In the previous blog, we had touched upon “How Analytics is influencing player and game performance”. In this blog, we aim to elaborate on the business-oriented applications of Analytics in sports. Some of the main applications from business point of view are
- Analytics for ticket pricing
- Fan Analytics- Fan engagement and sentiment analysis
- Social Media Analytics
Let’s look at how analytics is playing a part in all these applications and how some of the franchises are cashing in.
Pricing tickets using Analytics:
With team franchises paying huge salaries for players on their roster in addition to their travel and maintenance costs during the tournament, the costs are very high. So team franchises look to improve their revenues in the form of ticket sales, by selling merchandise, or by advertisements. Ticket pricing is one area where franchises can improve their revenues by applying analytics. One of the most common approach in analytical ticket pricing is variable pricing. Variable pricing is a practice of charging different prices for the same seat depending upon the game. These prices are decided before the start of the tournament and they remain static throughout the tournament. Several teams in National Football League (NFL), USA are planning to adopt variable ticket pricing.
Second approach and most important approach in ticket pricing analytics is dynamic ticket pricing. In this approach, prices vary as the tournament proceeds based on the factors like team performance, rankings, day of the week, position of the match and stage of the tournament (group match, semi-final or final match etc.) Put simply, with dynamic ticket pricing, tickets that are in high demand will be sold for more.
Predictive Modeling and Optimization Techniques can take in numerous variables around the fixed and variables costs, team performance, royalties and other income streams, target contribution and an estimate of the fans’ ability to pay to generate a stream of ticket prices that than can be deployed through the dynamic pricing mechanism. This will effectively leverage the interest and enthusiasm in the team and will allow for the expected ebb and flow as well.
If you follow Major League Baseball in USA, you are bound to know the San Francisco Giants. This is the first team to employ variable pricing for tickets. It has cleverly implemented this technique by employing this pricing for 2000 seats per every game. This experiment is a very big success for team bringing additional $500,000 revenue. The Giant’s ticket pricing model has included 120 variables while pricing the tickets.
Now, lets move to the second business oriented application of analytics; Fan loyalty analysis.
Loyal customers help generate the most revenues for any company and they tend to stick with the franchise even when the going is tough. So companies will do whatever they can to retain them by offering promotions or by treating them specially. The same applies for team franchises. Teams are now engaging their fans by providing personalized experiences in the form of personalized web content by evaluating fans past history. For true fans, the season lasts 24 x 7 x 365. Today’s best run teams are using fan insights to power a class of personalized, immersive experiences delivered on fans’ mobile devices, online, and in the stadium to bring fans closer to the action.
Analytics is also playing an important role in the personalization. For example, Major League Advanced Baseball Media personalizes digital ads and content on the fans’ websites based on inferred or furnished fan information. It is employing statistical tools to understand its fans and has recently adopted marketing analytics tools to place digital ads on the fans’ websites.
The other example of fan analytics is Facebook analysis on how fan support has changed during the recent football world cup.
With social media playing a crucial role in fan engagement, lets take a look at social media analytics in sports.
Social Media Analytics:
Sports fans use social media to share their opinions and feelings about their team and player performances on a particular day. Social media thereby offers a lot of data, which franchises can mine to understand fan sentiment and level of engagement. In Major League Baseball, the San Francisco Giants team is an aggressive user of social media analytics. They mine social media content to measure “buzz” and level of engagement around the overall team, individual players and on specific games. Brands are also using these insights to track the most followed players and those creating the most buzz to make sponsorship decisions.
Social media sentiment analysis is another such concept. The idea behind it is to analyze the tone of the tweets pertaining to the team. Post sentiment analysis, teams perform cluster analysis to segment the fans to better understand the types of relationships that fans have with their teams. In National Hockey League (NHL), teams have performed cluster analysis on social media sentiment and come up with specific social media personality fan profiles.
|TEAM||Profile of the fan base based on the tweets|
|Anaheim||Likes their team forever and always|
|Boston||Likes & Sometimes love their team, but can get a bit crazy|
|Colorado||Sad with lot of mood swings|
|Detroit||Sometimes love and sometimes contentment|
Based on these insights about fan personality profiles, teams can target the fans effectively.
The above discussed areas are the three most important areas where business oriented applications of analytics are taking place. There are many other areas where analytics can concentrate and bring value to the stakeholders. It is up to the team franchises how they want to use the analytics to bring the revenue to them.
BRIDGEi2i’s analytics solutions portfolio includes predictive modelling, personalization, cutting edge social media analytics, compelling visualization and advanced optimization capabilities.
The authors, Upasana Gautam and Venkateswara Prasad, are Analytics Consultants at BRIDGEi2i, a company on a mission to unleash the power of analytics and transform the lives of enterprises and individuals alike. We believe that the solutions to almost all intractable problems lies in our ability to mine & contextualize data. We want to help organizations prepare for adversity of all types by embracing data driven calculated risk taking, as a way of life. To know more visit www.bridgei2i.com
The views and opinions expressed in this article are those of the authors and do not necessarily reflect the official position or viewpoint of BRIDGEi2i.