How Sports Teams Should Solve For The 'Invisible Fan' Pt. 2

Let’s revisit a business challenge that sports properties are desperately trying to address:

The invisible fan.

From our conversations with decision-makers across sports organizations, it’s clear to us that franchises’ #1 technological priority is identifying who their fans are by leveraging unique touch points like live events, merchandise, and digital viewership.

But it’s a struggle:

FC Barcelona’s Director of Barça Vision put it bluntly:

Last time we talked about the “Invisible Fan” in late March, we broke down: 

Billion-dollar problem because the startup that can solve this daunting challenge could be worth billions.

Right data foundation because having the best AI tools is not nearly enough to actually solve the problem.

In fact, the technological capabilities are clearly there. 

And contrary to common misconception, there’s enough raw data too. 

Established teams have endless data points from decades of operations, but they’re disaggregated across a multitude of vendors: ticketers, merchandisers, broadcast and streaming partners, social media platforms, PoS payment processors, and more.

For Part 2 of “How Sports Teams Should Solve For ‘The Invisible Fan’”, we’re covering:

Sports as an industry presents the most unique data challenges because of its reliance on third-party vendors. 

Take a traditional e-commerce business like Shopify as an example. Its data collection is largely controlled in-house because its software products were built to serve as a merchant’s entire commercial operating system: storefront and checkout, payments, email marketing, customer profiles, purchase history, analytics, and fulfillment management.

Data is much more actionable when it’s easily controlled and derived from one place.

But for sports franchises, the operational focus has historically been delivering on-field performance. As a result, most core revenue and data-accumulating functions have been outsourced to third-party vendors:

All of their fan data – and the touch points across a fan’s lifecycle – are siloed across vendors.

And it will likely stay that way.

It is in each vendor’s best interest to control its own data warehousing and accessibility, creating switching costs for sports organizations that become increasingly reliant on those systems over time.

So the data that teams own is often just harder to find, pull quickly, test against, and assemble into profiles at scale.

If sports teams want to make thoughtful, data-driven decisions at scale, rapidly testing strategies, from marketing campaigns to fan activation to ticket pricing, is a prerequisite.

Right now, a full pricing strategy for one section of a stadium can still take days or weeks, especially if the team has to pull data across ticketing, resale, CRM, concessions, and finance systems – or outsource the analysis altogether.

Meanwhile, modern companies can run hundreds of strategic tests using AI at ease because their data infrastructure is sound. 

Our prediction: data siloes and outdated infrastructure will become the biggest bottleneck for franchises as they start implementing AI to optimize workflows.

Anthropic – yes, the $1T AI company behind Claude – recently broke down how an AI agent’s success is predicated on providing the perfect amount of “context”: data, documents, tech tools, APIs, etc. If your AI tools don’t have the right context, or have difficulty accessing it, they can “lose focus or experience confusion” like humans, making them more expensive and inefficient to implement.

Customer data should become its own revenue segment for sports organizations because it can capture dollars sitting on the edges.

The first unlock is fan feedback loops.

Every touchpoint around a sports team and its IP should become an activation point. If a fan buys merch from Fanatics, votes on a social media poll, opens a team email, or watches highlights on the app, how do teams move them toward buying a ticket – and continually upsell them?

There’s a famous lecture from Chamath Palihapitiya, former Facebook executive and Warriors co-owner, where he breaks down Facebook’s early “AHA” moment: once a user added seven friends in less than ten days, they would “basically” never churn.

Facebook built its marketing, onboarding, and activation efforts around that one stat.

With the right data orchestration, sports teams could start viewing their ecosystem the same way. They could identify which fan behaviors indicate someone is primed to buy their first ticket, upgrade to a membership, purchase premium inventory, or become a season-ticket holder – and then build their decisions around those signals.

The second unlock is sponsorships.

Never before have brands wanted to advertise more through sports. Just look at this past NBA season: SponsorUnited reported that team sponsorship revenue hit a record $1.8B, growing 11% YoY. And with sponsorship revenue growth outpacing deal growth by 5x, brands are willing to pay top-dollar to get in the action. 

We think there’s a world where they’d pay way more. 

Right now, sponsorship spend is still not nearly data-driven enough. 76% of U.S. B2C marketing executives who invested in sports sponsorships said they struggled to calculate ROI.

That tells us two things:

So the question sports teams should ask themselves is simple: 

How can we be even more valuable to third-party sponsors?

