This is a great time of year. Both spring and spring training are just around the corner.
Even though my Oakland A’s are forecast by most pundits to finish dead last in the American League, spring is reserved for optimism.
Applying analytics to baseball
When people think of the Oakland Athletics, they aren’t picturing their killer fluorescent yellow uniforms or Eck’s handlebar moustache. The more commonplace association people make nowadays with the A’s is Moneyball.
The term was coined by Michael Lewis in his book, “Moneyball: The Art of Winning an Unfair Game,” and further popularized by Brad Pitt in the movie adaptation. The story explains how the small-budget A’s were able to consistently compete against teams with much higher payrolls while paying significantly less per win than other franchises.
The book highlights a couple of fundamental practices that led to the A’s surprising success:
- The prioritization of data over scouts’ intuition
- The refinement of traditional baseball metrics, like batting average and RBIs, into more relevant and insightful metrics, like on-base percentage and wins above replacement.
The ability of A’s General Manager Billy Beane to create a contending team despite a limited payroll has garnered a ton of attention both inside and outside of the baseball world.
Since the late ‘90s, baseball has undergone a revolution of sorts – franchises have invested significant sums of money in building full-blown analytics teams. Positions traditionally filled by grizzled baseball lifers are now being occupied by MIT PhDs.
Beane has gained a reputation as not just a keen baseball mind, but as an expert in analytics and optimization. He has been a member of NetSuite’s board of directors since 2007, and garners $60,000 to $70,000 per speaking engagement for corporate events.
Applying analytics to sales
It has become commonplace to talk about applying Moneyball principles to myriad industries. Everyone is looking for increased efficiency and an edge over the competition.
When I took over the inside sales team at my previous job, I had visions of sophisticated data capture and analysis. I wanted to be the Billy Beane of wholesale commercial insurance.
It looked to me like my sales reps were using a model similar to the old-school baseball scouts: intuition and anecdote, rather than data and analysis.
It soon became obvious that my reps were in an even worse position than the scouts. At least the scouts were using some data, albeit imperfect.
We had nothing.
There was a ton of painful work ahead of us.
This might sound familiar to you. In my current capacity with XANT, I see many sales organizations and interview a lot of sales reps.
In my experience, there are too many sales organizations that don’t even have “batting average” equivalent metrics, or if they do, they can’t trust them.
This should be a pretty encouraging revelation because, in this environment, small adjustments can make your organization vastly more competitive and efficient.
Here are a few things to remember:
1. Even a little bit helps: Remember that you are probably not behind the curve. It can become discouraging when you have visions of what your organization should be, but it seems light years away. Take the process one step at a time and the results will come.
2. Accuracy: My colleague Kristin Trone recently wrote about the importance of clean data. This isn’t glamorous, but it’s the keystone of any data project. Clean your existing data and build a process to ensure accuracy moving forward.
3. Simplify: Define KPIs that are “must-haves” and work on capturing, regularly reviewing and leveraging that data. Once you have this under control, you can begin worrying about more sophisticated lead prioritization.
4. Iterate: Don’t rest on your laurels. Keep evaluating and be creative. This process does not always mean adding data points; often it means replacing or removing them.
I never reached my imagined “Moneyball” state with my inside sales team. However, the initial efforts to get there yielded returns every step of the way.
I now work for a company that believes in the power of big data and machine learning. Again and again, I get to see enormous lifts as companies transform into Moneyballers.
You already know you can and should do more with your data. Remember, this spring is a time for optimism. So lace up your cleats, put on the batting gloves, and get to work.
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