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Overchoice is a key problem for the video consumer

 April 2016

New video services are deployed every year, giving the consumers more possibilities to find and consume the video content, which interests them. Each service brings a selection of content to consumers, normally at least five hundred content items to justify subscription or usage in the first place. Choice has been a competitive edge for video services for years, and we don’t see any signs of this disappearing. It’s simply a message, which resonates with potential consumers.

However, once prospects become users and users become subscribers, I believe it’s a different story. At the same time as consumers complain about missing content in a service, they also complain about too much content. Recommendations are too generic and search and discovery functionality assumes that the consumers know what they’re looking for. I believe that this is, in many cases, due to overchoice.

A video provider has paid dearly for access to certain content and the temptation to get the value back by presenting as much content as possible to the consumer is huge. This knee-jerk reaction is wrong. Hopefully, the video provider’s content acquisition department has made the right choices and prioritizations when they have acquired their content portfolio, but that doesn’t mean that all content will be equally important to all consumers. It also doesn’t necessarily mean that your most expensive content necessarily needs to be the most heavily promoted. The overchoice of content will itself become a source of dissatisfaction among the users of the video service. Endless browsing and a feeling of either not knowing where to start or not finding anything to watch will materialize.

In the video industry, it’s been normal to then refer to the recommendation engine as the solution to this problem. Readers of previous blog posts know that I think the recommendation engine’s importance is overstated, but that doesn’t mean that I think it’s meaningless. Of course, for some users a recommendation engine makes sense, but the point is that for other users it doesn’t. The results of the recommendation engine may become obvious or weird depending on your consumption pattern. Instead, a clever combination of an intelligent and adaptive user experience, active editorial sections and automated recommendations make the best combination for an attractive user experience.

We believe that Accedo’s AppGrid platform is a great tool for addressing this challenge. With its inherent possibilities of creating adaptive user experience, it’s possible to create different UX profiles for different target groups. With new features like A/B testing, it is possible for Appgrid users to experiment with different profiles to see what gives the best results for different users. I believe that AppGrid will be a key ingredient in creating the video service of the future.

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