By Adam Saville, Sr. Consultant, Data & Analytics – Slalom
     Fei Lang, Principal Partner Solutions Architect, Data & Analytics – AWS
     Andre Boaventura, Global Generative AI Specialist – AWS

Connect with Slalom-2

Sports organizations possess a vast amount of data about fans which can be used to generate actionable insights. However, for many organizations data-driven decision making is focused on what’s happening at the player, team, and game level rather than fan activity.

Everyone would agree that loyal fans who support their team during good or bad seasons, attend every game (well, almost), and buy merchandise are crucial to keeping their teams in business. But fan engagement extends beyond the stadium and retail store; it’s also about ensuring they feel connected to the team and to each other.

The ability to send personalized marketing to fans because they’ve been members for 10 years, or a promotional offer on their birthday, increases satisfaction and loyalty while enabling teams to cross-sell, drive revenue, and fuel growth. This places a data-driven understanding of fans at the heart of sports organizations’ commercial development.

In this post, we will dive into the challenges facing sports organizations trying to understand their fan base and the untapped revenue potential of a fan data platform. We’ll also explore Slalom‘s fan engagement data platform accelerator built in collaboration with Amazon Web Services (AWS), which is aimed at resolving these challenges and based upon Slalom’s experience with a Premier League football club.

Slalom is an AWS Premier Tier Services Partner and purpose-led, global business and technology consulting company. From strategy to implementation, Slalom’s approach is fiercely human and delivers practical, end-to-end solutions that drive meaningful impact.

Current Gaps in the Market

Most sports teams and leagues struggle to quickly capture and leverage data about their fan base. This is often due to a combination of technical and business challenges which prevent organizations from creating and utilizing a 360-degree view of their fans.

Technical Challenges

  • Investing in technology without democratizing data: In many cases, data strategy initiatives focus on investment in technology without considering data access. This leads to data becoming siloed and staff becoming data gatekeepers with all requests for data being routed through them. This creates difficulties with cross-department initiatives and results in departments not taking full advantage of the wealth of data they possess about fans.
  • Manual data preparation: Without data democratization, staff across the business prepare data manually, which is both time-consuming and prone to error. Automation not only reduces error rates but frees up capacity for staff to focus on adding business value.
  • Absence of predictive or prescriptive analytics: The lack of predictive or prescriptive analytics creates scenarios where staff duplicate effort in performing similar analysis on the same data. Conversely, having data enablement and automation in place helps organizations create a single source of truth which connects data across multiple consumers, products, and suppliers.

Investing in addressing these areas to create clean, consolidated and reliable data will enable organizations to focus on delivering business value which would deliver improved commercial outcomes.

Business Challenges

  • Lack of data culture: Data stored within silos with little communication between departments van lead to a lack of data culture. Data literacy and enterprise-wide data training is required to allow staff to read, analyze, and discuss data. Without this training, key terms and metrics are interpreted differently by each department, leading to departments generating different key performance indicators (KPIs) to answer the same question.
  • Low technology awareness: This can leave the greatest impact on organizations. The lack of training on new technologies can lead to staff being ill-equipped to extract value from the systems at hand, which leads to staff relying on manual processes that fail to deliver value to the organization.

Slalom’s Fan Engagement Data Platform Accelerator

Regardless of the sport, the initial stage of a customer’s data journey is similar. Data is gathered from multiple sources and funneled into the platform, which can become the core component of a variety of systems and use cases throughout an organization. Data that’s used for historic reporting can also be leveraged to improve loyalty, marketing, and retail activities.

The fan engagement data platform is based upon Slalom’s experience with a Premier League football club that wanted to address some of the issues listed above, revitalize its systems, and offer a better service to fans. Once delivered, the platform placed the club in a position to:

  • Understand their fans and customer journey.
  • Create fan personas and segments to improve marketing campaigns.
  • Negotiate a better deal with partners.
  • Improve business processes to increased revenue.
  • Improve reporting to the C-Level suite.

The capabilities of the platform are illustrated below and concentrate on ingesting and presenting the data in a centralized layer (data consumption layer) in a way that empowers organizations to focus on segmentation and insight. By leveraging repeatable formats, organizations can start delivering insight in a matter of weeks rather than months.

Fan Platform Figure1

Figure 1 – Scope of Slalom’s fan engagement data platform.

The data platform accelerator can deliver three main outcomes for organizations:

Data Asset Value

  • Trust the data: Provides a single source of truth for downstream tools and teams.
  • Understanding the fan: Develops a detailed understanding of a fan by connecting relevant data points.
  • Centralize fan segmentation: Understand the market value of audiences, reduce effort required to coordinate campaign segmentation, and enable best segments to easily be pushed into marketing and retail channels.
  • Improve reporting: Improve measurement of the whole funnel by creating a consolidated view of data for analysis and insights.

Performance Lift

  • Improve conversion rates: Drive value from the best segments by focusing digital advertising and marketing channels.
  • Increase revenue: Improve targeting of promotional offers to segments showing high purchase intent or personalized offers based upon information known about the fan.
  • Improve fan retention: Increase satisfaction and loyalty by making up-sell and cross-sell opportunities easily accessible.

Operational Savings

  • Automate marketing and sales activities: Understand fan journeys over time and build in automation flows to reduce effort to engage fans.
  • Improve data policy compliance: Provide better operations around zero-party and first-party data.
  • Reduce digital advertising budgets: Grow owned audiences to reduce acquisition budgets over time and focus marketing activities on retention and monetization.

