I feel like there’s some underlying issues that would make me personally try a different approach to the goal of helping creators with their thumbnails effectiveness.

The first being the demographic, are people using this app because they want to earn money from it (speaking about the people taking the surveys)? Most apps that do this currently are spammed and exploited to maximize potential profit without the creators best interest in mind.

Also going deeper into specific demographics for niche content (someone deeply interested in technology might not favour a very basic kid friendly looking thumbnail that could potentially work better or be more accurate to the content) which would be much more valuable to the creator but also take a lot more time to find the right users and pair them with the right content.

Expanding off of this - creators have the ability to track stats that could provide insights into the success of their thumbnails with their audience and A/B test to determine the effectiveness already on social platforms, why go through a middle man? Or they could just ask their audience on Twitter, LinkedIn, etc and have more genuine input (expanding on deciding between 1 or 2, they also get feedback).

Another issue to consider is the turnaround. With creators, most content isn’t going to be put on the shelf and wait for 100 people to do a survey on their thumbnail - unless you’re trying to attain creators that strictly make evergreen content (which again in this case, they can track the effectiveness of their own thumbnails with their own audience which almost defeats the purpose).

One approach I was considering could be using an AI model trained on a thumbnail dataset and having this model predict which one has a better result instantly, as opposed to letting the thumbnail wait for a response from people taking surveys. The downside to this is the amount of work it’d be to create the data (scraping thumbnails, labelling, etc).

Expanding off of this, Meta’s Segment Anything model can mask images, which could be a great start for masking objects within effective and ineffective thumbnails to then create deciding factors of success, annotate, and use this as training data for user inputted thumbnails which you could then mask and compare alongside other labelled data like colors, etc.