SVOD/AVOD Winners Will Win on Program Discoverability

The strategy that the top five global SVODs amp8211 Amazon Prime Video Apple TV Disney Netflix and Paramount have all been using is to amass the largest libraries of choices the amount of available SVOD content has grown 53 in just the past three months Each of them now has over a half million program choicesBut as the article shrewdly observesamp8220The perpetually growing abundance of content amplifies negative sentiment from audiences about the difficulties associated with finding something to watch This is especially problematic when viewers start searching without having any idea of what they want to watch Mid2024 research from TiVo for example found that only 155 of people in North America and 16 of people in the UK know what they want to watch when they start a streaming sessionThe difficulty in finding the right content is affecting churn rates as Deloitteamp8217s 2024 Digital Media Survey found that 36 of Americans donamp8217t believe the content on their SVOD services is worth the money they spend Similarly A Googlecommissioned Harris Poll survey at the end of 2023 found that 48 of streaming subscribers canceled a service because they couldnamp8217t find something to watchamp8221Adding more choices the current main strategy actually exacerbates the problemNone of the competitors has made a concerted and successful effort to provide superb program recommendations personalized for the person or coviewing group that is assembled to watch something As Michael Collette CEO of Run3TV which is developing the industry app for NextGen TV says amp8220Most program recommenders are mediocreamp8221This is not a new finding it began to appear around 2019 In July of that year a Nielsen survey found that having tried them and come away disappointed amp8220only 26 use todayamp8217s recommendersamp8230 58 give up searching and go back to their favorite channelsamp8230 21 decide not to watch and do another activity instead of TVamp8221A 24country study done by Ericsson released about the same time found that the average industrialized nation adult spends 23 minutes a day trying to find the right show to watch and usually donamp8217t find it amp8211 and commented that amp8220this is 13 years out of the life of someone who lives to 80amp8221Why are most recommenders falling short Itamp8217s because of the data and the data science that they useVastly underestimating the importance of making program discovery quick and easy the tendency is to use offtheshelf algorithms The most common one is collaborative filtering The method requires some weeks to build up a model so it can proclaim amp8220People who watched the program you just watched also watched X Y and Zamp8221 This seems like a pretty good idea other than the long latency but it is really very superficial It ignores the 80 of the potential audience who never heard of the average show Screen Engine has been reporting for years now that the awareness of new shows is that low If everyone was aware of every show collaborative filtering would work better than it has in the past but not well enough for an SVOD to take the lead over its competitorsOthers have tried to use reviews as a basis for making recommendations but this utterly fails the personalization test A very common method is to use judgment to cluster programs into groups that are like program genres but more granular The leader in the field of video metadata in the world today is Gracenote owned by Nielsen Gracenote uses highly granular subgenres and also about 300 terms it calls amp8220moodsamp8221 and has the largest database in the world The importance of coding using psychological variables such as Gracenote moods themes scenarios and subjectissues cannot be overestimated in the current SVOD battleground The following graphic from the article demonstrates the nuanced psychological metadata that is the direction in which program discoverability has to goamp160Another approach which uses psychological metadata is the one that Iamp8217ve been developing over the years and first field tested in 1997 today exclusively licensed to a company I cofounded called RMT of which Audrey Steele is the President and Bill McKenna the CEO The 1997 field trial found 90 satisfaction with that recommender This approach started with over ten thousand metadata variables and used an early proprietary AI program recommender deployed in the largest MVPD at the time TCI John Malone CEO which used machine learning to identify the 265 variables most predictive of the loyal adoption of a TV series recommended when it had never been watched before by the viewers These psychological variables turned out to be of five types human valuesmotivations character and personality traits of the charactershosts moodsemotions situations and content descriptors There is only a slight overlap with Gracenote moods and for the most part the two methods appear to complement one anotherThere will be a firstmover advantage to the first of the SVODsAVODs who takes advantage of the psychological metadata now available to create what will be one of the new wonders of the world a program recommender that takes less than a minute to use and which is rarely wrong about suggesting a program you will love The groundswell from satisfied users will go viral The competitors will hope to catch up but the awareness of the leader will have a lasting effect on who wins the race to the topThis will be especially valuable to the SVODsAVODs in the part of the footprint where there is advertising because the ads can be metatagged the same as the programs and matching on these psychological metatags has been proven to increase the sales effect of ads an average of 36 NCS with RMT metadata the effect on first brand mention 62 605 with RMT metadataPeople are everyoneamp8217s prospect customers not homes not devices People have motivations and feelings and tend to watch programs with characters that represent their aspirational selves according to Whartonamp8217s Dr Americus Reed so it stands to reason that psychological variables are going to be the stuff you need to quickly identify the program to recommend to a specific viewer or group of viewers coviewingTotal streaming subscribers are now 99 of US population and about 2 billion globally about a quarter of the whole human race and annual streaming revenue is estimated at 200 billion in 2025 In the US the number of streaming services per household is 29 The size of the prize is even huger than this because streaming is still rolling out It addresses all screen devices owned by consumers including smartphones which number 75 billion globally about 92 of the human raceFor want of a nailamp8230Posted at MediaVillage through the Thought Leadership selfpublishing platformClick the social buttons to share this story with colleagues and friendsThe opinions expressed here are the author39s views and do not necessarily represent the views of MediaVillageorgMyersBizNet

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SVOD/AVOD Winners Will Win on Program Discoverability

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