Analyzing Analytics

Analytics is the one of the biggest buzz words in business right now, but what does it really mean? How do you use analytics? How does it add value?

What is Analytics?

Let’s break it down. Data analytics is the process of examining and assessing large data sets to find patterns, correlations, trends, preferences, etc. that help organizations make sense of their data in order to make data-driven decisions. At Fanalytical, our goal is to assess athletic department’s data to gain insights and find patterns within their customers’ ticket buying habits, donation history, and other relationships to University events.

What’s the Holdup?

Seems logical, right? So why aren’t more people doing this already? Data collection is half of the battle; integrating and finding connections in your data can prove daunting. Did you buy a ticket to a football game this season for your family, while your wife bought tickets for another game? Have you used your work email and your personal email to make a ticket purchase? Perhaps you make a spelling error filling out your address? Part of the challenge of data collection is understanding the source of the data itself. One of the first steps to data analytics is “cleaning up” the data. This involves removing duplicates, detecting errors, and correcting inconsistencies so that your data is uniformly structured. Oftentimes, this is a time-consuming task which may turn some organizations away. However, if you’re just “working with what you’ve got,” you’re not going to have a lot of success in the data analytics process.

What Now?

Having the data structured is not enough. You have to know what you want to do with; you must think about it strategically. Did I scare you off yet? Are you still with me? Good, this is where it gets fun—and profitable. How does data analytics add value? Data analytics is essentially a science experiment. At the risk of triggering bad memories from your high school science classes, I’ll break down the scientific process for you:

  • Make an observation: Season ticket sales are declining.
  • Form a question: Can we upsell some of our single-ticket purchasers to season ticket holders?
  • Form a hypothesis: Fans that have made ticket purchases to at least 2-3 games last season are likely to purchase season tickets for the upcoming season.
  • Conduct an experiment: You create an email campaign with a clear call-to-action that is highly targeted to the fans that fit within parameters of the hypothesis. Make sure to set goals for your campaign, such as click-through-rates (CTR) and conversion rates.
  • Analyze the data and draw a conclusion: You had a great subject line and exceeded your goal for open rates, but there’s a disparity in CTR and conversion rates. You notice that fans that had attended 2-3 conference games and at least one non-conference game had a higher CTR and conversion rate than the fans that only attended conference games. You can use this information as you move forward with the campaign.
Why You Need It

Essentially, data analytics helps you identify ways to form a 360-degree picture about your fans. With that knowledge, you’re able to target those fans and execute more successful campaigns. Rather than email blasting your entire mailing list, you’ll be able to come up with a specific offering that you know a tailored group of fans will actually respond to. As a result, well-executed data analytics will help you sell more tickets, retain and up-sell current fans at your games, and more.

Data analytics is like a map—albeit a coded one—leading you through a scavenger hunt. And trust me, the prize at the end is definitely worth it.

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