Data Analytics in Sports: Business Applications
Data analytics is a tool that has been around for many years, but sports is one of the industries that has taken the longest to adopt its uses. Analytics can be used both, on the Business and Sporting sides of a sports organization. In this article, we look at the business uses and how analytics can be implemented in CRM, Marketing, Sponsorship Valuation, and Human Resources.
First, what makes Data Analytics so important?
Analytics allows us to see patterns or insights that might not be detectable at simple sight. The whole purpose of Data Analytics is to use these insights to develop a product that is personalized for the individual and not the masses. Analytics can be used to develop CRM systems that store information about individuals and later be used, through marketing and fan engagement strategies, to build a stronger bond with consumers. It can also help us in areas such as Sponsorship and HR. Through the creation of different models, we can help obtain a more accurate valuation of a sponsorship or advertising campaign, and it can help us build a more efficient and productive workforce by providing us with information on who are the best candidates for each position.
Customer Relationship Management (CRM) and Fan Engagement
The implementation of a CRM system is essential for every business, whether it is in sports or not. A CRM system enables an organization to store detailed information about individuals from the first contact with the organization. Later, they can use this information to help increase revenues from their consumers and enhance the overall experience of each fan.
How does a CRM system work?
A CRM system requires a data warehouse that functions as a centralized, integrated database. It stores information about their customer’s demographics, purchases, member status, tastes, and other person-specific things. Then, organizations analyze this information and create content, messages, and offers, all tailored to each individual. To have an effective CRM system, it is important to obtain specific information. Meaning that the normal information about age, gender, location is not enough. Information about customer’s behavior, interests, and purchases (such as what type of merchandise does, he/she normally buys) is what will make the CRM system effective.
An important implementation of the CRM system comes into play when talking about Fan Engagement. Organizations can enhance the overall fan experience by extracting information about what they like, want, or how they behave and use that to design experiences that will suit and appeal to them. For example, the organization can predict what seats will be empty on game day and offer a seat upgrade to the customers that would most likely buy them at a discounted price. In this way, the fans receive a better experience, while at the same time increasing the club’s revenue from the seats that would otherwise be empty. Other uses include birthday messages or emails, milestone rewards, loyalty programs, and many other things that the customer will appreciate and make him/her feel more engaged with the organization.
Digital Marketing
As mentioned before, Data Analytics gives us detailed information about our consumers that otherwise, we would not be able to obtain. This information opens a whole new world of marketing possibilities, especially nowadays where a big amount of content is consumed digitally. The data obtained helps us understand better our audience. We can understand their behavior, attitudes, the latest trends, and even predict their actions; all things that can help the marketing team to comprehend what, when, and how is the best way to convey the message that they are trying to get across.
To better understand the relationship between Digital Marketing and Data Analytics, we can observe the four main categories:
· Audience data: the information obtained from the interaction of users with the organization’s platform and content, such as websites, videos, posts, etc. This type of data provides information about user’s interests, demographics, and devices.
· Acquisition data: provides information on how the user got to the content. What were the channels and marketing strategies that got him/her to the website, video, post, etc.
· Behavioral data: provides information on the actions and decisions users make on the website or other platforms. It tracks the amount of time spent on the platform, the number of clicks, what type of content is preferred, and any issues the user might have had. An important thing about behavioral data is that shows whether it is a new user or a returning one, and it also provides the organization with the bounce rate (the number of users that entered the platform and left without interacting).
· Conversions: provides information about the user’s actions that matter the most, such as, playing a video, buying tickets, placing an item in the cart, going to the checkout page, and whatever else the organization considers important to track.
With the rise of technology, especially social media, digital marketing has become increasingly important among younger audiences. Sports organizations can now reach a far wider audience than ever before, and they are starting to realize what a powerful tool it can be. Data analytics is providing sports marketers with information to develop strategies to engage consumers with the right content at the right time, allowing them to create more efficient marketing campaigns.
Sponsorship Valuation
Traditionally, one of the hardest things in Sport Sponsorship has been determining the actual value of the sponsorship campaign. Sponsors invest large amounts of money in these campaigns, and they never really know what their ROI is. In the past, sports organizations would communicate the value of the sponsorship through presentations in which they would explain how the activation happened and why it happened in that particular way. Everything was based more on “feel” rather than facts. As mentioned before, digital marketing is becoming increasingly important, and many sponsorship activations are happening through this channel. With the use of analytics, we can obtain real-time data on how the campaign is performing and achieve a much more accurate valuation.
How does the valuation process occur with Data Analytics?
To obtain the best possible valuation, the sponsorship is analyzed through three different approaches:
· Inherent Valuation: before analytics, this would just be the amount of capital that was invested in the sponsorship. Now, with analytics, companies can analyze the number of people that consume the sponsorship, often called the number of “impressions”.
· Relative Valuation: is based on ratio analyses. One very common ratio is the cost per thousand impressions (CPM).
· Comparable Valuation: It is done by comparing similar sponsorships within the team, league, etc. This one can be somewhat challenging since a lot of the information about other clubs is not public.
By implementing data analytics, the objectives of the sponsorship can be easier to identify as well. Let’s say that the purpose of the sponsorship is to expand the audience reach and increase the brand awareness of the sponsor. With analytics, organizations can track not only impressions, but also, the demographics of the people consuming it, the amount of time interacting with it, and the actions taken after consuming it. All things, that when making a final evaluation, help obtain a more precise valuation of the sponsorship.
Human Resource Analytics or Talent Analytics
The last application of data analytics we are going to look at is HR analytics. This type of analytics can be applied with both, the current workforce and with prospective candidates. The objective of its utilization is to develop a more productive and efficient workforce. As with the other applications, we can divide talent analytics into different categories:
· Human capital fact: analyzes turnover, employee count, roles, salaries.
· Analytical human resources: specific information about the different departments in the organization. This is used to identify productive and less productive workers.
· Human capital investment analysis: helps identify what actions, policies, and changes make the greatest impact in the organization.
· Workforce forecast: analyses and provides information about turnover, succession plans, areas that might need some help, and areas that might need to reduce their workforce.
· Talent value model: helps determine the level of employee satisfaction and the factors that contribute to that satisfaction.
· Talent supply chain: it’s useful when talking about the hourly workers. With it, you can predict the number of workers needed depending on the circumstances, minimize cost, and increase efficiency.
Conclusion
Data Analytics has immense potential and can be applied in many different areas. In today’s business world, sports organizations cannot afford to run without using analytics if they plan to stay competitive. The quality of insight this data provides can make the difference between a successful project and a failing one. However, there is still a lot of room for improvement; there is still some skepticism and other obstacles that difficult its implementation. As Data Analytics becomes a more known field and schools start implementing more programs oriented towards it, these obstacles will gradually fade away and we will see analytics play a bigger role within sport organizations.
Important Source: “Sport Business Analytics: Using Data to Increase Revenue and Improve Operational Efficiency” by C. Keith Harrison and Scott Bukstein
