Frostbyte Team Drives Insights into Sports Fans with a Custom Fan 360 Solution 

The global sports industry is big business. In 2023 in North America alone, the market is expected to be worth over $83bn. By 2026 the global sports market is expected to reach over 700 billion, according to Statista

But globally, many sporting organizations lack an understanding who exactly their fans are and what they are interested in, which makes it difficult for these organizations to tailor customer experiences based on what customers want. Sports leagues are no different from standard businesses—they want to engage, acquire, retain, and grow their fan base (their customers), which requires a robust customer 360 solution.

Unfortunately, only 14% of organizations have achieved a 360-degree view of the customer, while 82% said they aspire to get there, according to Gartner. The result: companies use their marketing and sales budgets inefficiently, and they provide poor, inconsistent customer experiences. This increases both customer acquisition costs and churn as well as a lowers return on ad spend.

Snowflake’s solution

Recently, the Snowflake frostbyte team worked with one of the largest sports leagues in the world to help it better leverage its data for a “fan 360 view.” But before we go into the details, you may be asking, “What is the frostbyte team?”

The frostbyte team is the technical force behind Snowflake’s industry go-to-market strategy, dedicated to building industry solutions that solve specific business and technical challenges across all industries, including healthcare, telecom, manufacturing, and more. 

This particular client came to Snowflake looking for a better way to predict the likelihood of a fan attending a sporting event, which would help them improve targeted outreach to fill seats at events and increase new fan attendance. To solve this complex problem, the frostbyte team developed a lead scoring system based on near real-time data that dynamically updates daily lead scoring, and tackles interdependencies based on a multitude of activities and characteristics for each fan. The system architecture is illustrated in the diagram below:

Reference architecture for customer experience solution

The solution predicted the likelihood of a sports fan attending an event depending on their demographic and behavior information as well as the communication channel (email, phone, etc.) used, as illustrated in the two charts below:

Lead Scoring: Data-driven daily lead scoring with complex interdependencies based on a multitude of activities and characteristics for each fan.
Next Best Action: Data-driven daily next best action scoring based on complex interdependencies on a multitude of activities and characteristics to determine which communication medium should be used by sales and marketing personnel.

Such a complex use case requires a cloud data platform that can power the advanced analytics and data science required to better understand sports fans. Snowflake’s Data Cloud breaks down data silos and offers near-unlimited scalability for first-party data while unlocking access to third-party data. It also brings the workloads to the data so you can run seamless pipelines between the different workloads that power your understanding of your fans giving you a big competitive advantage.

Snowflake Data Cloud workloads

The frostbyte team used a number of Snowflake’s workloads to achieve the client’s solution: 

Data engineering: Foundational layers for fan/customer 360: Creating the data foundation layer is the first and most important step to gaining actionable fan insights. In this case, we brought together fan data from a wide variety of different source systems such as sales, CRM, social media, and marketing. 

The data then went through several transformations and pipelines to ensure quality and consistency. Then, in a secure and governed way, we made a consistent, 360-degree view of our fans accessible through Snowflake’s data platform, which uses a single copy of data to support various workloads and teams. The data science team was able to leverage the new customer 360 foundation to help power their AI and ML models (for example, lead scoring and next best action). At the same time, the data apps and operational reporting teams used the data to gain deeper insights into customer behavior and the effectiveness of marketing campaigns.
Collaboration: A key differentiator of Snowflake is Snowflake Marketplace, where businesses can discover data, services, and apps from over 310 providers across 18 categories as of February 2023 to power their most critical data demanding workloads. Snowflake Marketplace enables you to access the most current data available while reducing data integration costs with direct access to live, ready-to-query data sets with no ETL required.

Discover data sets and monetize your own via Snowflake Marketplace

To build out the fan 360 insights, the team used third-party data from Neustar (demographic data) and ShareThis (behavioral data) directly from Snowflake Marketplace. Neustar’s “ElementOne (E1) Market Analytics and Segmentation” solution is a robust data set of over 17,000 consumer demographic, psychographic, and behavioral attributes, helping marketers identify their most valuable audiences, understand their current and prospective customers, and sharpen their marketing strategies for improved ROI. At the same time, the ShareThis “Data Set Social Data Feed” provides access to the company’s global network of proprietary social media intelligence through the analysis of 18 billion social events per month. In addition, the company provides analysis of consumer behavior data from more than three million global domains, representing 40 billion events per month. ShareThis identifies patterns in consumer behaviors, audience insights, modeling, and analysis, and also provides powerful enrichment for categorization, keyword extraction, entities, and concepts.

Data science and machine learning (ML): The Snowflake Data Cloud provides data scientists with their choice of frameworks, languages, and tools. Data scientists can also run scalable and secure ML inference with models running inside Snowflake as user-defined functions. For this particular use case, the frostbyte team created a model of fan activity including purchases, website and system activity, communication channels, venue demographics, fan activity history at each event, and Neustar demographic data. The model is deployed in Snowflake and provides daily score updates for each fan.

Applications: To enable the frostbyte team to easily create data applications without front-end development expertise, we leveraged Streamlit, an open-source Python framework. With Python the team is able to simulate a CRM system and showcase how sales and marketing teams could leverage Snowflake-powered data apps within a CRM to, for example, inform sales team members about which fans should be contacted by phone and which by email instead for each event.

While the Data Cloud delivers all this, taking advantage of the many opportunities and competitive advantages it presents is often new for customers. That’s why we created the frostbyte team to help organizations implement technical solutions that deliver real business value. To learn more about frostbyte, contact

The post Frostbyte Team Drives Insights into Sports Fans with a Custom Fan 360 Solution  appeared first on Snowflake.


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