Pros and Cons of C-Store Managers Using Data Analytics

Image of boy considering Given c-store staffing issues and inventory shortages, there are many pros and cons of c-store managers using data analytics.

Given c-store staffing issues and inventory shortages, there are many pros and cons of c-store managers using data analytics. Some argue that there is absolutely no way to ask store managers to do anything except to just keep the doors open. Hence, it can be scary to add another task to store managers for fear of data overload. Others argue, the c-store manager is the most important area to support and develop in these challenging times. That is, reducing c-store manager overload happens from the use great analytics. For them, adding this new job and skill saves them time and reduces their overall workload.

Key Areas Impacting C-Store Retail Data Analytics

While there are many reasons, operators frequently list one of these five reasons not to use c-store data analytics. It is not surprising that c-store operators are working long hours and have senior staff spending time in the stores working. Lots of interviews, lots of training, helping with orders, and even working registers.

With all that extra work and the stress of staff shortages, many operators just do not feel they have any time to pause and think. They list these five reasons most frequently as to why data analytics is just not a good idea:

  1. It will overload our managers
  2. Using the wrong numbers will make things worse
  3. It is not that effective compared to in-store observations
  4. I do not trust data analytics results
  5. Confrontations may be necessary

Many will explain their rationale as follows.

1. Overload

Area and store managers have a busy day. Their experience tends to be with people, vendors and customers. They know how to read people and things done. Looking at reports, working spreadsheets and sitting behind a desk does not make the stores run well.

In addition to not wanting store managers away from the staff and customers, it is really hard to keep the good ones. They already have too much to do, so adding even more thing to their plate may just tip the balance the wrong way.

Turning a Negative to a Positive

In order to mitigate the con of c-store manager overload by using data, the correct retail data analytics must be utilized. A key way to make a positive impact is to limit the amount of data and to prioritize insights that have high impact. On the pro side of using data in c-stores is the ability to show high risk items and high value results. For example, a simple list of the top 3-5 cashiers that are not making proper identifications for age restricted items or a list of high risk transactions with for shrink are time saving.

When data is delivered with just a few key points including the time and person involved, it makes it easy to take action. Retail data insights into the specific behaviors that need to be addressed save critical c-store management time and provide a means to address a broad range of issues with little research time required. Giving the store manager simple items to address fits naturally into the oversight tasks. Further,  coaching priorities are identified without requiring a lot of time or training.

2. Wrong Numbers

If staff are given numbers without a clear explanation it can be very confusing. Worse, if the numbers cannot be tracked to the correct staff or products, it can provide a very objective assessment, but may not present an accurate causation. For example, if cashiers do not sign in individually, the sales or shrink may not match the person involved. If the price book is not organized well, the wrong items may show in the wrong categories.

The Con of Bad C-Store Data

Clearly, not having accurate c-store data is a big negative item when considering the pros and cons of c-store managers using data analytics.  In addition to using bad data, there is also the issue of focusing on data that is hard to control or manage. Sometimes, huge reports are emailed that are never reviewed. Such unused reports are the classic example of data overload. Managers deal with it my ignoring the reports which leaves them vulnerable to unseen and risky situations.

An example of picking the wrong items to track are things like lottery sales or fuel sales. While all sales can be influenced in some manner, some items are more directly influenced than others. Picking the your tracking items is important to differentiate the  local competition and market forces that the local staff have little or no ability to influence from those things like store cleanliness and upselling. Using numbers that staff and managers cannot influence and holding them responsible can be demoralizing and hurt team performance.

Avoid Tracking the Wrong C-Store Data

Using data that tracks process-related activities that are directly related to the key objectives of the store is the place to start. Overload happens when there are more things to address than what one can handle. Use your key objectives (e.g., items in the bonus plan, items in the supervisor summary reports) for tracking. Even better, identify the behaviors that lead to the desired results and track those items. In the book, “C-Store Growth Mindset”, Mason Cowan explains there are two types of accountability – process and outcome.

Once the key areas are identified consider the desired outcomes and seek to find the key activities that lead to the objectives. Using both types of measurements provide guidance as you work (process), while also ensuring the process produces the desired results (outcomes). The combination of both tracking numbers helps to guide work and also to improve the work process. In summary, use a limited set of operational areas for focus.

