I wasn't hired to run Manchester United's marketing. That's what made it interesting.

For the 2025 Premier League Summer Series, Relevent Sports Group engaged me to help lead marketing operations across three major U.S. markets: MetLife Stadium in New York and New Jersey, Soldier Field in Chicago, and Mercedes-Benz Stadium in Atlanta.

The role covered local and venue marketing, partner coordination, ticket sell-through and yield support — and the piece this story is about: the measurement infrastructure needed to understand performance across the Premier League, four clubs, three venues, Ticketmaster, agencies and media partners.

The objective was not to build another marketing dashboard. It was to create enough structure around a decentralized ecosystem that we could begin answering harder questions. Where were ticket buyers coming from? What channels were influencing them? Which campaigns were converting? When were people buying? And could we connect any of that back to actual ticket revenue?

There was no single funnel. There were many.

A fan might see a club social post, visit the Premier League website, receive a team email, search Google several days later, and ultimately purchase through Ticketmaster. Another might discover the event through paid media. Another through a venue. Another through an app, radio, outdoor, PR or a partner.

And ultimately, many of those customers would simply appear in analytics as Direct.

The transaction is easy to see. The journey that created it is much harder.

The problem with attribution at this scale

The framework was designed before launch — not reconstructed after

This part matters, because it is the part most organizations skip. Months before a single ticket went on sale, we built the tracking plan: registration campaigns as the presale data-capture destination, direct-to-Ticketmaster links at on-sale, GA4 and Tag Manager measuring outbound clicks, promo codes, QR codes, email tracking, and Ticketmaster's referral, redemption and UTM reporting — all feeding one centralized point of collection and review.

The connective tissue was a prescribed UTM architecture distributed across the Premier League, all four clubs, broadcast partners, media partners and venues. Source. Medium. Campaign. Market. Partner. Placement. Creative. Ticket destination. It came with an integrity checklist — consistent naming, lowercase values, content IDs, pre-launch testing in GA4, and verification that clubs, venues and affiliates were actually using tagged links.

I couldn't control every campaign. And I wasn't supposed to. The Premier League and the clubs ran their own marketing. What the framework did was give every organization sending traffic into the ticket-buying ecosystem a consistent way to be identified. Not perfect attribution. Better attribution. And most importantly: a starting point, agreed on before launch.

Then build the performance picture

The data didn't live in one place. It lived everywhere: Google Analytics, Ticketmaster and TM1, paid search, paid social, programmatic, email and CRM, Premier League channels, club channels, venue channels, promotional codes, registration campaigns, media partners, attendance and ticket scans, transaction-level UTM data, audience research, outdoor, radio and agency reports.

Rather than treating every report as its own answer, I reconciled those sources into a common performance view. Because an impression isn't a ticket. A click isn't revenue. A platform conversion isn't necessarily a transaction. And a platform claiming credit for a sale doesn't necessarily mean it created the demand.

Those distinctions became important.

The commercial result

When the final Ticketmaster data was reconciled, the three events generated approximately $15.94M in face-value ticket revenue across 142,639 total tickets distributed — 127,675 of them paid, with 127,655 fans through the gates and a blended average face value of about $124.85 per sold ticket.

Those were the commercial numbers. The real value of the work came from understanding the behavior underneath them.

On-sale was game day

March 13 — the primary on-sale Thursday — was one of the clearest signals in the entire dataset. Soldier Field sold roughly 19,668 tickets that day. MetLife 13,672. Mercedes-Benz 11,289. Around 44,629 tickets in a single window, representing between 24% and 40% of total volume depending on the venue.

The implication was significant. An on-sale isn't simply another campaign date. It should be treated like an event itself. Warm the audience before it. Coordinate partner communication around it. Stack PR. Get CRM ready. Capture search demand. Create urgency. The sale may happen on Thursday — the work to create Thursday starts well before Thursday.

Then we learned people also wait

About half of ticket volume came more than 120 days out, concentrated around that initial sales window. But roughly 35% came during the final 30 days, 18–24% within the final six days, and even event day itself accounted for roughly 4–5% of sales.

That changed how I think about event marketing investment. There isn't one conversion window. There are several. On-sale captures passion. The middle maintains interest. The final month converts intent. The final week converts urgency. Spend the entire budget trying to win opening day and you're ignoring a huge portion of the eventual customer base.

Search was a closer. Email was better.

High-intent paid search through Ticketmaster: roughly $107,000 invested generated 11,660 tickets and about $1.82M in attributed revenue — a 16.9x return.

The lesson wasn't that search created all of that demand. A fan searching "Manchester United Chicago tickets" didn't wake up that morning having never heard about the match. Something created the demand — an announcement, PR, social, a friend, an ad. Then search captured it. That is exactly why understanding a channel's role in the funnel matters. Some channels create demand. Some reinforce it. Some close it. Search was exceptionally good at closing it.

Ticketmaster Premium Email did better still: about $39,500 in spend generated roughly $978,000 in attributed revenue — a 24.8x return. And during a July conversion push, Gmail produced more than 3,300 measured conversions at roughly $2.57 CPA, while Meta and TikTok were considerably more expensive as direct-response closers.

