Inside the First 90 Days: What We Actually Built for a Junior Hockey Club

We get asked one question more than any other: “What does this actually look like once we sign?”

Fair question. So here’s the honest walkthrough of a real deployment — one of our current pilots, a junior hockey club in a two-to-three-thousand-seat building. We’re deliberate about not naming pilot teams until they have a full season of data and have approved being featured (here’s why), so I’ll keep the identifying details out. Everything else is exactly how it happened.

Days 1–14: Discovery — finding out what they actually had

The club came in the way most do: passionate market, decent crowds on rivalry nights, and a marketing setup held together by a volunteer, a Facebook page, and the GM’s personal phone. Nobody could say which of last season’s efforts sold tickets, because nothing was connected to anything.

The first two weeks produced three things: a fan-persona map built from their actual ticket data (families within a 15-minute radius were massively underworked), an audit of every channel and list they owned (more assets than they thought — years of buyer emails sitting unused in the ticketing platform), and a revenue baseline so that everything after has a before to be measured against.

Days 14–40: Build — the machinery

This is the part clients underestimate. Before a single ad ran, we built: a CRM with every historical buyer imported and deduplicated, landing pages for single games, flex packs, and groups, email and SMS sequences mapped to the fan lifecycle, the AI response layer that answers every inbound lead in under a minute, and — the piece I care most about — attribution tracking wired from ad click to ticket purchase.

The club’s job during this phase was roughly three hours total: brand assets, ticketing access, and approvals. That’s by design. If a system needs your GM twenty hours a week, it’s not a system — it’s a second job.

Days 40–90: Launch — where it gets fun

Campaigns went live across paid social, email, SMS, and the AI response layer at once. Within the first week the pattern we see everywhere showed up here too: speed wins. Inquiries that used to sit until someone checked the inbox — group-night questions, birthday parties, “do you do school groups?” — were getting answered in seconds, at 9 PM, which is when hockey parents actually plan things.

The early signal we watch closest isn’t ticket totals — it’s database growth and response coverage: how many new households entered the system, and what percentage of inbound interest got an instant, useful reply instead of silence. Both moved the way the model says they should. The ticket math compounds from there through the season — and per our own rule, we publish full numbers only when there’s a full season behind them and the club signs off.

What I’d tell a GM considering this

Three things from watching this deployment (and the others running alongside it):

1. Your list is worth more than your ad budget. Every club we’ve opened up has years of buyer data doing nothing. The machine’s first job is waking that asset up.
2. The build phase is boring and it’s everything. Anyone can run ads. Connected measurement is what makes month four smarter than month one.
3. Skepticism is the right starting position. We structured this company around pilots-then-proof for a reason. The modeled outcomes are public, the methodology is on the how-it-works page, and the real dashboards are available on a call, under NDA, with the pilot club’s permission.

If you want to see the actual numbers behind this story — the ad accounts, the CRM, the attribution — book the call. I’d rather walk a skeptic through real data than write a prettier blog post.