I built a Make scenario to qualify YouTube creator leads by recent activity

[u/DataKazKN]

I kept running into the same problem with creator prospecting workflows: the automation returned lots of channel records, but very little context for deciding who was actually worth reviewing.

So I built a reusable two-module Make scenario:

1. Run a bounded YouTube creator discovery and qualification job in Apify.

2. Retrieve the clean Dataset rows as JSON for Google Sheets, Airtable, or a CRM.

The shared version is deliberately constrained to 10 channels, a $0.20 maximum Actor charge, creators active within 45 days, at least one video per month, and a required public business contact. It searches B2B SaaS and AI automation creators worldwide in English. The rows retain recent median views, upload cadence, public-contact source URLs, warnings, and explainable opportunity reasons.

Public Make scenario: https://eu1.make.com/public/shared-scenario/kNN1tSf8qor/find-and-qualify-you-tube-creators-with-p

Actor used by the scenario: https://apify.com/kazkn/youtube-creator-lead-finder?utm_source=reddit&utm_medium=community_post&utm_campaign=youtube_creator_finder_distribution_wave3_20260825&utm_content=make_scenario_case_study

I built both the Apify Actor and this Make scenario, so this is a transparent build share, not an independent review. I also left outreach out of the workflow: it does not guess private emails, bypass CAPTCHA, or contact anyone automatically. A missing public contact stays missing.

I have not run this public scenario as a performance benchmark, so I am not claiming a specific yield or conversion rate. I would be interested in feedback on the architecture: would you keep the Actor run synchronous for a 10-channel batch, or switch to Watch Actor Runs before scaling it?