What is "Moneyball"?
For centuries, Major League Baseball believed that the traditional experience and intuition of baseball scouts were paramount in player evaluation. It wasn't until the 2002-2003 Oakland Athletics bucked the trend by instead looking towards indisputable player performance data and evaluating players almost exclusively on their on-base percentage rather than deeming a player as "having the right stuff".
With wins most correlated to runs and runs most correlated to runners on base, the A's thought, "Why would we pay more for someone who hits singles and looks the part, when we can acquire a player who simply gets on-base?"
To put the “moneyball” strategy into perspective: the 2002 A's compiled a 103-59 record on a $41M payroll and went the distance with the $125 million payroll of the Yankees. Today, "moneyball" and its driving principal of sabermetrics is widely used across all professional sports in personnel evaluation and decisioning. Quite impressive!
Baseball and Hospitality:
For centuries, while Major League Baseball scouts were convinced that focusing on a player's ability to hit the ball was the leading indicator of player efficiency, data ultimately proved these scouts and the professional sports industry wrong.
Instead, we realized this:
- Scouts are a biased player evaluation source
- Player evaluation decisions were based on an incomplete and incorrect data set
- Professional sports teams were overpaying for the wrong talent and overlooking more effective talent at affordable costs
Within hospitality, mystery shopper programs are the industry's professional scouts in much of the following ways:
- Mystery shoppers are a biased evaluation source of a guest's experience
- Operational C-Level decisions are being made against an incomplete data set of 1-2 monthly reviews and an inaccurate data set
- Hospitality brands are overpaying for the wrong reviews and overlooking more
Using the Right Data:
Measurable change began happening for the Oakland Athletics when they committed to disregarding conventional thinking and making decisions based on an irrefutable data-driven approach. Soon after, the A's acquired players who were seemingly old, out of shape, and unconventional because their penchant to get on-base drove runs, which drives wins.
For hospitality brands, teams must begin focusing on the data sets that drive satisfaction, which will drive faster great visit frequency, which will drive revenue. First though, we must begin focusing on the following:
- Featuring guests as the premiere source for satisfaction data; the ones we aim to please
- Collecting 20-50 feedback submissions monthly per store to ensure statistical relevance
- Identifying and investing in Key Drivers that most greatly impact the guest overall experience, e.g. speed, friendliness, cleanliness, food, etc...
- Applying Key Driver approach to specific store groups in order to ensure guest needs who care about different things in different location areas are being meet
- Track, measure, apply iterative improvement; rinse and repeat
We allow our partners to align with a data-driven strategy in order to collect the right data from the right source and tailor a guest services strategy for targeted guest segments. Through a process of moving away from a conventional feedback process, hospitality brands can achieve their version of "moneyball" by using Tattle:
- 10-20X more feedback than mystery shoppers through mobile-first survey link
- 93% of feedback comes from guests within .5-mile radius
- Customizable Store Groups to ensure the right data for specific guests, e.g. Malls, Hospitals, Airports, etc...
- Key Driver determination to double-down an areas of guest importance for specific store groups
- 50-60% cheaper than mystery shopper programs