Data science is helping c-stores succeed from inventory to employees.
Grabbing a soda and a snack after filling the gas tank may not seem like contributing to an intricate data analytics cycle; but for c-stores looking to maximize business, this sort of customer behavior provides valuable insights that can inform everything from inventory management to employee retention.
By aligning or connecting data capture tools to one’s generating reports and insights, and using the data to inform decisions, convenience retailers can harness the full potential of data science.
Data science does not need to appear like a lofty goal reserved for the most advanced companies. Nor should it be considered a tedious concept for convenience store retailers to embrace. Systems and software that do the work are available, and stores reap the benefits.
Front door to back door
In order to meet the needs of customers coming in the front door, c-stores need to track purchase patterns, making sure what is coming in the back door is the right product to fit the need.
To be most cost-effective, for example, owners need to ensure they have the proper inventory, without keeping too much or too little on hand. Product needs to move smoothly from the back door (coming off the truck) to the front door (purchased by a customer) in a consistent pattern.
Foodservice at convenience stores represents two lines of business that are monitored back to front as well. First, pre-made food like sandwiches and salads which aren’t made on premise can be monitored similarly to other consumer packaged goods coming from a distributor or directly from the manufacturer.
Second, there are food offerings made on premise from fresh ingredients. Monitoring purchase behavior can aid in managing inventory on things like burger patties and lettuce; likewise, data can inform when to prepare those foods so as to reduce food waste.
If the analysis shows that there are around 100 freshly made turkey sandwiches sold around midday between Tuesday and Friday, but not during the weekend, then it makes sense to prep enough ingredients for 100 turkey sandwiches, and to do so in the late morning so as not to leave it “sitting out” for too long. Being able to do so for all items appears daunting, but tools that use data and data-science/analytic foundation can help retailers manage this complex environment.
Personal data vs. behavioral data
Data privacy is a hot topic for good reason. Though consumers seek transactional personalization, there’s only so far some are willing to go to provide the information that informs it.
Fortunately, c-store retailers can gain more from tracking shopping behavior than personal customer data. Knowing a customer’s birthday might enable a once- yearly recognition, or understand general differences between Gen-Z and Boomer customers, but it is far more useful to understand what a customer is buying, what offers they are engaging with, and whether they are redeeming points for fuel. In other words, actual behavior can be turned into a powerful asset to help anticipate future outcomes, purchase choices, and product inventory needs.
Some general personal data can be helpful to better paint who the customer may be. For example, a zip code can give some sense of a socioeconomic background, but on its own, that information is fairly inoperative. By adding in purchasing behavior, a picture can begin to form of a shopper persona, their preferences, and tradeoffs, what may drive them to change behavior, etc. But, overall, action data, not necessarily personal data, can be the most powerful, creating the opportunity to leverage the information coupled with available tools in the market.
Lower stress = employee retention
Talk around the so-called great resignation of 2021 may seem to be based only in the corporate world, but c-store retailers aren’t immune.
For every fuel retailer employee who quits, there are others left to carry the load. At a c-store, this can mean one employee covering every facet of the business. The responsibility of helping a customer at the fuel pump, cooking a hotdog for someone inside and checking out customers in line can be overwhelming; meanwhile, restocking the shelves quickly moves to the back burner.
Use of data science and tools can ease the burden of employees and reduce stress levels, helping to increase the likelihood that they’ll stay with the company. This of course may not be the only instrument in the toolbox, but one that can help reduce operational complexities.
By studying customer behavior data, c-store retailers can determine when business rushes most often occur, and what types of items need to be ready when they hit. If energy drinks are in high demand in the morning, the store associate knows to stock the cooler before the morning rush; if the next busy time is midday, they can plan to prep sandwiches in between.
Adapting automation like self-checkout can also support an employee working solo by streamlining the cashier process. Data collected at the self-checkout kiosk can further inform purchasing behavior, as well as gauge customer adoption of such technology.
For smaller c-store retailers, the best bet for 2022 is to take a “crawl, then walk, then run” approach when it comes to integrating data analysis.
Implementing a loyalty program is an effective way to start. Focusing on one narrow area like a coffee club for frequent buyers can start to draw actionable data on a small scale that can grow over time.
Another option is to engage a partner to build and oversee data capture and analytics. For c-store owners with varying levels of tech-savvy, this can mean getting up to speed more quickly.
In either scenario, the integration of data tools is a trend we’ll continue to see in the coming years. Harnessing the insights they enable will be a key part of fuel and convenience retailer business growth. When it comes to managing, or even optimizing, back-door to front-door, connecting between tools and data remain an industry opportunity.
Dafna Gabel is vice president, dtrategic insights at PDI Software