Thumbnail Test

👋 Hi PH, Ch David here from Thumbnail Test. What is this tool? Thumbnail Test is an A/B Testing tool for YouTubers. How does it work? Select a video > Upload a few thumbnails > Begin a test. Our system will change the thumbnail as often as you want it to and tracks all the metrics you care about. Our analytics let you know which thumbnail + title combination brought you the most clicks per video. Pricing Starts at $24/month billed yearly. Channels under 10k subscribers get 40% off for that plan!! PH Users will get 25% off with this code at checkout: PH25OFF ________ 💡 The problem & Why we're here YouTube has been postponing this feature for +5 years now. It's not clear when it'll be out or whether it will be powerful enough. Thumbnail Test is the only tool that's dedicated only to A/B Testing! It's a little trick that works even for your old videos — you just have to upload a few thumbnails and see your metrics go up. 🏅 Top Benefits • A/B Testing for Thumbnails... • But also for Titles • Team collaboration features • Works for all your old videos (not only for new uploads) • Simple set-up and analytics The best part? We're showing love for small channels — as previously mentioned, you get 40% off the Basic Plan if you've got under 10k subscribers. We know what it's like to be a small YouTuber. We've been there, and we're doing our best to help every creator, regardless of their channel's size. 🏷️ Offer for you! A good way to thank you for being here is a 25% discount (only 50 seats available). Add "PH25OFF" as a coupon for 25% of your 1st payment. 😸 Feedback? Please let us know your honest feedback - good or bad, we'll take it and try to improve however we can. We want Thumbnail Test to be the first tool to come up in discussions about YouTube A/B Testing — your feedback comments are more what we're looking for :)

congrats on the launch, ch david and the team at thumbnail test! thumbnail test seems like a valuable tool for youtube creators looking to optimize their thumbnails and titles for maximum clicks. i'm curious, how do you ensure that the a/b testing results are statistically significant for small channels with limited data compared to larger channels? looking forward to hearing more about your journey!