Building a Walled Garden for a Leading OTT Video-Streaming Publisher

A leading OTT video-streaming publisher partnered with Clearcode to design and build its proprietary AdTech stack.

Below is an overview of our partnership and the various development projects we worked on with them.

Key points:

  • Our client wanted to build their own AdTech stack to create a walled garden and monetize their first-party data.
  • The AdTech stack consisted of a self-serve ad platform, an ad server, a data lake, and a customer data platform (CDP).
  • The goal of the project was to replace our client’s existing third-party tools to have full control and ownership of their AdTech stack.
  • We built the AdTech stack using a wide range of technologies, including AWS, Python, Go, React, Terraforms, Docker, TypeScript, Django, Bitbucket, and many more.
Building a Walled Garden for a Leading OTT Video-Streaming Publisher

Our partnership lasted for 18 months and was one of the biggest projects we’ve ever worked on.

Tomasz Chmielewski
COO at Clearcode

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What We Did

Below are the main details about the self-serve ad platform, ad server, CDP and data lake development projects.

Self-Serve Platform

  • Designed and built the self-serve ad platform’s UI.
  • Built the functionalities needed to create and manage ad campaigns, such as uploading creatives, creating line items, and setting up ad targeting.
  • Integrated with an external AdTech platform to execute and update the campaigns created in the self-serve ad platform.
  • Integrated with a payment system to provide payment and billing-management functionalities.
Influencer Marketing

Ad Server

  • Created the technical requirements, scoped out the whole project and proposed a set of technical requirements for the MVP based on our previous experience, knowledge, and research.
  • Developed a web SDK that would act as a layer between the ad slots and ad server to display ads and pass user IDs to the ad server.
  • We implemented Video Ad Serving Template (VAST) 4.2 to support the delivery of video ads between our client’s video player and the ad server.
  • As part of the ad targeting processes, we had to come up with a way to identify whether a visitor belonged to a given audience. To achieve this, we received audiences from our client’s customer data platform (CDP) via the SDK and used bloom filters to match visitors to the predefined audiences.
Programmatic video serving

CDP and Data Lake

The goal was to integrate the CDP with the ad server and self-serve ad platform to allow advertisers to run targeted campaigns on our client’s OTT video-streaming platform.

The data lake was built to retrieve data from multiple sources, including:

  • Our client’s OTT video-streaming platform: Information about movies, actors, etc. from our client’s content management system (CMS).
  • Our client’s analytics platform: Event data about video views, watch time, sessions, as well as location, device type, and other types of data.
  • External data sources: Interest-based audience segments created by third-party data.
  • Data from our client’s data science team: Data created by machine learning and data analysis.
  • Device information: Data about the price of certain devices. This data was then connected with the device-level data collected by the analytics platform.

Once the data lake collected this data, it would organize and clean the data, e.g. remove duplicates and change data schema.

The CDP would then retrieve data from the data lake, create audiences from the data, and export the audiences to our client’s ad server and use them for ad targeting on our client’s OTT video-streaming platform.

The Main Technologies and Frameworks We Used