Using Analytics To Determine Seat Value
Understanding seat value is understanding the role of data analytics on venue sales.
As data grows, so does the opportunity to decide truly how to use it. Right now, data is collected, but rarely is it used properly. That isn’t just the fault of some tech-head who built the C.R.M. either. It comes down to whether the actual franchise and its executive staff fully embrace, as well as know, what they should be doing with the data that they compile. Otherwise, it is gathered, rots, and is useless far more than it is useful.
Yet, data gurus rarely get into this kind of discussion. They’re still in the phase of getting everyone to collect the stuff, rather than actually having an actual use for it. This is an ass-backwards approach to the matter, and may be stopping a lot of sports franchises from even seeing a need to collect data that is readily available for them. Until the money is shown at the end of the rainbow, the investment won’t be fully made, nor embraced, by the franchise itself.
So, let’s start by reverse-engineering the entire process. Instead of asking what data to collect, we should be searching out what we want to collect it for. Right there, I’ve probably helped break the internet, which is so used to asking the question, rather than being determined to have an answer that creates the question.
Engaging Seat Value Estimations
That’s where I come up with the term “seat value” as a data rating that I want to discover. And not just any type of seat value, but I want to know about everything that goes on inside of that individual seat, or each individual seat, within the entire sports venue. That means that each seat capacity will determine various things for me, including how much revenue it gains for the franchise, how many times it was used per season, and whether the price achieved by the customer to obtain that seat was at the maximum or close to it (this is where dynamic pricing may reveal a kick in the shorts more than it should).
If we are to go even further, that also means examining whether or not two seats neighboring each other are gaining equal amounts of revenue. Think about that scenario for a moment: The idea that one seat is outgaining the other, even though they are right next to each other. Any ticket director worth their salt is leaning forward at the suggestion. That means that somewhere, within the scope of the season, analytics is revealing that one seat is being oversold while the other is being undersold.
Between Big And Little Data
The scope of big or little data is less important than what it actually tells us about revenue generation or the lack thereof. Often, we are focused on the customer profile, in order to reveal what the larger mass of information shows us. But few times do we focus on how that data is used, within the venue itself, to unveil exactly how each component of the venue is being sold or utilized. By looking at the entire venue as a living, breathing component of data, it allows the analytics professional to sift through the noise and discover the signal (al la Nate Silver).