‘Small businesses will benefit significantly from spatial data, retail analytics in post-Covid world’

Technology for MSMEs: When talking about core factors that contribute to the success of a retail company, location is one of the most important ones, as it helps in determining not only external market conditions but also the internal scope for action. Of course, when you talk about operational practice, ROI becomes a crucial decision criterion for taking over a new site. Hence, decisions about the location are typically a microeconomic investment consideration.

Being able to predict the turnover for a potential new site becomes quintessential for every planning decision that relates to location. However, the sales forecast continues to be one of the most insurmountable challenges for detailed location planning until this day.

Predicting revenue for stores is quite critical. It allows retail leaders to be agile, make informed decisions regarding store operations and also effectively plan new store openings. Leveraging spatial data science can help businesses, small or big, in gaining a competitive edge, as well as pursue continual growth. Global retail brands like Domino’s, KFC, etc. are using location analytics and deep micro-level insights to assess new store location to maximise revenue probability. Small businesses like convenience stores that heavily rely on location, will benefit significantly from spatial data and related retail analytics. Especially in the post-Covid world, spatial data will play a key role in determining the success of a venture.

It is well-established that location data has transformed business practices and made processes more efficient, specifically in site-planning. Newer data streams today and the insights generated thereon have made it possible for companies to identify locations that can potentially enhance sales for retail businesses.

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To create a relatively accurate revenue production model, it is important to first identify variables that are in and around locations and can work as predictors. On a traditional footing, this is done using demographic insights gained from Census and Point of Interest (POI) data. While the Census data provides insights regarding residential pointers for the area of operations, POI information can help identify patterns of nearby retail establishments, which can serve as a predictor for the model.

Additional information, such as card spend data can be added to the model to obtain aggregated and anonymous merchant-level transaction insights. This will include details on how, where and when people spend their money. This becomes even more relevant when you look at transaction percentile scores that can help you in gauging the frequency measure. Given that similar ranges of retail spaces are generally placed within proximity of each other, having a frequency measure can give you an understanding of the customer volume for each establishment.

That said, empirical evidence has established that the potential sales of a new retail outlet can be predicted quite precisely by deploying inductive methods of spatial data mining as geographical information plays a vital role in the improvement of the forecast. With novel data streams bringing in a modern era of site planning, solutions to previously considered impossible situations are now possible. When you look at revenue prediction for retail spaces, based on spatial data science, you have to look at various types of information sets ranging from traditional to new derivatives. This will enable you to identify, understand and then quantify the impact of a particular location on the sales revenue. Thus, leveraging spatial data can help in enhancing the predictive power and accuracy of machine-learning models, enabling businesses to outsmart their competitors.

Ashwani Rawat is the Co-Founder & Director of Transerve Technologies. Views expressed are the author’s own.