By Don Gray
Wikipedia defines analytics as the discovery and communication of meaningful patterns in data and also categorizes different types of analytics such as descriptive, predictive and prescriptive analytics. For those interested in the science around analytics, the above descriptions are a good starting point on a somewhat complex subject.
Human beings are naturally analytical; we are constantly absorbing data, evaluating what it means and adapting to this information in a wide variety of ways. For example, I enjoy foie gras but after reading about cholesterol and potential health issues, it seems I may need to significantly reduce my consumption. How did I reach this conclusion, and more importantly, what am I going to do about it?
I read an article by someone who appears to be an authority on the topic of health (descriptive analytics) who has some relevant facts and figures from research being done on fat mice.
I do more research on the topic and read more articles that apparently validate this information and this data clearly outlines what will happen if I continue to eat foie gras frequently – I think the mice had to eat it every day (predictive analytics)
It appears clear to me I must reduce my foie gras input. I diligently prepare myself for self sacrifice (I really like foie gras!) (prescriptive analytics)
Now all of this is primarily done in my head, of course, but the fundamentals around analytics involve using large amounts of somewhat unrelated data to understand, predict and act without necessarily having perfect information. Our brains are very complex computers capable of doing analytics on many topics at the same time. I still eat foie gras but I have determined based on the available information (descriptive) and my lack of will power (predictive) that I will indulge a few times a year and not fear for my life (prescriptive). The fact is, I knew this well before I did all this research and I really don’t eat much foie gras but because of that article I felt compelled to continue down the path to validate what I already knew – eating foie gras every day is not good for my heart.
The search for the unknown
How does this relate to using or not using analytics in your foodservice operation? All profitable operators are using natural analytics similar to those used to evaluate my consumption of foie gras. They know what they need to know already, but often have a need to validate this knowledge. Simple reporting can cover most of this need but I think many successful operators also hunt for the unknowns — they are trying to discover what they don’t know.
I believe one of the biggest mistakes many of us make when running a business is developing processes and reporting that validates what we already know. We build processes, systems and reporting around parameters and metrics we believe to be the ones needed to run our businesses, tried and true methods learned in business school or on the job. This is necessary, of course, because it is important to make sure your fundamentals around costs and revenue forecasts are legitimate. However, what about other things that may be important but hidden from view?
Hidden variables
Based on our experience dealing with thousands of foodservice operations, I would like to postulate a few of these hidden variables:
- Consistently cold entrees from location No. 1, but only on Fridays and Saturdays;
- High turnover in location No. 3 but only in the last two months at lunch;
- Reservations being lost and tables double booked at locations 3,4 and 5;
- Food costs on meat higher than normal in location No. 6 but only Saturdays;
- Customers unhappy with service at location No. 13 during lunch and dinner for last month;
- One of your unique menu items is no longer selling as it used to but nothing has apparently changed.
If the restaurant operations are showing food and labor costs in line with the enterprise’s budgets, it may be difficult for an owner or manager to be motivated to hunt for some of these unknowns, but it is clear that ignoring issues such as these could be dangerous for the long-term health of the business. Most companies will rely on their local manager to observe and detect these hidden variables and act on them and, frankly, that may be all that is needed if profitability is maintained.
Bring in a specialist
It is clearly impossible to check for every conceivable issue faced by a foodservice establishment and look for trends. However, if the operator sat down with his team and a data analytics specialist, I believe it is possible to build a series of tests that will act as a yellow canary for possible hidden endemic issues.
These tests can take the form of “gamification” or surveys in real time by customers as part of the bill-paying routine. The bill could also be presented on a small tablet that includes a small survey; the survey is designed to be quick and easy and anonymous or tied to a loyalty member. There may be 20 questions you could ask but probably only 5 at a time and the questions are random. The responses to these questions are designed to be numbers so the data can be easily loaded into an analytics engine that pulls in sales, costs, staffing, weather, location, demographics, etc.
How does this help the restaurateur? It helps the operator identify what he or she needs to know about his or her operations. If everything is perfect, then likely they know everything they need to know and have a firm method in place to find and fix anomalies that could be the first sign of unexpected change.
Gathering relevant data and getting it into a database that can be queried in a wide variety of ways with many different views is not an easy task. This requires close coordination of the POS system, time keeping system, inventory system, customer data and a flexible survey tool. With the advent of cloud based POS systems and more cost effective analytics tools, the ability to build useful analytics templates in now within reach of the smallest chain operators or large multifaceted single location operators.
Know what you need to know.
About the author:
Don Gray is CEO of Givex Corporation, a global Information services and technology company offering clients cost-effective gift cards, omni-channel loyalty, analytics and cloud-based POS solutions. Givex helps small, medium and large merchants build, understand and effectively use their ever-expanding data.
