How can content providers benefit from ML?
Let’s look at how niche content providers can benefit from data-driven strategies to compete in a highly competitive market and maximize their ROI by using ML (machine learning) technologies to power Niche content providers catalogs.
To get some background on artificial intelligence and machine learning in the video entertainment industry, we asked some of our clients how they are using or planning to use these technologies in the future to maximize their business profits.
How our clients are using ML technologies to power niche content providers catalogs:
One of these clients is PureFlix. They are doing pretty interesting things with recommendations based on machine learning. Marc Beckwitt, EVP at PureFlix, explained to us how they are taking advantage of these technologies to maximize their success. PureFlix launched back in 2015 to bring faith- and family-based content to viewers in the U.S, and since then has expanded to Canada. They’ve grown quite nicely over the years and now offer over 102 million hours of content, a very small operation compared to the big players, but in the past nine months they’ve exceeded what it took them all last year to build and are projected to offer an extra 40 million hours of content by the end of the year, a growth of 167% over last year. As a small provider they differentiate themselves through content strategy, marketing strategy, analytics strategy and the smart use of their data.,They utilize services from 24i, Comcast or Jump Data-Driven Intelligence because the technology stack is not where they want to position themselves as an innovator, so they don’t have a cadre of engineers like Netflix and others that are just building stuff organically. They take the best of breed, put it together and then use innovative strategies around content. They have a very broad catalog and that means they need very good intelligence on how to drive it effectively, so they need machine learning – some extra help behind the scenes – and that’s why they use recommendation technology, to differentiate themselves in the marketplace by driving depth into the catalog and viewer engagement. These strategies have already led to an increase in the number of hours watched and views and affected the way they curate and recommend content using machine learning and artificial intelligence.
When we asked Ryan Chanatry, General Manager at Topic, how they plan to differentiate themselves from the competition and how they use data-driven technologies to maximize their business, he said that they consider themselves a little unique because they don’t focus on a genre; as a niche service they program across multiple formats, from scripted to unscripted, and they’re pretty agnostic when it comes to length,so they just really look to bring the best stories from around the world –whether that’s a two- or three-minute short or an eight hour long form. Topic focuses on filmmakers, projects and passions that the larger streaming services have overlooked or don’t make sense for them. and They don’t have a demographically-led audience, and program to a frame of mind and sensibility, to consumers who are looking for this kind of meaningful programming. Because they are small, they don’t focus on competing at the platform tech or stack level like Amazon and Netflix. They work with providers like 24i or Jump to integrate their content and leverage data and analytics and do more upstream actions. When they started out, they were analyzing their customer’s tastes in the direct space of, say, Facebook, to understand what drives a conversion but also to know what titles interest their audience base. So if a title was doing super well on Facebook based on their testing, then they’d promote that title on their Amazon channel later; simple stuff, but Facebook does all of that heavy lifting, the kind their team would never have been able to do. They’re now really excited to get into recommendations using Jump’s algorithms and take advantage of data-driven strategies to generate accurate recommendations and help grow the business.
GSN is another good example of a niche content service provider. It’s a game show streaming service with a complementary app to the user’s cable subscription. GSN went to market a few years ago with an mvp and their goal is now to try to understand their audience better, to know how their audience might be different in a digital space and to understand a lot more about their user base, who they are, how they’re consuming, when they’re consuming… to create smarter cohorts and figure out how to better market their app and platform.
As with the other companies, they are not big enough to build a technology stack, so they use third-party products such as Jump Data-Driven Intelligence Solutions. They have recently launched a live stream of their cable network and the early indications are that it’s really what their app has been missing. Their users are spending a lot more time with them and their goal is to start measuring when they are watching GSN, how are they watching, what devices their customers are using and if there’s an opportunity to grab some of the users that maybe are distracted by traditional prime time programming, and bring them into their environment. So they’re pretty excited about the next level of analytics that they are getting with Jump and the level of detail that they are getting over their previous version, which was a pretty labor-intensive analytic platform.
There’s a common pattern with most of our niche content clients, which is basically that if you are not very big, it’s better to work with specialized vendors. At the end of the day it’s better to invest in marketing and content than in technology that you can probably get on the market with really experienced companies that can help media companies in the entertainment industry to get to every device and use data in a smart and effective way to track audience behavior, bottlenecks in your UX, recommendations performance, etc. ML technologies can definitely help to power Niche content providers catalogs at a really low cost.
How Data analytics can help your OTT business improve UX:
Data analytics is the secret weapon for success and increased engagement through the use of technology to analyze behavioral users, understand whether they’re engaged with the service, act before they leave and offer them accurate recommendations to retain them. PureFlix, for example, found that the more devices a household uses, the more engaged they’ll remain. So if you have folks that are only on a single device but you’re available on lots of different platforms, be proactive about making sure they know this because the more platforms they get onto, the simpler. This is an engagement strategy that leads to more views and longer engagement.
Data can also help you build your business model around better trial conversion rates to make trial acquisition efficient and marketing pay off, by finding the patterns that lead to success. You’ll find, for example, that some titles lead to very good conversion events but some titles don’t necessarily lead to lifetime value, and some titles get people to convert but they’ll watch other.
ML technologies are really helpful to power Niche content providers catalogs and there are definitely pretty interesting ways to tie data together to do something unique, and then tie that to audience metrics and behavioral patterns to fine-tune how you recommend content in an effective way and engage users to increase conversion rates and lifetime value. Machine learning technologies and the use of data-driven strategies can help your OTT business with that!
Want to learn more about this data-driven niche content strategy for digital media services? Let’s chat!
You can find the complete webinar where we discussed this topic here.
You can also listen to it in this podcast version.