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SENSY Inc. has begun a collaboration with Intage Inc. to realize store development support by combining Intage’s large-scale purchase history data “SCI-CODE” with SENSY’s AI store development DX service “SENSY GeoScope,” with the aim of improving the sophistication of store development and store opening decisions for retail companies.
“SENSY GeoScope” is a service that combines POS data held by retail companies with data such as population, trade area, and competing stores to simulate sales forecasts for potential store locations, trade area and competitor analysis, and the impact of changing store opening conditions.
This time, we will combine the store development support function of “SENSY GeoScope” with Intage’s large-scale purchase history data “SCI-CODE,” which continuously captures “when,” “where,” “what,” and “how much” consumers purchased.
By leveraging the value of retail companies’ proprietary data and incorporating information on consumer demographics in the market, usage patterns of competing stores, and actual store usage patterns and shopping habits, we aim to further enhance store development simulations, including those for areas where there has been no prior store opening experience and new store formats.
Through this initiative, the two companies will verify the effectiveness of store development simulations combining “SCI-CODE” and “SENSY GeoScope” and consider deploying them as a joint solution.
In the retail industry, store development is a crucial management decision that will significantly impact future sales and profits.
On the other hand, evaluating potential store locations involves many factors, including the population of the trading area, competing stores, road and parking conditions, sales floor space, and regional characteristics. Traditionally, this area has largely relied on the experience of the person in charge and external research.
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In recent years, the importance of investment decisions regarding new store openings and renovations has increased significantly due to factors such as rising construction costs and land prices, labor shortages, and changes in the competitive environment. Improving the accuracy of store opening decisions is crucial not only for reducing investment risks but also for enhancing the growth strategy of the store network and improving the value provided to local residents.
In particular, in areas where we have no prior store locations, it is crucial to understand which stores consumers in the vicinity of potential store locations use and what kinds of products they purchase.
However, since our own POS data only allows us to understand purchasing behavior at our existing stores, it was difficult to directly grasp the usage and purchasing patterns of competing stores in areas where we did not yet have a presence, as well as how consumers differentiate between stores and shop around.
Therefore, both companies aim to utilize market purchasing data to understand consumer purchasing behavior and the actual usage patterns of competing stores in the vicinity of potential store locations, and to use this information to inform decision-making regarding various store developments, including areas where they currently do not have stores.
Source: PRTimes
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