Leverage First-party Data to Boost Monetization and Retain Subscribers

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I’m not the first to note that streaming has become competitive within the broadcasting industry. So to differentiate your service from that of your competitors, hyper personalization is crucial. To achieve this, it’s essential to know and understand your viewers. In other words, it’s time to integrate a DMP into your ecosystem and leverage first-party data to boost monetization and retain subscribers.

What is Personalization?

Personalization is a broad term. It applies to UI, EPG, VOD catalogues, look and feel, content, and ad personalization. Operators are currently under pressure to start personalizing all these aspects to stay competitive and maintain relevance to their subscribers. In many markets worldwide, operators are finding that it costs less to retain a subscriber than to acquire a new one. In fact, to keep a subscriber, personalization has practically become a life-or-death strategy.

Data Management Platform DMP =
Organization and Efficiency

First-party data is a great opportunity for operators. Third-party cookies will depreciate quickly and eventually will cease to exist. In a cookie-less world, content and service providers have the perfect opportunity to collect and activate first-party data and offer it to advertisers for efficient ad personalization.

Content and service providers own the inventory, which can multiply with millions of watched hours every month. They also own the relationship with their viewers. They know their customer profiles from the subscription or registration data. The viewer engagement on their platforms also generates a wealth of data every day. For example, what content the viewer watches, and when, can easily indicate their topic affinity. Such data is extremely valuable to advertisers.

Through interactive surveys or panels, content and service providers can also ask their viewers about their preferred topics or which ad categories they favor. Viewers are largely open to providing such data when rewarded with a more personalized service. At the end of the day, if viewers are going to watch ads anyway, they prefer to see those related to their interests.

There are three types of data to be collected:

  • Declared: data that the subscriber actively discloses, for example by participating in a survey.
  • Contextual: data about the context of the content on display, for example content genres, synopsis, key events, etc.
  • Behavioral: data the operator collects on subscribers’ behavior and what types of content they prefer.

A Data Management Platform (DMP) is a centralized database technology system. This system allows you to collect, store, and manage large volumes of viewer data. These can include demographic information, viewing history and behavior, affinity metrics, etc., from multiple sources. This helps you build and organize detailed customer profiles, segment your audience, and target them with relevant, personalized ads. In turn, you’ll be able to create, and measure, more effective and efficient ad campaigns by leveraging first- and third-party data to identify and target specific audiences.

AI and ML for Advanced Audience Clustering

Service and content providers must decide what data they want to collect, and why they need to activate this data.

The first step is to define the objective, and therefore the data sets to be collected. This thinking process is crucial before figuring out the data strategy. Artificial Intelligence (AI) and Machine Learning (ML) are starting to play an important role in organizing the data sets. For example, an ML model can be trained with known data on how to infer a subscriber’s affinity to a specific topic (e.g., fashion). ML models get better with training, and the better the model, the better the predictions.

Another application of ML in advertising is when advertisers want to match their data sets with those of the content or service provider. For example, a large retailer has collected a considerable amount of data about its customers over the years. So it ends up getting to know a lot about them. When this retailer goes to a content or service provider to advertise, to get the maximum ROI on its budget, it’s best to match its first-party data against that of the operator. A “clean room” data exchange process takes place. This happens in order to figure out which subscribers match the advertiser’s data set in terms of their likelihood to buy, their brand loyalty, or other metrics. This data-matching process has been proven to be the best way for advertisers to make sense of the operator’s first-party data.

By using clean rooms, advertisers can leverage the benefits of aggregated data analysis. And this while maintaining compliance with privacy regulations and protecting individual user privacy. Clean rooms act as a trusted and neutral environment for collaboration. In turn, advertisers can make informed decisions based on aggregated insights without compromising the privacy of individual users.

Ateme’s All-in-one DMP Solution to Leverage First-party Data

Ateme’s SSAI comes pre-integrated with leading DMP and clean-room solutions to facilitate and simplify data tasks. Our objective is to enable our customers to optimize inventory and increase revenue from advertising. By combining our SSAI offering and off-the-shelf integrations with several adtech components including leading DMPs, you benefit from easier integration to leverage first-party data, optimize your inventory, and boost revenue from advertising.

For more information on how to integrate to a DMP to personalize your ads and build revenue, get in touch! And if you’re heading to Amsterdam for IBC 2023, stop by and see us at Hall 1, Booth D33 or set up a meeting.

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