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Revenue Prediction Models with Spatial Data Science

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 pertaining to 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. Having said that however, the sales forecast still continues to be one of the most insurmountable challenges for detailed location planning till this day.

Predicting revenue for stores is quite critical, as it allows retail leaders to be agile, make informed decisions regarding store operations currently and also in planning of new openings in the most effective manner. Being able to leverage spatial data science can help businesses in maintaining a lead ahead of the competition as well as pursue continual growth.

It is no secret that the advances that have been made possible today owing to location data have aided in continued transformation of business practises and processes when talking about site planning. Newer data streams today and the insights garnered by them, make it possible to determine the sites that are likely to enhance sales for retail businesses.

To generate a relatively accurate revenue production model, the process begins with the identification of variables that are in and around locations that 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 information regarding residential pointers for the area of operations, POI data can help in the identification of patterns of nearby retail establishments that can serve as a predictor for the model.

Additional data information such as card spend data can be added to the model that can help in providing aggregated and anonymous merchant level transaction insights, regarding details of how, where, 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 insights regarding customer volume for each establishment.

Empirical evidence has shown that the potential sales of any new retail outlet can be predicted quite precisely with inductive methods of spatial data mining as geographical information received in the process plays an important role in the improvement of the forecast. With new data streams bringing in a new era of site planning, solutions to previously thought impossible situations are now possible. When one looks at revenue prediction for retail spaces based on spatial data science, one has to look at various types of data sets ranging from traditional to new derivatives to be able to identify, understand and quantify the impact that a particular location has on your sales revenue. Thus leveraging spatial data can help in enhancing the predictive power and accuracy of machine learning models, providing businesses with a much needed competitive edge that sets them apart from the rest.

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