Retailers are under tremendous pressure today to improve store profitability while simultaneously dealing with flat to declining sales as labor costs become an increasing percentage of revenue. There’s no shortage of news stories today proclaiming the demise of brick-and-mortar stores at the hand of thriving online competitors. But not so fast. According to the latest figures from the U.S. Department of Commerce, eCommerce sales still make up only about 9% – 10% of total retail sales. While eCommerce sales are growing, even Amazon sees the value of brick-and-mortar as evidenced by the Whole Foods acquisition, Amazon Books, and Amazon Go. Retail isn’t really going away – rather it’s changing to serve different consumer needs that are still evolving in an omni-channel world.
In addition to these shifts, retailers are responding to changes in customer preferences by redefining their in-store experiences (e.g., RH), rationalizing wholesale and using data to track the customer buying journey across multiple touchpoints that span different channels. Further, many retailers are adjusting compensation plans and updating the responsibilities and performance expectations of different roles, while revising hiring profiles to attract the right talent to perform what is becoming very different work (e.g., customer experience, customer education on products, clienteling).
All these efforts create value at the margin, but don’t address the biggest issue: increasing labor costs. Successful retailers will need to continuously – and in real time – monitor and manage store operations and retail execution to better align staffing with demand in order to enhance profitability. Below, we outline some common issues retailers face and how to address them to improve efficiency. The key to maximizing return on labor investment is to:
There are a number of internal data sources that retailers can use to better understand store productivity. Start with well-known metrics like store sales, store traffic, ADS, UPT, and conversion, and very importantly how these metrics relate to one another. These 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 coach and support new or struggling sales associates.
Additionally, you can start to use these measures to segment stores based on common characteristics (e.g., high volume / low ADS, average volume concentrated Thursday through Saturday). While volume is used most often, it can cause managers to make decisions that undermine instead of lift sales (e.g., higher rate of sales from clienteling-related sales that are transacted over the phone).
Once you have a working model in place, you can begin to layer in other data elements such as individual sales, CRM data, customer transactions, etc. These operationally-focused data analyses should seek to answer some key questions such as, “What is the relationship between conversion and employee coverage?” and “Should we concentrate outbound calls between 11 and 1 pm on Tuesdays and Thursdays?”
Once the data is analyzed for insights, you can start taking action to improve operational efficiency, such as:
Of course, the decision to take any of these actions should not be made in a vacuum. These changes have the potential to undermine success if not implemented effectively, so it is very important to pilot the program to identify and address unintended consequences, and to determine the real-world impact from any changes made. By using readily available data to develop more integrated analytics that “connect the dots” between action and performance, you can build the case for change that improves store margins by more effectively addressing controllable expenses. More importantly you can better align staff with opportunity, so you can grow the top line as well.