04
event sponsorship
Turning Event Sponsorship From a Guess Into a Signal
Replaced gut feel sponsorship decisions with a system that shows where target accounts cluster and validates it against real event activity.
clayticketmaster apihttp api functions
situation
Teams pick event and sponsorship locations on gut feel. A city that sounds right, a conference that seems relevant. Budget gets spent in markets where the target account base barely shows up, and nothing connects where an ICP actually clusters to where events are happening.
the build
- Built a system that shows which cities your target customers actually cluster in, then checks what's happening there. Find companies in the right size range, confirm headcount, and find their VP Marketing, Growth, and RevOps leaders — and which city each one is in.
- Match every city against upcoming Ticketmaster events, push the matches to a table, and surface the clusters: where your buyers and real events overlap.
- Wrote the whole thing as one reusable block so the headcount checks and leader lookups can run against any list. Proved it on a second dataset — the same build runs on an existing customer list just by swapping the input.
- Tightened it from "half-reusable plus manual glue" into one repeatable function. The first version left the event lookup outside the block; the second folded it in, so the entire pipeline runs as a single step.
process map
1Find companies in range
2Confirm headcount
3Find leaders and their city
4Match cities to live events
5Push the matches to a table
6Surface where buyers cluster
impact
- Turns event and sponsorship spend — one of the least accountable lines in a GTM budget — into a data-backed call based on where accounts actually are, not where a conference happens to be popular.
- Know before a dollar is spent, not after — no more discovering post-event that a city was the wrong bet.
- Minutes, not days — the answer comes from running a query instead of a manual research slog.
tech stack
currently using
- Clayenrichment, reusable functions, HTTP API calls
- Ticketmaster APIlive event data
- LinkedInsource data
phase 2
- Web scraping to validate and backfill location data
- 6sense or Clearbit for firmographic scoring
- CRM sync for matched clusters
- Slack alerts on new high value clusters
- People Data Labs to cross validate self reported location
reflections
- What broke taught me the most. The Ticketmaster API is picky about date formatting and kept rejecting my requests. I fixed it with a column that generates today's date and the end date in the exact format it expects, computed fresh on every run — and as a bonus, the table now refreshes its own event window instead of going stale.
- What I'd fix next: tighten the ideal-customer profile (headcount alone is a bracket, not a qualified list); validate location data (self-reported cities go stale the moment someone moves); extend the event window to 90–120 days (30 isn't enough runway to plan outreach or a sponsorship); and handle duplicates (without deduping, the cluster signal gets noisy and stops being trustworthy).