligence reports through a referral to MaRS, and provided data from the Statistics Canada Survey of Household Spending, as well as U.S. Bureau of Labor Statistics Consumer Expenditure Survey. Using the data from the reports and surveys, sales could be cross-referenced across demographics and distribution channels. Umer confirmed this approach was right for his business through client interviews as well.
Through the initial datasets used for providing neighbourhood information to clients and the augmented survey information Communitech provided, Umer was able to find similarities and better insights on how to move forward with his business. StoreCrossing was then introduced to user analytics expert and data growth coach Shaohua Zhang, and is currently formulating a more complete user profile of both buyers and sellers. Since then, the firm has seen an eightfold increase in the number of pop-up shops, and is continuing to drive exponential user growth.
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