The first week of college football is in the books, and it was a wild and woolly opener that combined expected blowouts with shocking upsets. The early season is never predictable, but as we get deeper into the season, the playing field generally looks more even as teams meet their better-known conference rivals.
The podcasters and bloggers I follow make valiant efforts to make sense of these early season games, breaking down even the unlikeliest rivals and their rosters in exhaustive detail, and the discussion of home field advantage factors heavily into every prognosticator’s assessment. Home field advantage is one constant, it seems. One particular stadium has a loud crowd, another game requires a team to travel two time zones, and that place in Iowa City has a pink locker room. Consensus seems to indicate these factors matter.
A pink locker room awaits visitors to the University of Iowa’s Kinnick Stadium
The data disagree. Home field advantage might be a constant, but it’s no accurate predictor of success.
According to an analysis done by the folks at DataOmaha, it turns out that home field advantage in college football is a myth. Teams are more increasingly likely to win on the road, and home field advantage has nearly evaporated.
Yes. In the last two years, Power 5 teams won at home 50.8% of the time. In the Big10, the scales actually tip in favor of the road teams. What’s driving these trends? A lot, it turns out, and you can dig into those details here (College Football’s Greatest Myth) if you’re so inclined.
This post really is about marketing
But this post isn’t about football. It’s about how easily we cling to our assumptions, even as the truth lurks among the data. It’s also about how important it is to start synthesizing and using the data that surrounds the brands we represent. As marketers, we just have to look, and one needn’t work for a huge brand with vast resources in order to use digital data to inform and improve how the brand goes to market.
In short, this is really about the death of the way things have always been done.
The truths unearthed in the data unearth may upset some apple carts. It’s hard for people to accept proof that a long-held assumption (such as home field advantage) simply no longer holds water. I hate to sound like a bureaucrat, but in my experience, embracing these new truths generally requires some stakeholder education.
So how do you help people let go? Testing of new tactics is a good place to start. Create some controlled experiments or A/B tests against clear benchmarks, and see what happens.
Learn, and then adjust.
Here’s one example. I used to run social media and content for a former employer, and noticed significant differences in response rates between audiences on LinkedIn and Facebook. At the time, most of the content we were producing was focused on cutting edge industry trends, and how early adopters and fast followers could capitalize on new technologies.
However, our audience on LinkedIn wasn’t nearly as interested in those ideas, and our views, engagement and ultimately leads from that channel were dipping precipitously. So we dug into a couple of the leading industry-related groups on LinkedIn, each of which featured lively conversation and membership numbers in excess of 100,000. The analysis of the conversations there identified four topics around which the groups strongly engaged – and three of those were topics the brand wasn’t speaking to at all.
We created a new weekly feature on the blog designed to speak to one of those topics, and curated content on LinkedIn relating to the others. Within weeks, we noticed a spike in LinkedIn activity and followers, and lead flow subsequently improved. In fact, in the ensuing months, the growth in the KPIs I was watching for LinkedIn showed strong up-and-to-the-right movement.
This was a clear win.
Sure, the analysis took some time, as did adjusting our strategy to fit the data. But it worked, and we used the test data to inform an entire marketing arc, building out a section of related content on the website that was hooked into our lead nurturing engine. We successfully used an observation (differences in social audience behaviors), tested responses to the findings on one channel, and used that data to fine tune the acquisition strategy.
Importantly, the proof the experiment offered, by testing findings with “our” channel, our content and our people, gave stakeholders the confidence to pivot.
Get started by asking a good question
At this point, most of our brands have decent data sets available to them. Website analytics are two great places to start, and our marketing automation stacks are also a rich and important source of information. Counterintuitively, though, the data itself isn’t the place to start.
Before you start exporting sets and whiling away happy hours in excel, spend some time thinking about your business objectives and desired outcomes. Use these as context for your analysis, and then push yourself to identify the specific questions for which you’re seeking answers.
Unquestionably, art accompanies the science of data analysis – you have to be open minded enough to allow the data to suggest additional questions, but regimented enough that you don’t get sidetracked. It’s a balance.
In the earlier example, I didn’t set out asking the question “How do our Facebook and LinkedIn audiences differ?” The question I was asking was related to what content lead to the most productive leads, but then rapidly leads to questions about differences in audience make up, preferences and behavior.
Just do it
Failing to evaluate available audience and customer data is, in my mind, a mortal sin for marketers. Consequences of ignoring data are myriad and dangerous, to both brands and careers. Opportunities for marketing to contribute significant value are overlooked, and those opportunities carry costs in terms of time, as well as revenue. Companies that get it right generate competitive advantage, making life exponentially more difficult for their competition.
I like to think of the digital intelligence at our fingertips as the little blue dot on the Google Maps app on my phone. It’s constantly re-calibrating, alerting me to potential delays, and suggesting more efficient alternative routes. Sticking slavishly to the strategy plan penned a couple years ago and getting mired in “the way we’ve always done it” is like using an old atlas to navigate as you drive. It may not be up to date, and even if it is, you’ll probably miss a turn or two.
The DataOmaha study reveals that advantage on any given Saturday for our favorite college football teams is a function of a whole slew of variables, and those shift from week to week. If you’ve not done so already, find and debunk your brand’s “home field advantage myths,” and let me know how you fare!