By Tom Hill and Donncha Carroll

It’s been a tumultuous few months in retail – from store closures, to the safety and well-being of employees and customers, to inventory management, and the further and now accelerating transition to eCommerce sales. All of these retail challenges add to the pre-existing disruptive forces that were compressing margins within brick-and-mortar stores, changing and shaping customer expectations across the omni-channel experience and impacting labor models.

Recent events will almost certainly alter many of the underlying assumptions that made different locations viable. We believe that store operations will continue to be reimagined in the short and mid-term requiring retailers to think very differently about how to segment stores. The fundamentals have changed and in ways we are only starting to learn about now. To respond we are advising our clients to gather the data and develop the measurement infrastructure to categorize locations based on characteristics that are relevant to operations specifically. Resist the temptation to simply look at revenue or potential as the patterns of buying behavior are frankly more important.

Retail Store Segmentation

There are a number of internal data sources that retailers can emphasize and use to better understand store productivity. Start with building well-established metrics like store sales, store traffic, ADS, UPT, and customer conversion, but also very more importantly start exploring how these metrics relate to one another. The interrelationships hold the patterns that determine why one store performs better than another. Armed with these insights, managers can much more effectively determine how best to think through optimal staffing and to coach and/or support new or struggling sales associates.

As predictable patterns emerge from this analytical review, use these insights to segment stores based on common characteristics (e.g., high volume / low ADS combined with minimal staffing, volume concentrated Thursday through Saturday with low traffic conversion rates). While volume is used most often, if used exclusively it can cause managers to make decisions that undermine instead of lift sales. For example, some volume can be explained by store location or positioning and may not easily convert to sales. There’s always a story behind one data point and why a more comprehensive view across multiple measures is necessary to properly categorize stores.

 

5 Benefits of Data Driven Decision Making

By using new in-store technology from organizations like Kepler Analytics you can get even more information on traffic patterns including activity located outside of the store, dwell times and fitting room information. These additional sources of information provide new insights that can paint the bigger picture enabling actions that improve operational and marketing efficiency:

  1. Increase the focus of effort on retaining past or repeat customers while more efficiently addressing new customer acquisition
  2. Pair products based on customer persona that reflect a segmentation model developed from sales and CRM data
  3. Use data from the eCommerce site to better arrange store layout and enable more suggestive selling
  4. Build a better understanding of the true connectedness of your omni-experience by evaluating how your digital spend relates to store visitation and buying patterns
  5. Adjust customer experience strategy and product sales around traffic and other metrics

Developing more integrated analytics that “connect the dots” between action and performance is really the difference maker and how you and your team can progress along the analytics maturity curve.

When you’re ready to take it to the next level you can start to explore predictive modeling techniques that forecast demand and can more effectively support planning and resource allocation.

Contact us here to learn more about how Axiom Consulting Partners helps retailers improve store effectiveness.

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