‘Big Data’ Vs. ‘Traditional’ Marketing Research In The Sports Industry

In his December posting, SLRG’s Jon Last speaks to the impact that Big Data is having on marketing research in the sports space.

The adoption of advanced analytics has made an unprecedented impact in the way that general managers configure the rosters of their teams. As a sports marketing researcher and one of those guys who admittedly began upon such a path by memorizing the statistics off of baseball cards as a kid, I’ve always embraced such an approach. But the fundamental question that emanates from such a reality is: To what extent does and should the big data phenomenon factor into the business side of sports organizations?

Clearly, dynamic tiered pricing has permeated the industry. We have also been fortunate enough to be involved in a number of other intriguing, but proprietary behavioral analytics projects that have accrued to better delivery of various marketing mix elements around an enhanced fan experience, but where does that leave traditional marketing research?

There’s been an ongoing popular debate among insights professionals regarding the utilization of behavioral data (BIG DATA) as a surrogate for or even a disruptor of traditional market research. The disruption argument gets the headlines, but it strikes me that the more thoughtful discussion should be about how the two complement each other.

Big data has become more popular as it becomes more personal and more accessible. With easier collection and cultivation of transactional information and online conversations, sports organizations are able to drill down and examine what the customer is looking for, as well as their buying habits. Golf facilities and sports properties can use this data to surface current trends, and recommend specific products to the consumer. Those in favor of big data believe that if we can track what the consumer spends, what they do and where they go, that this is superior to a survey or interview, which relies on recollection.

Yet, behavioral data does have its downsides. Quite obvious from the name, big data has extremely large files that have to be analyzed to reveal trends and patterns that reflect consumer behaviors. Big data can be so large and complex that it can become difficult to store, analyze, and transfer using traditional database and software techniques. It is also limited to those customers that readily provide access to personal information that can be linked to their transactions or other behaviors. One can also recognize that the big data approach is limited in that it gets at the what, but not necessarily the how and why.  It’s also somewhat self-selecting, rather than rigorously and representatively sampled, as good traditional market research can be.

Traditional marketing research focuses on identifying factors that influence the buying decisions of consumers. Data is collected through focus groups, surveys, one on one interviews, observational research, and intercept surveys. Through this, sports marketers are able to find out the consumers’ likes and dislikes. By conducting focus groups or in depth qualitative interviews, researchers are able to get a better understanding of the consumer and their emotional reactions. Moderators are also able to intervene, challenge the consumer, and ask any additional questions that they may have. Such face to face interaction also allows for the introduction of body language and projective techniques into the analysis, both of which yield insights that are masked or even falsely attributed, through other methodologies.

So, which one is better?

My school of thought is that the ultimate holy grail is to be able to meld and model behavioral data (BIG DATA) with attitudinal data (Traditional MR) thus enabling both modalities to work in concert to provide a more robust customer segmentation as well as better targeted and customized marketing communications.  With both behavioral data and attitudinal data working in concert, sports marketers can derive insights never before possible. The output is both descriptive and prescriptive, and can accrue to more efficient and better business decision making and evaluation.