Help Your Subs Escape the Recommendation Echo Chamber & Reduce OTT Churn

Preventing a content echo chamber depends on understanding the complex relationship between content assets and viewer attention. Find out how a data-driven approach can better achieve this, drive trial conversions, and lure back stalled subscribers.

Consumers have a love-hate relationship with recommendation engines. When working as intended, a system that pushes relevant content to viewers cuts through the noise of an SVOD library. That’s good news for increasing consumption, which often results in higher engagement, satisfaction, and lifetime value. But recommendation engines have a skeleton in the closet – they can quickly become an ‘echo chamber’ in a way that greatly heightens the risk of OTT churn.

Recommendation engines have had their work cut out for some time. In 2017, it was reported the average viewer spends 51 minutes per day searching for content to watch. And it is reasonable to assume this figure is the same, if not higher, in 2020 given increasing competition and the range of content available on the average OTT service.

Content paralysis is a growing issue, which recommendation engines are supposed to help address. In theory, at least. The reality isn’t quite that straightforward.

How a content echo chamber can spike OTT churn

Most recommendation engines use an algorithm based on a hybrid of user ratings, viewer counts, user history, and past interactions. Very quickly, this combination can end up pushing only a small percentage of viable content to a subscriber. Or, more likely, artificially inflating already popular content higher into the rankings. It can be the case that the better a piece of content is deemed to perform, the more likely it is to get recommended. Whether it would be truly engaging to that particular viewer or not.

The standard grid-style OTT home screen doesn’t always make it easy to discover new content in a world of automated recommendations either. So, it comes as no surprise that one-fifth of viewers give up the search after five minutes and revert to content they have seen before. It goes without saying these subscribers will likely churn if this state of affairs continues. They will either feel like they have seen everything that is worth their time, or that the service doesn’t truly understand their preferences.

However, with recommendation engines still a vital cog in the streaming machine, what can OTT businesses do to tackle this issue, avoid the echo chamber effect, and reduce churn risk? The answer lies in better understanding subscriber attention and engagement.

Tracking subscriber attention to increase engagement

Last week, we discussed how subscriber attention is a powerful metric for identifying the true value of content. This same metric can make it easier for content promoters to understand the relationship between assets, not only to tackle churn but also to grow the bottom line.

Content attention data is particularly valuable to ‘wow’ new users and keep them on board after the trial period. By tracking and identifying what content new subscribers gravitate to that holds their attention most – after they have finished the blockbuster title or original series that led them to the service in the first place – it is possible for content promoters to design a successful engagement funnel for retention.

Not all content is created equal and knowing what assets to promote heavily to different users cannot be based on simple metrics such as viewer count, or left to a recommendation engine alone. Instead, identifying what related content would likely lure a new subscriber deeper into the ecosystem paves a clearer path to conversion. Subscriber attention metrics also have a bearing on working out the increased likelihood of a trial user converting to a paid-up subscriber if they watch a particular piece of content – akin to the hidden value of content we discussed previously.

This is a powerful way to overcome the typically high churn rate among new trial users. The same approach can also be taken for stalled subscribers. First, by identifying content that would best bring an inactive subscriber back into the fold, based on insights pulled from similar users. Then, by building a content promotion funnel to keep their renewed engagement high.

The broader impact of content on subscriber retention

The other factor at play is that recommendation engines can only work with what they are given. Knowing what content is needed to satisfy the subscriber base and keep them coming back for more is half the battle.

For example, if comedy titles pull in 50% of total consumption with a high attention index score across the board, but make up only 15% of the total library, that is a key insight to better understand the wants and needs of your user base. If used to inform content licensing and promotional efforts in the future, this will help reduce the risk of OTT churn by making users feel their content needs are being met more effectively.

Advanced data dashboards like the Wicket Scorecard make this possible. One of the features of a platform like this is being able to breakdown engagement with your content library by genre. This is invaluable for informing a future content strategy that will bolster the bottom line, as well as working out the relative performance and overall entertainment value of one genre vs another.

After all, in today’s attention economy that’s marred by subscription fatigue and content paralysis, targeted content promotion and perceived catalog relevance can make all the difference in terms of loyalty.

Adopting a data-driven strategy for better content promotion

Understanding how subscriber attention and content relationships impact lifetime value and subscriber happiness is essential for any OTT business. But it is even more important today when the big players are extending their trial periods, putting renewed pressure on smaller players to follow suit and encounter longer lead times to revenue from new subscribers.

Taking a data-driven strategy to content promotion, in addition to using a recommendation engine, can turn subscriber metrics into actionable insights that will help convert subscribers into fans, building loyalty and LTV.

Take a closer look at the Wicket Scorecard to find out how to gain unrivaled insight into your platform’s content relationships and performance.

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