This is a true story, with only the names changed for privacy of the protagonist.
It happened exactly as you read it.
Here’s what happened:
“One Sunday in August, John and Jane Doe were on their way home from a brief vacation, driving along Hwy 13 in the northerly direction. When they stopped for gas at their favorite c-store location, John filled up their SUV and got inside to get a chocolate bar for Jane, as they forgot to stock the sufficient amount in advance, and chocolates are a necessity - or a food group - for Jane.
John quickly located the desired item on the shelf and got ready to pay for the purchase - but apparently he was not the only one willing to do just that. The line to the only open cash register was long and it moved with glacial pace. John waited, and waited. And waited. After 15 excruciatingly long minutes John’s patience ran out and he left the store - without the chocolate bar.”
This story is not about what John had to tell Jane and how he managed to receive her eventual forgiveness. This story is about the lost sale and the need for c-store operations managers to plan ahead for high-demand hours.
So let’s look into whose fault it was - store manager, regional manager, chief operating officer? It could be all three - the first because he did not took care of assigning extra cashiers for hot hours, the second because of structuring the personnel expenditures based on “yearly averages”, and third - because she was unable to provide her subordinates with reliable information about changing needs of the customers - not “in general”, “on the average'' and “as usual”, but in relation to the specific conditions of each individual store. And all this happened today, when modern data science provides all the tools needed to do it properly.
Considering that the revenue is directly proportional to the traffic flow passing by the convenience store location, Ticon offers clear diagrams of traffic flow fluctuations by days of week, seasons and even intraday (Figure 1). These are true traffic volumes with high data precision. This data is successfully used for site selection and trusted by major players of the c-store industry. The diagram below, for example, clearly shows why John could not buy the chocolate bar - because on Sunday in August, the expected number of customers is almost twice higher than average.
Figure 1. Daily, monthly and seasonal traffic volume estimations
With the increased demand for service industry workers and adverse impact of post-COVID reduction in “desire to be employed” among their ranks, proper staffing of the store is an ongoing challenge for the managers. Having the ability to adjust the work schedules of the limited numbers of available employees, based on the demand analysis provided by the data experts, is a key in achieving the revenue goals, as well as high levels of customer satisfaction.
Therefore, if the COO will explain to the managers that they should make timely requests of data for each individual c-store, and that regional managers should control the productivity of the stores according to the potential customer demand, then a store manager will understand that he’d better take timely measures to hire part time employees or adjust work schedules, to make sure he is going to get maximum revenue at his store. By the way, the cost of this data over the course of the whole year does not exceed 15% of c-store’s average daily revenue. And Ticon supplies this data ready for decision making, with no further processing necessary.
Ticon is a data analytics company. But our roots are in traffic engineering, and this unique compilation of skills gives us an edge over the competition when it comes to making sure that the data is up-to-date, meaningful and relevant to the location of interest.
Come and ask for a sample of our popular C-Site InsightTM report enriched for operational needs and schedule a private chat to learn about the nuggets of useful information we can show you for your location of interest. And the best part? You won’t have to wait in line!
