Audience Analysis Explains One Reason Why TCS Will Spend $280 Million On Racing Partnerships

BY ADAM GROSSMAN

Tata Consultancy Services (TCS) announced this week that it is making a reported $280 million investment in racing that includes remaining the title partner of the New York City Marathon through 2029. This TCS investment builds on findings we first explored last year when we explored how partners can best activate racing partnerships.  

The pandemic had a negative impact on racing with several events canceled, including the New York City Marathon last year. Yet, some racing events were still able to generate partnership revenue because runners were still able to engage (and were arguably more engaged) with the sport both outdoors (i.e. running on trails, paths) and indoors (personal fitness equipment).

A Front Office Sports post highlighted the success of the Ironman Group’s Virtual Racing series that launched at the beginning of April 2020. In particular, Ironman was able to successfully integrate sponsors into these races such as shoe brand HOKA ONE ONE. Why were the virtual races “a very important series to HOKA?”

HOKA ONE ONE global director of sports marketing Mike McManus stated, “Triathletes found Hoka before Hoka started to invest in triathletes. We have the opportunity to connect with all those consumers. We also can, through our athletes, offer advice and training programs, so through social media, through these virtual races, we now have a platform that we can continually speak to and connect with our consumers.”

TCS is clearly in a different phase of its company lifecycle than HOKA. TCS is both owned by the conglomerate the Tata Group and has a market capitalization of approximately $159 billion. 

The benefits that HOKA is generating through the partnership with Ironman now sounds both similar to how partnerships were delivering value to companies before the coronavirus pandemic and should align with TCS revenue and brand goals. More specifically, brands are able to engage with customers (in this case new customers) in ways they could not achieve on their own.

The Block Six Analytics Audience Inference Platform (AIP) helps to prove this point. AIP uses natural language processing (NLP) to determine the keywords that followers of an account disproportionally talk about in organic posts on Twitter. We examined posts from the followers of the @IRONMANLive account for posts both relating and non-relating to Ironman in 2020.

Detailed below are the top results for English-language keywords from this analysis. The higher the score means the more @IRONMANLive uses this word in posts as compared to the base population.

1.        swam

2.        calorie

3.        miler

4.        nike

5.        pour

6.        badge

7.        strava

8.        physio

9.        brownlee

10.     dublin

11.     diary

12.     leeds

13.     noch

14.     murphy

15.     ontario

16.     altitude

17.     triathlon

18.     completed

19.     ironman

20.     lockdown

AIP showed that the first coronavirus related topic with “lockdown” was the 20th highest rated keyword. This means that @IRONMANLive followers were more focused on performance / competitive topics than on the coronavirus based on what they are saying.

These results demonstrate the importance of looking at organic conversation. In this case, AIP provides evidence that racers were interested in performance and technology even at the beginning of the first wave of the pandemic (when conversation around COVID-19 would be close to its peak).  

In addition to keyword analysis, AIP leverages NLP and B6A’s proprietary algorithms to determine demographic profiles of audiences. Our research has found that racing accounts disproportionately reach upper income and more highly educated demographics which are more likely to be TCS target audiences.

Reaching the right people at the right time with the right message in ways that drive engagement is likely one significant reason that TCS is making its racing investment. This type of analysis is a feature of AIP and machine learning more generally as compared to survey work. We can examine what the followers of multiple sports accounts are saying and determine the best way to communicate with each audience based on their organic conversations.