I update a 60-track pop discovery list every Friday and need a faster way to A/B test openers by skip and save rate without living in spreadsheets. Anyone pairing Chartmetric alerts with Spotify API pulls into Airtable (or Notion) to tag mood/energy and flag early skip spikes within 24 hours?
And i’d route Chartmetric alerts through Mailparser into an Airtable automation that fetches Spotify audio analysis for the first 30s and popularity (Web API Reference | Spotify for Developers), then auto-tag openers by intro energy and flag A/B candidates within 24h — it beats babysitting spreadsheets. Caveat: Spotify doesn’t expose true skip/save rates, so I use popularity deltas plus Chartmetric playlist position changes as proxies; are proxies okay for your “flag early skip spikes within 24 hours” goal?
@sophie_lee57 co-sign on alerts; I add an Airtable field that flags the opener when its 24h popularity delta z-score drops below -0.6 and weight it with first 30s energy from Spotify’s analysis (Web API Reference | Spotify for Developers); automation then auto-swaps to the B opener. It’s proxy only and misses true skips, but it’s fast — do you need actual skip data?
I’d skip chasing per-track skips and rotate two opener variants in 2-hour blocks on Friday, logging playlist follower delta in Airtable — clean A/B without spreadsheets. Tag candidates by intro <= 12s from the first section plus a Chartmetric Shazam-velocity pop “within 24 hours” to pick slot 1. Would you try a Make.com scenario to automate the swap and writebacks, or are you set on Notion?
Quick example: I’ve got a Make.com scenario that checks SpotOnTrack snapshots for the stream-growth gap between slots 1 and 2, auto-swaps the opener when the gap tops 8%, and enforces a 90-minute cool-down while writing the variant tag to Airtable — faster than wrangling sheets, but the cool-down prevents thrash. @sophie_lee57 are you tracking a slot-gap like this or weighting mood/energy differently?