Which reinforced something I have believed throughout my career. The database matters. Getting someone's attention is expensive. Having permission to communicate with someone who has already demonstrated intent is valuable. Really valuable.

Creative wasn't equal either

The paid registration campaign generated roughly 24,437 registrations against a target of about 23,333. Meta was the clear driver. But simply saying "Meta worked" would have missed one of the more useful lessons.

Static Meta creative dramatically outperformed video: 21,857 registrations at roughly $1.54 CPA versus 1,560 at roughly $4.06. Same platform. Very different result.

That's where reporting starts becoming strategy. Not did Meta work? But: which audience? Which creative? Which market? Which message? Which moment? Which action were we asking someone to take? That's the level where performance data becomes useful.

The markets behaved differently

MetLife produced the highest face-value revenue — about $6.22M at an average paid-ticket value of roughly $147. Chicago generated approximately $5.09M while maintaining the strongest comp discipline at roughly 9%. Atlanta generated about $4.64M with an average paid ticket around $113.

That matters because attendance is only one measure of an event. So is yield. So is paid share. So is price. So is inventory discipline. So is how quickly the market converts. The goal isn't simply to fill a stadium — it's to understand the economics of filling it.

Then we found the real attribution problem

Once the transaction-level UTM data was cleaned, I worked through an attribution model that used the primary source and, where a transaction appeared as direct, looked to a known secondary source where a legitimate channel existed. That model represented approximately $16.77M in attribution-model transaction value.

Notice that number is different from the $15.94M in final face-value ticket revenue. That became a lesson in itself: financial truth and attribution truth are not always the same number. They carry different definitions, different transaction scopes, different fee treatment, different reporting windows. The $15.94M says what the ticketing business generated. The $16.77M says how transactions classified across marketing sources. Those numbers should never be blended simply because they both contain dollar signs.

And here is the finding that mattered most. Even after cleaning and secondary reattribution, approximately 52.6% of the attribution-model value still appeared as direct or none.

That might be the single most important learning from the project. Because "Direct" doesn't mean marketing didn't influence this person. It means: at the point we observed the transaction, we no longer knew enough about what had influenced them before it.

A customer could see Meta on Monday. Open a club email Tuesday. Visit the Premier League website. Search Google Thursday. Come back directly and buy Friday. And the final sale still risks reading Direct.

The revenue was captured. But much of the journey that created it was gone.

Why the finding mattered more than the number

An honest assessment

Here's the part I'd want another marketer to hear. The architecture we designed was stronger than the attribution technology we had available to execute it. The framework correctly called for consistent UTMs, promo and QR attribution, GA4 and Tag Manager measurement, Ticketmaster reporting, centralized review and partner governance — before launch. Where the system fell short was persistent identity, cross-session stitching and true incrementality testing — capabilities that largely sat outside anyone's control mid-campaign.

Financial and ticketing measurement was excellent. Paid digital measurement was strong. Cross-channel journey attribution was the hard, honest gap. Naming that gap is what makes the strong numbers credible.

This wasn't my first time in that fight, either. At Indy Eleven, the question was whether we even received the media we bought — we were only verifying 6% of it when we started. The Summer Series was the harder sequel: once you trust what was delivered, which of it actually created the sale? Verification first, attribution second. The discipline compounds.

What I would build next

If I had the opportunity to architect the next Summer Series from Day 1, I wouldn't stop at UTMs. I'd create a persistent attribution system connecting first touch, last non-direct touch, GA4, search, paid social, CRM, club marketing, Premier League platforms, venue marketing, Ticketmaster, promo codes, offline media, transactions and attendance into one warehouse. The customer who discovers the event in March and buys in July shouldn't lose their entire history just because their final visit happened to be direct.

And eventually, I would take the system one step further. Beyond attribution. Into incrementality. Because the most interesting marketing question isn't which channel got credit? It's: would the sale have happened without it?

What transfers

I didn't build Manchester United's marketing strategy. I didn't control the Premier League's CRM. And I didn't personally generate $15.94M in revenue. That isn't the story. The story is that I walked into a decentralized global sports property where dozens of independent marketing activities were feeding one commercial outcome, helped architect the measurement framework before launch, and then operated it — into a much clearer picture of how the business performed.

• Design the measurement before the marketing runs. The tracking plan, UTM architecture and integrity checklist existed before on-sale. That's the difference between building attribution and excavating it.
• Create a common language before you create a dashboard. UTM discipline across independent partners was the unglamorous work that made everything else possible.
• Separate financial truth from attribution truth. Two reports can both be technically correct while answering completely different questions.
• Find the windows. On-sale day and the final fortnight behave nothing like the months between them.
• Ask what within a channel worked. "Meta performed well" is not a finding.

I've spent most of my career somewhere in the space between fans, brands, experiences, marketing, revenue and data. The numbers aren't interesting to me simply because they're numbers. They're interesting because they help explain behavior. Why did somebody care? What made them take the next step? Why did they wait? Why did they finally buy? And what should we do differently next time?

Marketing becomes considerably more valuable when you can explain what it actually did.

The belief this project reinforced