Five Reasons to Use C-Store Retail Data Analytics

Image of c-store. There are five reasons to use c-store retail data analytics. C-store operators use retail analytics and these five beneficial attributes to control the store performance, save time and increase profits.

C-Store operators that try to control the store performance often cite one or more of these five reasons to use c-store retail data analytics. They find that c-store analytics saves time and increases profits by:

  1. Reducing c-store manager bias in decision making
  2. Pinpointing c-store problems quicker
  3. Recognizing good performance of cashiers
  4. Reducing anxiety about the unknown
  5. Locating best practices

Reduce Bias

One of the biggest demotivators is having your work ignored and having the ‘favorite’ person recognized due to popularity or favoritism rather than on merit. Naturally, c-store managers have biases towards those that they like. Using numbers provides a ranking system that can be made without subjective evaluation. Of course, there are lots of ways to maintain a bias such has getting the best register or time slot. Nonetheless, using a comparison is objective and removes one aspect of rating results.

Pinpoint Problems

Retail data analytics can show the top and bottom performers, show the trends that have the highest change both good and bad and list the very top values for things like total sales, refunds or take rates. When used as comparisons with either cashiers, stores or even same store over time the top differences can be spotted easily. Naturally, a manager needs to know if results are changing or are much different than other similar stores or cashiers.

Instinctively, outliers make us curious and demand attention. Using data analytics consistently finds these outliers. Watch the top performers and ask them questions about what makes the difference. Then repeat the analytical review with the low performers to identify the things that the low performers can do like the top performers. Have the managers coach and assist to ensure the best behaviors are used.

Feel Good About Your Work

Doing work and seeing that it made a difference is satisfying. Trying something new – especially something that is hard or uncomfortable – and see things improve is very rewarding.

The risk of course, is doing something different and getting bad results. Great managers know how to set the expectations for growth without setting the expectation of perfect execution. Fear of failure can cripple learning and trying new things. Progress over time is rarely a straight line. Performance also may not stay better without monitoring the results. Using analytics can help spot coaching and training needs and help reinforce the changes that need to be repeated.

Given the challenge for c-store retention in the current market, many c-store managers focus on this feedback aspect of data usage. Such managers, site it as the most important of the five reasons to use c-store retail data analytics. That is, objective data makes the praise and positive feedback absolutely believable. Thus the good feelings are based on trusted outcomes. Everyone loves to feel good about their work.

Reduce Anxiety

For many, the fear of the unknown can be worse that knowing things are bad. If we do not know about something and we are trying new things, it can hard to continue. Worse, it is nearly impossible to fix a problem that is unknown.

With confidence in your team and the ability they have to do their jobs, knowing about problems can be a fun situation. it makes it easy to assign the important work. Even better, the ones that fix it get to feel the satisfaction of doing a good and important job.

Identify Best Practices

Knowing what practices produce the best results is important. Therefore, applying data analytics across stores and over time, those cashiers and stores that produce the top results are identified. Next, the best results are studied. Obviously, it is simple to visit and discuss the practices used to achieve the top results. Comparing the work approach from the best results to the lesser results identifies the specific actions that make a difference.

Using these differences to coach and train the other cashiers and stores, can then prove that the practices make a difference. If things do not get better, then the situation is different or the best practices may not be understood or followed well. Either way, there are more insights to guide the c-store management effort.

What Experts Say About Data Analytics

While it is not specific to the five reasons to use c-store data analytics, these leading management experts provide summary quotes as to the value of using data to manage. They like having objective ways to track and coach store performance.

  1. “In God we trust; all other must bring data.” William Edwards Deming
  2. “Accurate information is a key part of motivation.” Mary Ann Allison
  3. “We’ve got to use every piece of data and piece of information, and hopefully that will help us be accurate with our player evaluation. For us, that’s our life blood.” Billy Beane
  4. “No great marketing decisions have ever been made on qualitative data.” – John Sculley
  5. With data collection, ‘the sooner the better’ is always the best answer.” – Marissa Mayer
  6. “Data beats emotions.” – Sean Rad
  7. “Data are just summaries of thousands of stories – tell a few of those stories to help make the data meaningful.” — Chip & Dan Heath

For more information about retail c-store data analytics:

C-Store Scorecard Option

C-Store Growth Mindset: Making Peace with Accountability