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How Resonance AI Has Helped TV Anthologies Succeed

It wasn’t that long ago when the idea of a season-long anthology series seemed quite novel. Telling an entire single story over the course of multiple episodes, effectively creating a new miniseries every season but with similar themes or even actors, was cool but not necessarily something that would be replicated again and again with much success.

But in the nine years since American Horror Story popularized the idea, there have been numerous stabs at this format and a wide range of quality. While this isn’t a review blog, we have all enjoyed a limited series anthology, only to watch the second season with a mixture of confusion and disappointment. The true appeal, after all, is for a show to establish a solid foundation of the kind of show it’s going to be, or the universe it inhabits, and then start fresh each time.

Ideally, that would mean that every time a new season starts, they would have taken all the good from the first season, removed all the bad, and created something that’s an improvement on the old model. But, as we all know, that is not what usually happens.

There are two major reasons why.

One of them is that starting from scratch is tough. Writing something good a second time is hard for anyone, and the writers who try to pull it off have their work cut out for them.

But another, maybe even bigger, reason that the limited series anthology doesn’t get better with each season is because there is a lot of disagreement on everything that made the previous season so successful. Was it the characters? The story? The music or lighting or dialogue? Was it a combination of these? And despite its success, what did not work as well? What needs to be avoided or deemphasized the next time around?

Writers, producers and directors can go back and forth on this over and over with no one ever fully satisfied with the answer. And this is why an incredibly valuable tool in this conversation would be AI.

We’ve seen proof, with two series that have used Resonance AI to create better content.

The first series had just seen a significant drop in viewership for their second season. While the first season had been a massive hit, and the audience had shown up in big numbers for the premiere, ratings continued to decline and by the end the season was considered a flop. Between ratings and reviews, it seemed as if the series would not have a third installment.

But the studio wanted to make sure. They had Resonance AI analyze both the first and second season and determine what had gone wrong.

Our analysis determined that there were too many characters, with the main character registering overwhelming negative resonance with the audience. It also surfaced that the plot was too complex and the themes were too dark for viewers.

After our analysis, the studio decided to pursue a third season that steered clear of these elements and that season was both a critical and commercial success.

The second series only had one, successful, season and wanted to make sure that they didn’t make the mistake of straying too far from what made it work.

We analyzed the elements of that season, finding exactly what the audience liked and what failed to engage them. We then offered a list of themes and character traits that could be transferred from the first to second season, along with other info about use of music, lighting, locations and pacing of dialogue.

In both of these cases, the creation itself and all of the creativity was left up to the writers themselves. And in both scenarios they did incredible. But they were able to follow their own instincts after being equipped with data-driven insights that gave them a richer, different perspective on their content.

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