Take JPMorgan Chase and the Knicks as an example.

Chase has been the Knicks’ primary marquee partner since 2010. The partnership includes prominent signage and exclusive lounge access for Chase cardholders. With current technology, teams can measure who had seats in the Chase lounge and, with more difficulty, estimate how many impressions each sign generated across broadcast, streaming, and in-person viewership.

But what if the Knicks could go deeper? 

Through anonymized fan intelligence, they could show Chase:

That is a much more valuable sponsorship product.

Instead of selling Chase signage and impressions, the Knicks would be selling audience intelligence, customer acquisition, and closed-loop measurement.

Third is university donors.

This is more nascent, but potentially even more creative.

Universities already have massive alumni bases, but many still fail to identify high-net-worth alumni who could become major donors. The athletic department may be one of the best places to find them.

These alumni are going to games, buying season tickets, purchasing merch, opening athletics emails, attending hospitality events, and interacting with the school’s sports programming. 

Those are all signals.

With the right data layer, universities could use sports engagement as a primary avenue to identify and activate potential donors that legacy screening processes may have missed.

And that’s the larger point.

Sports teams already have countless data touch points across social media, ticketing, hospitality, concessions, merchandise, email, streaming, and in-venue behavior. 

The opportunity is using those signals to create new revenue across tickets, memberships, sponsorships, premium experiences, and donor pipelines.

We believe there are two structural choices teams will face as they revamp their data orchestration.

Path 1 is switching to a unified data platform. 

All team operations run through a single vendor, managing ticketing, fan engagement, CRM, and more instead of relying on external vendors that created the data siloes in the first place.

This automatically creates a closed fan feedback loop, gives AI agents much more granular context, and makes the fan journey much easier to build.

Value proposition: migrate off existing vendor relationships and double down on a best-in-class solution that runs those functions simultaneously.

Path 2 is adding a coordination layer. 

Teams layer in a cloud infrastructure product that sits on top of their existing vendor stack, connects the data without migration, cleans and unifies it into a central fan profile, and makes it queryable and actionable.

Instead of switching, they keep their existing ticketing vendor, CRM, and operating processes – the layer just makes everything talk to each other.

Value proposition: keep the processes teams are used to, but make them far more useful through a product that works closely with the organization to manage those workflows better. 

While both paths are still in the very early stages, each could be highly transformative in a sports team’s journey toward becoming a robust, data-driven decision-maker.

And in a world where AI is only as good as the context it can access, the teams with the cleanest fan data layer will have the biggest commercial advantage. 

If you’re interested in either of these paths – or ideas around how your organization should revamp their data infrastructure – feel free to reach out! 

We’re more than happy to help.

University of Utah and foundation finalize Crimson Brand Partners, a new commercial operating company for Utah Athletics, with financing from Otro Capital (June 12th)

Crystal Palace owners explore sale after club’s historic trophy run and Woody Johnson’s 2025 stake purchase (June 15th)

Hexis, a personalized nutrition app for athletes, raises $2.1M seed round led by Apex Capital (June 16th)

Swsh, an AI-powered fan engagement platform for live events, raises $4M seed round (June 16th)

Juggernaut Capital Partners launches $500M dedicated sports investment platform with global football icon Gareth Bale (June 15th)

Alexis Ohanian’s Seven Seven Six acquires ONIT, college sports trading card company focused on long-tail athlete collectibles (June 18th)

Formula 1 names Fever Official Supplier to power global ticketing platform beginning in 2027 (June 16th)

Novig wins CFTC approval to launch sports-first prediction market as competition intensifies (June 16th)

If you find our weekly job board helpful, join the T4Q Talent Network.

Now - here are some cool roles we found and personally curated this week. Enjoy!

1. Deep Blue Sports + Entertainment - Strategy Director: $200K

2. ADvantage (Adidas Founder Family Firm) - Venture Capital Investor

3. Wispr Flow - Chief of Staff to CEO

4. Apple - Sports Strategy Team Member: $169K

5. International Olympic Committee - Head of Media Rights

6. Impeccable Development (Carlos Boozer’s Real Estate Firm) - Marketing Manager

7. Hospital For Special Surgery - Global Sports Marketing Manager: $98K

8. Brooklyn Sports & Entertainment - Sr. Director, New Ventures: $190K

9. DAZN - Strategy & Business Development

10. Sony - Games Portfolio Strategist