Who would restauranteurs trust with the manager code or swipe card when they are away?
Making such decision seems natural for many businessmen and women. However, the restaurant industry possesses a singular fixture that makes such a decision very difficult. The sector shows the highest turnover rate in the nation. That means people come and go twice as much as the national average among all industries. At this rate, everyone is stranger all the time. Then, if you only get to know people for a short time, what criterion would you use to decide who to give a POS swipe card? The answer is data.
My client in the NYC metro area faced that dilemma recently. Overwhelmed with purveyors, payroll, and bills, he needed to delegate some responsibilities to ameliorate the burden of running his restaurant. When he went through his staff list, he realized all of them were nice, kind and professional to some degree. It was hard for him to pinpoint the right person and be sure that he or she was the correct one. At the time, Econometricus.com had been helping him and the chef with menu development when the dilemma was brought to our attention. The owner had trusted other employees in the past by using his mere intuition. Consultants at Econometricus.com did not want to contest his beliefs, yet, we offered a different approach to decision making: we asked him, why don’t you look at your POS data. As he said his decision comes down to trusts, we noted trust builds upon performance and evidence.
Right after that conversation, we downloaded data comprising servers’ transactions from the POS. We knew where we were heading given that our Server Performance program helps clients to identify who performs, and who does not perform precisely. The owner wanted to give the swipe card to the most average user. We looked at the Discount as Percentage of Sale metric and came up with a graphic description for him to choose. The first thing he noticed was that one of his previous cardholders had a high record discounting food, Benito. The owner stressed that Benito was a nice, generous and hardworking guy. We did not disagree as to Benito’s talents; however, we believe that Benito can be generous with his own money, not the restaurant’s resources.
Once he got disappointed with Benito’s performance, our restauranteur was presented with two choices, either he would give back the swipe card to Benito and oversee him, or he would choose among the employees that were around the average. He agreed one more time to make a data-driven and fair choice.
After graphing the data, the selection process narrowed the pool to four great servers. All of them looked very similar in both personality and job performance. The owner’s next suggestion was to flip a coin and see who wins. Instead, we proposed a more orthodox approach to decision making: the one sample student t-test.
We told the restauranteur, the criterion will be the statistical significance of their discount record when compared with the arithmetic average. The score closer to the arithmetic mean would win the card. We shortlisted Heath, Borgan, Carlos, and Andres as they stood out the rest of the staff who looked either too “generous” or too “frugal.” Among those four servers whose discounts scored within the 7% range, we run the t-test to see if there were any significant differences from the staff average. Heath’s score was not statistically different than the staff average. When compared her score with the mean, her p-value was higher (0.064) than the .05 threshold we set for our significance level. Thus, Heath was the first eliminated from the shortlist. Borgan was next, and his p-value was 0.910. Borgan was within the range and classified to the next round. So was Carlo with a p-value of 0.770. Finally, Andres got a p-value of 0.143.
At the end of the day, there was no difference among the shortlisted candidates. The next step relaxed the threshold to .07 significance level. Following this more relaxed criterion, Heath’s p-value disqualified herself, and we could cut the list down to three finalists. With three shortlisted candidates, the restaurant owner was able to make his first data-driven decision.
Categories: Statistics and Time Series.
Awesome use of stats in a non-traditional setting!