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Sports brands face a unique challenge in today’s digital age: how to stand out in a crowded market and deliver personalized experiences to fans who have an endless number of entertainment options.

The answer? Predictive data.

Predictive data offers insight into the behaviors, preferences, and likely future actions of fans, businesses, and communities. By leveraging predictive data that covers all aspects of life – both personal and professional – sports brands can make informed decisions that lead to better fan experiences, more effective marketing campaigns, and increased revenue.

In this blog, we’ll explore the actionable ways in which sports brands can use predictive data to take their game to the next level. Here are the four ways sports brands can use predictive data:

1. Customer acquisition and increasing individual ticket sales.

Sports organizations can use predictive data to gain insights into the preferences and behaviors of potential ticket buyers. This allows them to personalize messaging and directly target the consumers who are most likely to purchase tickets.

One specific example of how sports brands can use predictive data to improve customer acquisition and increase individual ticket sales is by leveraging data enrichment and analysis to identify the audiences that are best aligned with their brand. By appending third-party data, such as AnalyticsIQ’s 1,500+ PeopleCore consumer data points, to their CRM file, sports organizations can gain valuable insights into who their fans are, their preferences, and their decision-drivers.

Using this data, sports brands can conduct analyses to see which audiences, such as “Millennial Sports Fanatics”, are most likely to purchase tickets, and then create targeted and personalized messaging to reach those consumers. By building a model to identify new consumers with similar characteristics to their existing fans or segmenting their current customers, sports organizations can identify and target the right people with tailored offers and messages. Additionally, unique data points like “Spontaneous Spenders” can be used to tailor messages and calls to action, such as a time-sensitive offer.

In this way, predictive data can help sports brands increase ticket sales by enabling them to reach the right audience with personalized messaging and offers at the right time.

2. B2B engagement and maximizing relationships with local businesses.

Predictive data can be used to understand businesses and the people behind them. By linking an individual’s consumer profile to their professional profile, sports organizations can identify the right businesses and decision-makers to target with true personalization.

One way sports brands can improve B2B engagement and maximize relationships with local businesses is by leveraging B2B2C data, such as AnalyticsIQ’s Connection+ linkage, which accurately links an individual’s consumer profile to their professional profile. With this data, sports organizations can better understand local businesses and the people behind them, both as professionals and everyday people, enabling them to more effectively expand their B2B relationships.

For example, a sports brand could use Connection+ to identify decision makers with a passion for the sport or target price-sensitive CEOs with discounted group tickets. By gaining a 360-degree understanding of the individuals making decisions for companies, sports marketers can identify and target the right businesses and decision makers with true personalization. This approach can help sports brands build stronger relationships with local businesses, increase sponsorship revenue, and drive more ticket sales.

3. Growing fan diversity.

Sports brands can use predictive data to better understand the makeup of their fanbase, including factors such as ethnicity and language preference. This allows them to reach new communities and tailor offers, events, and merchandise.

Using predictive data, sports brands can gain insights into the ethnicity and preferred spoken language of their fans. For instance, AnalyticsIQ’s data can provide information on an individual’s likely level of assimilation and whether they are likely to be bilingual or unassimilated.

With this information, sports brands can better understand the makeup of their fanbase and cater to the language preferences of different segments of their audience. They can also tailor offers, events, and merchandise to better reach and engage with diverse communities.

For example, a soccer team in a city with a large Spanish-speaking population can use predictive data to identify fans who prefer Spanish-language communications and tailor their marketing messages accordingly. This can help the team better connect with Spanish-speaking fans and improve their overall fan experience. Similarly, a basketball team with a diverse fanbase can use predictive data to identify the languages spoken by different segments of their audience and translate their in-game announcements and advertising to cater to different language preferences.

4. Boosting fan engagement.

By analyzing fan engagement and loyalty, sports brands can identify ways to make in-event experiences more engaging, increase merchandise sales, and measure the results of their advertising campaigns.

One way to increase fan engagement is to analyze your audience’s shopping behaviors to optimize merchandising and target custom audiences via connected TV (CTV). For example, you can use predictive data to understand which merchandise items are most popular among certain audience segments, and then use that information to optimize your advertising and promotions for those items.

Another way to increase fan engagement is to improve the in-event experience. By analyzing fan interests and preferences, you can identify areas where you can make in-event experiences more engaging. For example, if you notice that a particular segment of your audience is highly engaged on social media during games, you can create interactive experiences that allow fans to share their experiences on social media in real-time.

Finally, you can use closed-loop attribution to measure the results of your advertising campaigns and link them to metrics like ticket and merchandise sales. This allows you to see which campaigns are driving the most revenue and adjust your strategy with data-driven precision.

Game on.

It is clear that predictive data can be a powerful tool for sports brands looking to take their marketing game to the next level. By leveraging predictive data, sports organizations can gain valuable insights into the behavior and preferences of fans, businesses, and communities. This can lead to more informed decisions, better fan experiences, and improved marketing results.

From customer acquisition to maximizing B2B relationships, growing fan diversity, and boosting fan engagement, predictive data offers numerous actionable ways for sports brands to succeed. As the sports industry continues to evolve, predictive data is poised to play an increasingly important role in driving success and growth for sports brands. So, whether you’re a data scientist or marketer for a sports team, league, or organization, it’s time to take a swing and explore the exciting possibilities of predictive data.

Are you ready to take your sports brand to the next level?

AnalyticsIQ can help you harness the power of predictive data to improve your marketing results and grow your business. With our 1,500+ PeopleCore consumer data points, BusinessCore B2B data, and B2B2C Connection+ linkage, you can better understand your audience, identify new customers, and personalize your messaging to drive sales and engagement. Contact us today at to learn how we can help you take your game to the next level, and check out our complete Sports Data Playbook here.