Get Buy-In for Tracking Goals

Even when the right numbers are used, if they are not explained they can have the same impact as wrong numbers. If  the staff is not involved in helping to set the scoring system or it is not explained, there can be misunderstanding or resentment. They may feel that the numbers are taken out of context. For example, there may not be a way to communicate why their situation is unique or different. Stores have different sizes, staffing levels, customer demographics that numbers may not address fully.

3. Human Touch is Needed

Data analytics is not that effective compared to in-store observations. Even if the numbers show results, the only thing that impacts performance are the behaviors in the stores. There is only so much time. no one wants to have data overload. Managers should spend their time in the stores watching and coaching, rather than in the office studying the numbers.

Irony of Objective Data

One of the surprising benefits of using retail data analytics is the way it can increase time for coaching. By having precise counts and examples of great results or poor results easily available, c-store managers can move directly to high impact feedback. Of course using data well can be both a pro or a con for c-store management. If used only to punish, it will not improve a management relationship.

However, by seeing both the good and the bad objectively, meetings are focused and fact-based rather than arbitrary and favorites-based. A good manager uses the data to show a new staff member  their growth as they are trained (transactions handled, upselling accomplished) compare to more experienced staff. For experienced staff, the results provide areas to review and analyze.

By pinpointing operational issues (e.g., lower sales, too much shrink, inadequate age verification), store managers know who needs attention. It makes c-store interactions more personal and direct. With specific results it is easier to ask targeted questions and make suggestions. There are more opportunities to meet with staff to identify with new ways to handle difficult customers or bad service  such as long lines, missing items, or high prices.

4. Lack of Trust

The people preparing the analytics may not understand the business or even calculate the data correctly. Worse, the data may not be available to address issues early so the analysis only brings problems after the fact. It can be used to bully or catch mistakes so that store staff feel blindsided. It can make staff feel they no one takes time to listen to them or even understand issues, but just sends numbers and expects better results regardless of the situation.

Expectation Overload

Many times staff feel like they cannot win. If the numbers are good, the company is great and if things are bad it is there fault. Worse ownership may worry that making small improvements that are the result of better branding and upkeep will empower the staff to ask for bonuses that they have not earned.

C-Store managers can feel like they are in a no-win situation. When this happens it is easy to have manager overload. Ownership and leadership must lead by example. Expecting store staff to perform well and have trust comes with trustworthy leadership from the top down. Using c-store retail data is a tool to help everyone win. With teamwork and alignment, data (even for bad results) becomes a useful tool to make things better.

5. Confrontations May Be Necessary

The numbers sometimes paint a bad picture that requires action.  Consistent late attendance, low upselling, inadequate age verification or excessive shrink activity limits success. With labor shortages, it may just be too hard to address staff short-comings, yet having numbers may make it hard to overlook.

Store managers must ensure basic operations are met. Balancing the need to keep staff working, making profits and not expecting too much is hard. The benefit of c-store managers using data analytics is the time it saves and justification it provides. Rather, than expecting excessive in-person or video monitoring, now the c-store manager can simply address those top priorities that are identified. It magnifies oversight and reduces the burden of deciding which issues cannot be avoided.

When the Pros Outweigh the Cons of C-Store Data Analytics?

C-Store Data Analytics is not for everyone. It does take time to review even the best analysis. Of course, there is the expense to buy tools or pay someone to create the analysis. Further, it requires looking at things objectively. Before embarking on a new approach to use data, it may be wise to talk with others. Given the pros and cons of c-store managers using data analytics, take time to consider key staff to help analyze the benefits and provide guidance on the best way incorporate a new management tool. Building support as well as having peers to explain the reasons for a change helps with motivation. Retail data analytics are here to stay, but do not ignore the human aspect. Allow some time and team input to help to maximize results and reduce the negative impact of the change.

Involve your key staff and set fair and reasonable expectations. Of course, make it safe to take a bit of time and even make some mistakes. It takes a little practice to use analytics, but it pays off when done well.

Research C-Store Retail Analytics

  1. For real life examples, join c-store manager workshops for using data analytics to avoid manager overload.
  2. Hear from leaders of four chains that use data analytics in a recorded conversation:  C-Store Growth Webinar with John Lofstock of CS Decisions.
  3. Read more about setting goals in our blog – Begin with the End in Mind.
  4. Watch C-Store Manager Best Practices and Tips Videos