How a media ops team cut cross-platform creator discovery from days to ~20 minutes
Creator marketing ops · manual research across platforms · duplicate and low-quality leads slowing outreach
Shipped, sanitised for public useAt a glance
- ~20mper discovery batch vs days manual
- 3platforms (YouTube, Instagram, TikTok)
- Indexdedupe against influencer registry
- Filtersquality, geo, and content shield
Scraper runs, enrichment checks, and normalisation run before records enter the team review queue — not a raw dump into the CRM.
Client context
A media business running creator campaigns needed a steady stream of net-new talent across YouTube, Instagram, and TikTok. Outreach depended on finding creators in specific niches, but the team could not scale manual research without quality dropping or duplicates flooding the pipeline.
The baseline was fragmented: each platform had different search tools, handles were inconsistent, and there was no shared index to catch creators already in the network.
The pain
Every new campaign restarted manual discovery from scratch.
- Search creators platform-by-platform with no unified criteria
- Manually check view counts, followers, location, and bio fit
- Re-discover creators already in the index under different handles or URLs
- Export messy lists with no enrichment or audit trail before outreach
A single niche search could take days. Duplicates and low-quality leads still slipped through, wasting review time and slowing campaign setup.
What we built
Cross-platform discovery with explicit quality gates — not unfiltered scraper output.
Key decisions
- Shared normalisation layer — one handle/URL convention across platforms so the same creator does not enter twice under different formats.
- Index-first dedupe — match against the influencer registry before enrichment spend, not after.
- Review queue, not blind import — structured output for human review; downstream CRM steps only after quality checks pass.
Measurable outcomes
| Area | Before | After |
|---|---|---|
| Discovery cycle time | Days of manual research per niche | ~20 minutes per structured batch |
| Platform coverage | Ad hoc, one platform at a time | YouTube, Instagram, and TikTok workflows shipped |
| Duplicate leads | High — no shared index check | Index dedupe before enrichment |
| Data quality | Inconsistent handles and thresholds | Normalised fields + filter gates + audit output |
- Unified discovery driver replaced one-off manual searches with repeatable scraper runs per platform.
- Enrichment checks and quality filters reduced low-fit creators before they reached the review queue.
- Team could trace what was searched, filtered, and dropped — not just the final shortlist.
“Before, discovery meant spreadsheets and guesswork across three platforms. After, we run a batch, review the audit output, and only then move creators forward.”
Good fit if…
- Creator or influencer outreach depends on manual multi-platform research
- Duplicate records and inconsistent handles are slowing your pipeline
- You need scraper output with filters and review gates, not raw data dumps
Not the right fit if…
- You need a consumer-facing creator marketplace or UGC platform built from scratch
- There is no operational owner to review and maintain discovery criteria
Got a messy acquisition or data pipeline?
Start with an Ops Automation Audit to map sources, duplicates, and handoffs — or a 10-day sprint if one high-value workflow fix is already obvious.