One Picture Tells The Story

I’m not a physicist, as most of us aren’t. E=MC2 (an equation of mass-energy equivalence), is far too complicated for me to understand, and impossible for me to even discuss. All I know is that in 1905, Einstein wrote the equation not as a formula, but as a statement in the form of a sentence written in the German language, and later condensed into a simple formula that had meaning to the scientific community he hoped to improve.

The origin of the statement, ‘a picture is worth 1,000 words’ is lost to history, but its meaning is clear. I could write a white paper on how I came up with the graph that leads to this article, but it would not begin to explain the work that went into it. Its origin can be traced back ten years when I began to collect data from retail stores, and the idea came to me after comparing what I had found in articles written by scholars who had analyzed similar data such as mine, and came up with their own conclusions.

The ‘picture’ above is a graph that represents a typical category within a specific convenience store. What interested me the most, was that regardless of the category chosen, or the particular location of the store, the pie chart remained similar in all cases and in all categories. This gave me a base line to work with that represented the ‘health’ of a store.

As I thought of the organs that supported life, I realized that organs such as the heart, liver and lungs could be further broken down into human cells, in the same way that categories such as cigarettes, beer and snacks can be broken down into individual products.

When the body is 'okay', according to standards currently set by the medical profession, most of us don't seek medical advice unless certain conditions occur. No two individuals are exactly alike. Conditions that may be a mere inconvenience for me, may be fatal to someone else experiencing the same, and this is biased by the overall health of the individual. For example, if you have a history of heart disease, and you wake up with chest pains, you would probably call 911; whereas I, having no history of heart disease in my family, might just take another antacid tablet and go back to sleep.

So the condition we refer to as ‘being healthy’, is based on the standards set forth by the medical profession, and not so much by the individual. 

In the retail industry, the health of a business is often characterized by statistics supported by trade associations and publications. But in either case, what I determine to be indigestion, may very well be the onset of a heart attack, and my over-confidence in statistics might very well result in my death, IF I do not seek  medical attention for the wrong reasons.

We are constantly being fed the optimistic notion that the convenience store industry is growing and doing well. Yet the statistics being used to make this determination are ambiguous. Increases in gross sales and numbers of stores mean nothing if profit remains static.

The average profit in a convenience store has remained at about 2 percent ever since I started working with convenience stores in 1981. To the average retailer, making a 2.1 percent profit is assumed to be ‘okay’; when in actuality, the store might be capable of producing a much higher profit. While we may perceive we are doing well, we may actually be doing worse than other retailers in our area. Relying on statistics based on national averages can be misleading at best, and deadly at worst.

Overall, we know from the research of people a lot smarter than ourselves (later validated through our own research)  that the health of a retail business is considered to be 'okay', when 30 percent of the products are performing well, 15 percent are dead, with the remainder 55 percent being unprofitable, or at best marginal.  Anthropologically speaking, that would put us back somewhere in the middle-ages, with the life-expediency of a person, being about 30 years old. We possess technology to do much better than this.

By removing the 15 percent, depicted as the RED area in the chart above, you would think that we could eliminate the RED portion of the chart entirely. But that is not the case (see below). The conundrum we face is "what would we replace it with?" If we replaced it by increasing the blue area (the 55 percent), we haven’t done very much to improve our situation. In fact, we might make it even worse.

There is no reason we can’t strive for categories that look more the one below:

Here we have 75 percent of the products performing well, 20 percent being there to accommodate the occasional customer and make the store look good, and 5 percent that is dead and not producing at all.

The only way we can accomplish this is by closely monitoring the products that occupy the categories. If a product hasn’t sold in 90 days and it is not seasonal, it would fall into the RED area of the graph and marked for deletion. There will always be RED products, because even products that sell well at the onset, may fall out of favor with customers over time. The trick is to not let them lie there and stink up the store forever.

By examining the BLUE products and either replacing them with products that will sell, move them to the RED area, move them inside the store, or present them in a better fashion, some BLUE products could be turned WHITE, which will increase our “items that sell well” products and lessen those that do not.

This exercise can be accomplished simply by analyzing your transactional data from the POS. Whatever you do, don’t throw your transactional data away until it is at least 90 days old (longer if it is seasonal). Look for tools that analyze transactional data. I know they are out there, because we have one ourselves.

Please contact me if you have any questions about how the two graphs were produced. I will be happy to discuss with you, and help you to decide how you can implement a tool of your choice to track the products within categories, and to increase the profits in your business.