Once upon a time, the only way a convenience store manager knew what was selling was to look at the shelf.
“It was up to the person being there, seeing an empty spot on the shelf and making the judgment call to say, I’ll order that again,” says Chris Kiernan, director of retail development for ADD Systems, a provider of back office and mobile software for c-stores and other markets. “If you were not there looking for empty spots and ordering accordingly, you missed the chance for those sales.”
Thankfully, those days are gone. Data analytics can help a retailer understand a store’s customers in a way that was not previously possible. “With the appropriate software systems, you can look at your sales data and understand the velocity of items being sold at different times of the day, so you can better accommodate their needs in the morning versus afternoon and rush times,” Kiernan points out. “In addition to looking at each day’s sales, you can look at month-over-month changes. If there is a drop in sales, you can ask: Why is that? Did something change in the offering? Did you change the vendor? Just by analyzing data you can tell if sales are up or down, which times of day are better and make better vendor decisions.”
One of the interesting pieces of information a retailer can learn is which items are being purchased together. “Seeing what is sold together gives us a picture of customer buying habits,” says John Coyle, VP of sales for ADD Systems. “Promotions can be implemented to take advantage of these habits and increase sales velocity on those items. You’ll see a lot of promotional signage—‘buy a hot dog, drink and chips for $4.99’—that encourage additional sales.”
Building a better inventory
Understanding what is sold and when helps a c-store retailer make smarter purchasing decisions. Coyle offers an example of a client that performed an inventory analysis and learned that he was running out of inventory of the Heineken keg can twice a month—the days he ran out, he lost sales. The other days, it was a top seller. The result of the report? The client worked with the distributor to increase order frequency of this product.
“By identifying this opportunity and then taking advantage of the fact that it was a top seller, he leveraged reporting from business intelligence to increase sales and profitability just on that one item alone,” Coyle says.
Besides adjusting product, a retailer can also use the data to properly staff a c-store to ensure consistent and high quality customer service. As an example, during the morning rush a store might want to staff up with an additional employee to restock the coffee area and keep the counters clean. Such actions can help to increase customer loyalty. Conversely, during slower times, a store might not need as much labor, as it could cut into their margins.
Data, which is captured at the point of sale, provides other key information, such as what the sales trends are outside (at the pump) and how one store in a chain compares to the next.
“One of the more significant changes is the data allows us to manage by exception,” Coyle says. “We want to identify both what is not working as well as what is working above and beyond expectations. Managing by exception means you have actionable information that lets you make timely, precise decisions.”
For c-store retailers looking to strategize their inventories and gain a better understanding of consumer preference and overall store performance, data analysis is the way to go.
This post is sponsored by ADD Systems