C-store 3D: New Data Sources for Better Operations  

Advanced data capabilities give convenience store operators a dashboard to maximize their merchandise turns and revenue.
Data sources for convenience stores

In the past, most data from convenience stores and fueling stations was either generated by their ERP systems — focused on avoiding out-of-stocks — or by their bookkeeping platform to monitor financial performance. New analytic tools enable pulling near real-time data from the point-of sale (POS) in conjunction with inventory data from a back-office system, allowing convenience retailers and brands to achieve a richer view of consumer behavior. 

These analytic tools make it easy to draw insights from transaction-level detail, which helps manage dayparts, assortments and promotions to improve sales. As convenience retail faces increasing competition from the broader retail and hospitality segments, the right technology can make a big difference. 

Many c-stores began using these tools to gain insights prior to the pandemic. They have been able to track pre- and post-pandemic shopping behaviors. This gives operators a baseline of activity against which to measure current performance, and evaluate their merchandising and promotions. The resulting insights can enable more proactive management, yielding higher revenues and greater profitability. 

A Better View of Changing Habits  

Per PDI's May 2021 C-Store Shopper Trends Report, c-stores saw annual sales increase 12.9 percent over 2020 and the average spend per visit jump 1.9 percent year over year. Some of this might have been attributable to consumption changes driven by the pandemic. But trend lines indicate the patterns are holding and represent the proverbial "new normal." 

While the trend is interesting, more significant is the fact that the data is there to identify the pattern. Advanced analytic platforms capture POS data in near real-time to fuel on-demand dashboards. The level of detail is everything you would expect to be associated with the SKU. But it also includes time-stamp details and basket item-sets that often go unexamined by c-store operators. 

This represents a treasure trove of actionable insights. By tracking these shifting purchase patterns with more granularity than just out-of-stocks, operators can adjust their merchandise mix for maximum turns and revenue per square foot. 

For example, a recent national snapshot revealed that while cigarettes remain the largest category by volume, dollar growth in alternative snacks and general merchandise was higher. And the biggest growth was in general merchandise as a percentage of transactions. 

These insights point to shifting baskets within the convenience channel. Operators who can see the detail of item-sets can drive cross-selling activities to increase basket size. What's more, with access to normative regional or national data, operators can benchmark their performance beyond same-store metrics. This is a powerful tool for a retail segment traditionally run by habit and hunch. 


The near real-time reporting capabilities and the very different behaviors by daypart enable a unique understanding of buyer intention that can inform more sophisticated merchandising strategies. This is especially important for c-stores, which tend to not be destinations for planned purchases.

Being able to recognize and forecast when certain types of impulse purchases are made enables operators to be proactive in what they feature or promote. 

The ability to correlate c-store purchases by daypart to fuel purchases is another leverageable dataset. In combination with loyalty programs and mobile app promotion platforms, retailers can reach customers in the forecourt to drive sales in the store.

Multisite operators can cross-tab data by geographies to optimize product selection and merchandising, squeezing even more sales and profit from each location. They can also experiment with displays or promotions in one location and get granular, actionable results on which to make decisions for adjusting a program or expanding it.   

The POS data can also be paired with loyalty program information for even greater insights and business-building opportunities like hyper-targeted ad programs to plant the seeds of an impulse purchase before the customer even leaves their house. 

Like Big Stores, Only Better 

As stated earlier, c-store sales are growing, driven by changing buying habits. The good news is that there are more opportunities for maximizing revenue. The bad news is that the trends will be harder than ever to read without sophisticated data capture and analytic systems. 

The unique impulse purchase/opportunistic transaction nature of c-stores is better suited to leveraging high-speed data analytics than any other retail environment. In fact, real-time data capture and time-stamped reporting are almost mandatory for such a dynamic retail environment. 

Grocery and big-box retailers have already shown the power of leveraging POS data. Advanced c-store data capabilities provide a level of insight to inform business growth with even greater velocity than their larger counterparts. With almost four times the number of c-store locations relative to grocers, the geographic insights are likewise more robust. 

Advanced c-store data capabilities give operators a dashboard to maximize their merchandise turns and revenue. It's always been recognized that shopping behavior in c-stores is different from other channels. Now, data systems can quantify those differences on a number of dimensions that can power new tactics, new strategies, new profits, and even new revenues.  

Brandon Logsdon is president of PDI Marketing Cloud Solutions. He joined the company in 2018, and oversees loyalty and fuel pricing solutions, in addition to PDI’s MarketLink and Data Monetization services. Prior to PDI, Logsdon served as president and CEO of Excentus.

Editor’s note: The opinions expressed in this column are the author’s and do not necessarily reflect the views of Convenience Store News