The Scenario
You curate live-event themed playlists for a music streaming startup. This quarter's brief: surface touring artists your listeners are likely to have missed. You have 10 anchor artists in column A — acts you know your audience already follows. What you need is a discovery layer: who does SeatGeek think is similar, who's actually on tour, and how popular are they on the live circuit?
The bad version:
- Search each anchor artist on SeatGeek, find the performer profile page, scroll to the "Similar Performers" section if it exists, note the names, and paste them into the sheet.
- Realize SeatGeek's performer pages don't consistently surface similar artist data in the same place, and three of your anchor artists don't have that section at all.
- End up with partial recommendations for 7 of the 10 artists and a spreadsheet with blank rows where the others should be.
A discovery tool that surfaces results for only 70% of your anchors isn't useful for a structured research project.
The Easy Way: One Prompt in SheetXAI
SheetXAI is an AI agent that lives inside your Google Sheet. It reads your artist names, calls SeatGeek's performer recommendations endpoint for each one, and writes the top similar performers with popularity scores into adjacent columns — one row per anchor artist, no gaps.
For each performer name in column A, get SeatGeek's recommended similar performers and write the top 5 recommendations with name and popularity score into columns B through K.
What You Get
- Columns B, D, F, H, J: Names of the top 5 similar performers for each anchor artist
- Columns C, E, G, I, K: Popularity scores for each recommended performer
- Rows where SeatGeek returns fewer than 5 recommendations are filled as far as the data goes, with remaining columns left blank rather than erroring
- All 10 anchor artists covered in one pass
What If the Data Is Not Quite Ready
You want a narrower set — 3 similar performers per row instead of 5
Fewer columns, easier to scan.
For each performer in column A, get SeatGeek performer recommendations and write 3 similar performers per row — name in columns B, D, F and popularity score in columns C, E, G.
You want to deduplicate across rows — if the same artist appears as a recommendation for multiple anchors, note it
Cross-anchor overlap is a signal of breakout potential.
For each performer in column A, get SeatGeek's top 5 similar performer recommendations. Write them into columns B through K. Then add a column L that lists the count of how many times each recommended performer appears across all rows, to flag names that appear as a recommendation for multiple anchor artists.
Column A has performer IDs instead of names
You already did the name-to-ID lookup and want to use IDs for precision.
For each SeatGeek performer ID in column A, get performer recommendations and write the top 5 recommended performers' names and popularity scores into columns B through K.
Pull recommendations, filter to active touring artists, sort by score, and generate a discovery shortlist
For each artist in column A, get SeatGeek's top 5 similar performer recommendations. Only keep recommendations where the performer has at least 3 upcoming events. Deduplicate across all rows. Sort the qualifying performers by popularity score descending and write their names, scores, and upcoming event counts into a new sheet called Discovery Shortlist.
One prompt — enrichment, filtering, deduplication, sort, and new sheet output.
Try It
Get the 7-day free trial of SheetXAI and open a Google Sheet with a column of artists you want to expand on, then ask SheetXAI to pull SeatGeek's similar performer recommendations. See the performer popularity enrichment spoke if you need scores for known artists rather than discovery, or the SeatGeek hub for all available workflows.