X-Pand is one of a kind location intelligence tool which combines internal sales and customer data with relevant external data. Considering, 90% of the customer’s data lies outside the company, the tool is able to provide unmatched insights about the potential of a location. This tool can be used to efficiently identify a location for new outlets as well as for evaluating the performance of the existing ones.
X-Pand – What Makes it Stand?
Location is probably the most important factor contributing to the success of a retail store. However, reversing a location selection can cost companies dearly as it involves a considerable amount of time, money and resource investment to open a new store at any location. There is an implication on supply chain and transport that must be managed besides an impact on operating cost, marketing cost and marketing strategy. The prevalent options for opening a store is either to depend on the local knowledge that can’t be validated scientifically and/or pick standardised locations like an IT Park or a mall. Combining these two approaches is also an option. But that too, is completely inward looking and not exactly customer-centric. Similarly, evaluating performance of a store basis historical data does not encapsulate the fact that one store can have a significantly higher potential than the other.
Clients’ internal sales and customer data along with external demographics as well as socio-economic data are used to create business relevant indexes like Profitability Index and Connectivity Index. Generalized Linear Model (GLM) is used to identify features with a high correlation to store sales. These drivers are then inputted into Machine Learning Models, which help identify catchment areas with high sales potential. To help make an informed decision and particularly to bring an outlet location choice closer to the profitable proposition, any suggestions, constraints, rules and preferences set by the client are also considered in the model. This way, the tool is able to measure the potential of a location more accurately than ever before.
The algorithms are tested and validated recursively on random samples created from the small pool of data. The final algorithm is then used to score the potential or existing stores in different markets. Sensitivity analysis is also performed to account for individual factors. The final output comes in a form of an interactive map. The client can zoom into the map to see all important data points. A list of locations is provided to the client along with a score that acts as an indicator of priority. The model refreshes and learns from itself as new stores are opened or as new sales data comes in.
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