How Generative AI Transforms Fan Engagement

Integrating generative artificial intelligence (AI) with the fan data platform can bring a multitude of benefits. One of the most significant advantages is the ability to create hyper-personalized fan experiences.

Historical data, such as past interactions, preferences, and behaviors can be utilized to offer fans content and promotions tailored specifically to their interests. This level of personalization deepens fan engagement and loyalty, increasing their satisfaction and, ultimately, revenue.

The integration of generative AI is versatile and can be applied to various use cases. For example, consider AI-driven chatbots that engage with fans in real-time, providing instant responses to questions or assisting with ticket purchases. These chatbots can be integrated into the team’s website or mobile app, enhancing fan satisfaction and streamlining the ticketing process.

Additionally, generative AI can integrate into fan loyalty programs to drive additional engagement and retention. For example, the system can automatically award loyalty points for attending games, making merchandise purchases, or engaging with the team on social media.

It also identifies high-value fans and offers exclusive perks such as meet-and-greets with players or discounts on season tickets. Furthermore, it can predict when fans are most likely to redeem rewards, optimizing the program’s impact.

Fan Data Platform Accelerator Architecture

The following architectural design is for Slalom’s fan data platform accelerator, which can be found in AWS Marketplace, and outlines how AWS services have been leveraged to implement the accelerator.

Figure 2 – Fan data platform architecture on AWS.

Component details of the data platform accelerator include:

  1. Leverages two types of ingestion mechanisms based on data source: API and batch ingestion.
  2. For API ingestions, a cron schedule via Amazon EventBridge triggers AWS Step Functions to make calls to the relevant API endpoints and store the raw data in Amazon Simple Storage Service (Amazon S3). Amazon DynamoDB is used to store any ingestion related metadata, while AWS Lambda functions execute Python scripts to call the APIs and Amazon Simple Queue Service (SQS) is used to store and trigger all the API calls needed.
  3. For the batch ingestion of files, a cron schedule via Amazon EventBridge triggers an AWS Step Functions workflow to ingest the raw data and store it in S3.
  4. Landing Bucket is the destination for “Pushed” data sources with tight security controls. These “Landed” files are then moved to the Raw Bucket for further processing.
  5. AWS Glue processes incremental data from the raw data S3 bucket and writes the transformed data to the processed bucket.
  6. AWS Glue Data Catalog is used for the catalog the S3 data lake, and AWS Glue DataBrew can be used to cleanse and transform the data.
  7. Amazon Redshift is used to natively integrate with S3 and provide data warehouse capability.
  8. Amazon Athena and Amazon Redshift Spectrum are used to query data in S3 via SQL. Amazon QuickSight can be used to build business intelligence (BI) dashboards and generate business insights, and Amazon CloudWatch is used for monitoring the extract, transform, load (ETL) pipelines and send alerts and notifications to end users via Amazon Simple Notification Service (SNS).
  9. Lifecycle policies on the S3 buckets to archive the data into Amazon S3 Glacier.

The fan data platform architecture leverages native AWS services to provide the organization with:

  • Preconfigured pipelines: The platform currently has preconfigured pipelines for some of the most common systems used by organizations covering consent, ticketing, turnstile, and retail datasets.
  • Scalable solution: The platform has been built to be scalable and secure using best-practice cloud engineering techniques.
  • Repeatable processes: A clear, repeatable process for onboarding new data sources. Allowing new data sources to be configured and data ingested in days rather than weeks.
  • Interchangeable components: The platform is built with interchangeable components. Whilst data connectors are in place for some of the most used systems, these can easily be changed to integrate with your chosen vendors.
  • Integrates into your estate: The platform uses best-in0class AWS technology, but if you already have well-used technology and capabilities it can integrate with them.
  • Tailored functionality: Functionality can be tailored to meet the needs of the organizations and any AWS service can be integrated with the platform. This allows organizations to create a personalized experience for their fans and leveraging solutions made available from AWS Industry Blueprints for Data & AI.

Can this Accelerator Help with a Customer Data Platform?

A customer data platform (CDP) aids organizations in capturing data from customer interactions (such as Facebook and your website) and consolidate that data into centralized user profiles. This unified data can then be leveraged to create personalized messages, email, and retail offers.

Slalom’s fan data platform compliments a CDP solution by integrating additional data sources into a central data warehouse containing the transactional touchpoint history for a fan. As an example, the reference architecture in Figure 3 illustrates how AWS services  used in this accelerator compliments a market-leading CDP.

Scenario #1 – Fan Data Platform with a CDP

Figure 3 – Fan data platform with a CDP.

In absence of a CDP solution in place, Slalom’s platform can be extended with AWS services to unlock insights into customer segmentation. Section 10 on the diagram in Figure 4 shows the additional capabilities required to incorporate a CDP into the platform.

Scenario #2 – Fan Data Platform without a CDP

Figure 4 – Fan data platform without a CDP.


To get started, reach out to Slalom which offers a complimentary tailored workshop for customers. You’ll delve into current challenges, develop your data strategy, and explore how a fan engagement data platform can be beneficial and tailored to your needs.

You can learn more about Slalom’s fan data engagement platform accelerator in AWS Marketplace.


Slalom – AWS Partner Spotlight

Slalom is an AWS Premier Tier Services Partner and purpose-led global business and technology consulting company. From strategy to implementation, Slalom deeply understands its customers—and their customers—to deliver practical, end-to-end solutions that drive meaningful impact.

Contact Slalom | Partner Overview | AWS Marketplace | Case Studies