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.
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.
result
Built a system that finds where target accounts cluster, then validates real market activity there against live event data. Find companies in a target range, validate headcount, enrich for VP Marketing, Growth, and RevOps leaders including city, match each city against Ticketmaster events in a forward window, push matches to a table, surface the clusters.
Wrote it as a reusable function so headcount validation and leader enrichment run against any source table. Tested it against a second dataset to confirm, which means the same build runs against an existing customer list with nothing changed but the input. The first version left the API call outside the function. The second folded it in, so the whole pipeline became one repeatable function instead of a reusable half plus manual glue.
impact / results
Turns one of the least accountable line items in a GTM budget, event and sponsorship spend, into a decision backed by where accounts actually are rather than where a conference happens to be popular. Instead of a team discovering after the fact that a city was the wrong bet, they know before a dollar is spent, in the time it takes to run a query rather than the days a manual research process would take.
tech stack
Clay (enrichment, reusable functions, HTTP API calls), Ticketmaster API, LinkedIn (source data).
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.
process map
reflections
What broke taught me the most. The Ticketmaster API is strict about date formatting and kept rejecting the request. Fixed it with a column that generates today's date and the end date in the exact format the API expects, computed fresh on every run. Side benefit: the table now refreshes its own event window every time instead of going stale.
What I would fix next. Tighten the ICP, because headcount alone is a bracket, not a qualified list. Validate location data, because self reported cities go stale the moment someone moves and a bad city means a bad match downstream. Extend the event window to 90 or 120 days, because 30 is not enough runway to plan outreach or arrange a sponsorship. And handle duplicates, because without a dedupe strategy the cluster signal gets noisy and stops being trustworthy.