In the highly competitive world of streaming and Over-The-Top (OTT) services, personalization is key to user engagement and retention. However, platforms often face a significant hurdle known as the “Cold Start Problem”. As well as being a major issue for new services, it’s a problem when new subscribers join a subscription. The Cold Start Problem can make or break a streaming service’s success, especially in its early stages. Approximately 50% of viewers subscribe to content that resonates with their interests. Let’s dive into what this problem entails and how companies are working to overcome it.
What is the Cold Start Problem?
Cold Start occurs when there is no information about new elements in a platform, making producing good recommendations and personalization very difficult. With traditional recommendation solutions, this is typically an issue when a new user joins a platform, a new service launches and has no historical user data or a new piece of content is added to the library.
In these scenarios, the service doesn’t have enough information to accurately predict what content a user might enjoy, leading to a less personalized experience. Poor recommendations can negatively impact engagement due to a lack of interactions and metadata. The less personalized an experience is for longer, the more negative impact it will have on the perception of the service, for example, it’s been found that 60% of viewers believe personal recommendations can reflect shows that the OTT service is trying to promote rather than a choice tailored to their viewing habits.
The Impact on Personalization and KPIs
In modern streaming services, personalization is key. It’s what keeps users engaged, helps them discover new content and ultimately, makes them stick around. If viewers can’t find something they want to watch quickly, especially if they’re undertaking a trial of a service, they are very unlikely to stay as paying and loyal subscribers.
In a limited trial period, it’s vital to make a good first impression. With numerous streaming options available, users can easily switch to another service if unsatisfied. Gaining viewers’ trust early on has proven to create a positive experience which leads to adapting to the platform. The faster services learn and understand their content and users, the higher the impact will be on their KPIs.
How To Overcome the Cold Start Problem
Streaming services employ various tactics to mitigate the Cold Start Problem, however, some still need to be quicker and more effective compared to having a solution that can deliver personalization faster.
Utilizing Demographic Information: A service can collect information such as age, gender and location during sign-up to provide a baseline for recommendations. Although this is the most obvious route, users may not provide accurate or complete information. Nowadays, services don’t like to use this type of information as it opens up the risk of privacy concerns. They should be explicit in explaining how the data is collected and what it’s used for.
Initial Preference Selection: This is where, upon signing up, services ask users to select their favorite genres, actors, or shows. For a user to select, this is more customization rather than personalization. This can be a quick fix, helping initially, but asking for input from a user creates a barrier, as consumers are not always willing to do this, in particular on the lean-back experience where input mechanisms are limited and users only want to be entertained.
Collaborative Recommendations: Use data from similar users to make initial recommendations. However, this is dependent on having enough information and even then the ‘new user’ and ‘new content’ problem prevails. Collaborative solutions can only provide meaningful recommendations when users have made interactions and content has been acted on.
Connect to Social Media: Give users the option to connect their social media accounts to gather more preference data. Similar to the demographic information strategy, this relies on the user giving up this information, which isn’t always possible. Again, this reduces the frictionless experience for viewers and
How XroadMedia Overcomes The Cold Start Problem, Effectively
Unlike other recommendation engines, XroadMedia learns quickly, even from the very first interactions, we start to understand the user intent to provide personalization and transparent guidance towards the right content. A combination of tools such as generative AI, editorial recommendations and business rules help to create what seems to be a more tailored experience at the start of users’ journeys whilst allowing the time to learn more details about each and every user’s preferences and watching behaviors,
How fast?
Our solution learns preferences and delivers personalization in real-time, so even after a few watch events or user interactions, we can deliver recommendations within the UI and personalize other viewer touchpoints like notifications and ads. As well as interactions and thanks to the flexibility of using and combining multiple algorithms, our solution can adapt to the quality of information given and can even enhance metadata to improve the personalization that is delivered to the end-users. This helps build trust with subscribers faster, resulting in increased engagement and return visits.
Sticking to the Rules
As well as business rules set by operational teams, such as certain campaigns or editorial curated lists, you can deliver personalization based on the time of the day, the device, the location or other relevant characteristics. So even if you don’t know much about the user, there can be relevant recommendations that fit the environment.
Going Further than Collaborative Algorithms
As previously mentioned, you can use collaborative filtering based on similar users. In addition, our solution can offer statistical recommendations such as ‘Most Watched’ which can help users discover trending content. As well as what is popular within your platform, you can use third-party sources such as Google Trends and X to identify themes and content that are relevant to the topics and user based on location. So even when users are new, they are able to discover content that is relevant to the majority of your subscriber base,
Connect Users and Content with AI
XroadMedia delivers emotive, engaging row titles that are created by generative AI, which are more exciting than standard text used in services today, giving an explanation as to why viewers are being shown certain content but in a more captivating way. Additionally to making changes in the UI, we use AI to fill the holes in your metadata to be able to make deeper connections between users and content.
Cohorts that are Smarter
Building on statistical recommendations, create specific cohorts for every type of subscriber, including new users. Our solution can automatically identify who is new and can deliver the recommendations that are most likely to get interactions to start learning more about the user.
The Cold Start Problem remains a significant challenge for streaming and OTT services. While various strategies can help mitigate its effects, the key lies in continuously and automatically striking the right balance between personalization and content discovery. For more than a decade, XroadMedia has been helping media companies deliver enhanced, tailored and captivating viewing experiences, even from the beginning of users’ lifecycles.
If you want to understand more about how you can defeat the Cold Start Problem, get in touch with our personalization experts.
