Southern States focus and analytics system
Most of us in retail know, all too well, that the business climate has not been kind in the last few years. Every business decision, move or change we make must result in a positive impact on the company’s bottom line—or we don’t make it. But knowing which moves are the right ones before we actually make them, can be the greatest challenge of all.
At Southern States Cooperative, it all starts with a commitment to get closer to our customers through constant collection of customer data, both internally and externally. We use marketing research, our own transactional information and third-party data to make better decisions as an organization. Then, we rely on sophisticated, predictive customer analytics to guide our direct marketing efforts, determine new store locations, drive customer loyalty and discover trends in pricing and promotions that can help our company maintain its competitive advantage. Here are some of the ways Southern States uses analytics to gain deep customer insights:
In our direct marketing efforts, we build predictive models that help us better target our customers and understand how their behavior is affected by different marketing messages. Using predictive analytics, we were able to optimize our direct mail efforts, reducing the number of catalogs mailed by 74 percent, while simultaneously more than doubling the response rate.
We found that there are certain spending patterns and demographics that can help us identify likely responders. This allows us to target our mailing list to just those with the highest likelihood of profitable response. Even sending to fewer people, we grew revenue, increased gross margins (after mailing costs) by nearly 47 percent and reduced our overall costs. And we didn’t do it by rule of thumb, trial and error or throwing darts at a board. Our predictive analytics models guided our decisions based on real data.
We also use predictive analytics to make decisions about what markets provide the best opportunity for growth. We develop decision models based on data from current store locations and use these models to target new locations with similar customer types, consumer spending patterns and competitive landscapes. This allows us to go beyond simply finding the right location for future stores and also forecast revenues for these stores.
In addition to using predictive analytics, we also find deep insights by appending primary market research to our own transactional data, allowing us to understand not only how customers feel about and respond to our brand, but also how those factors translate to behavior from a sales perspective. Ultimately, this enables us to measure what drives loyal customers versus the general population.
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