Sports is different. You hear this refrain constantly from Sports people. They mention it when they talk about the tactics in other industries and why they won’t work in Sports. When I was on the Brand side, it was no secret that Sports was a decade or so behind when it came to technology and data strategy. This is what drew me to the Sports industry and had me excited about the possibilities for change. However, as I tried to implement change, I was often met with resistance. Much of the resistance stemmed from an inability to clearly define the effective application and outcome of using Brand methodologies in the Sports market. Simply because “Sports is different.”
However, the more I familiarized myself with Sports, the closer I looked, the more I saw similarities from my previous life. On the surface, Brand marketing strategy and Sports technology and data strategy may not seem to have many commonalities, but in looking deeper, they do.
Different Industries, Same Goal
Brand marketing utilizes different data sets, different methodologies, and different tactical marketing executions to drive revenue. In Sports, a comprehensive technology and data strategy uses different data sets, different methodologies, and different technological executions in order to drive revenue. At their cores, both Brand Marketing and Sports technology and data strategy have the same goal. The tactics are the same as well. Grow your potential consumer base, understand and market to your potential consumer base, and convert that potential consumer base to brand loyalists.
Brand marketers use Awareness, Sentiment, & Purchase Intent as the three main metrics to measure the effectiveness in achieving these goals. Sports teams benefit by capitalizing on these three proven metrics to design a technology and data strategy that delivers results.
Grow Your Universe
Awareness – From a Brand perspective, building awareness (both aided and unaided awareness) is at the top of the marketing funnel. The idea is to increase the number of people who are aware of your brand through marketing tactics. In Sports, “Awareness” as part of your technology and data strategy is tactically increasing the size of your database. This step is usually accomplished by purchasing lead lists, outreach campaigns, and/or geofencing different locations. The price tag for accomplishing database growth/Awareness ranges from free (inbound activity) to six figures (geofencing multiple locations). While important, it is easy to overspend when it comes to this piece of the technology and data strategy. Especially if you have not mined your current database efficiently which most sports teams have not.
Make Them Love You
Sentiment – Sentiment analysis is the middle piece of the marketing funnel, and where marketers attempt to increase the positive sentiment of the Brand. This is normally the purview of the Consumer Insights division. Their job is to “know the consumer” so that marketing and product strategies are tailored to the consumer’s lifestyle and product use cases. Sound familiar? It should since this is where the bulk of analytics and technology companies in Sports focus.
Sports teams want to know everything about their fans and have that information in one place. Hence, most technology and analytics companies in Sports focus on helping Sports teams do one or more of the following:
- Gather data on their fan
- Centralize that data
- Visualize that data
The “Sentiment” piece of a technology and data strategy is more important than the “Awareness” piece. If you are more efficient at understanding and knowing your fans from a messaging and product standpoint, then growing the database becomes less of a priority. Most Sports teams know this, and as such sink the majority if not all of their technology and analytics budget into this piece of the strategy.
The idea is that if you have happy fans, then it is easier to convert them to loyalists and ultimately convert them to multi-game ticket purchasers. However, much like the Brand marketing funnel, this is only the middle piece of the technology and data strategy. If you have stopped here with your focus, you’re missing the most important piece.
Get Them To Buy
Purchase Intent – The most important piece is “Purchase Intent”. It does no good to have high “Awareness” and high “Sentiment” levels if you cannot convert those to purchases. Brands know this. That is why they invest in Shopper Insights divisions as well as Consumer Insights divisions. The mindset of a person that is shopping is different than that of a person that is consuming. Shopper insights focus on understanding the factors that influence buying behavior, so marketing teams understand which tactics to use to push a shopper to a buyer and ultimately drive revenue.
Brands also understand neither demographics nor lifestyle define shopping behavior, occasions do. The same consumer will shop for multiple different types of occasions and therefore their needs and mindsets change depending on which occasion they are shopping. Methodologies utilized to understand shopper behavior and purchase intent include behavioral segmentation, ethnographies, purchase data analysis, etc. This is also where the majority of “push” marketing (direct selling, packaging design, point of sale displays, etc.) take place.
From a Sports standpoint, the “Purchase Intent” metric is the most difficult to replicate. Shopper insights tend to focus on multiple purchases over time whereas ticket shopper insights try to focus on a single purchase. Also, shopper data changes constantly which requires an automated data infrastructure that can update insights in near real time. This explains why few teams (and fewer analytics companies) focus on this piece of the data strategy and instead try to correlate fan “Sentiment” with “Purchase Intent”.
Machine Learning and Artificial Intelligence is Key
Statistics 101, correlation does not equal causation. While correlating “Sentiment” with “Purchase Intent” can be marginally successful it is extremely inefficient and expensive. Automated machine learning and artificial intelligence helps solve this problem for Sports. These disciplines predict the likelihood of someone purchasing a product, determine the factors that influence buying behavior, and identify when someone is in a buying mindset.
In Sports, a machine learning and artificial intelligence tool with the technological infrastructure that allows for automated ingestion, a data lake environment suited for advanced statistical analysis, and hierarchical models predicting likelihood to purchase is the best way to understand “Purchase Intent” and ultimately drive sales.
Sports might be different from the Brand world from a product, sales cycle, and competition perspective. However, from an analytics, data strategy, and technology standpoint they are the same. The same methodologies can also be used to achieve the same goal – Revenue. The application may not be clear intuitively. But they are there. You just have to be willing to look deeper.
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