Case Study: Communications, Media & Entertainment

Audience-overlap insights

A popular brand within a network needed to solve big data questions on a short timeframe.

Audience-overlap insights

A popular brand within a network needed to solve big data questions on a short timeframe.

In the television industry, understanding where different shows have common audiences is vital. With modern data tools, executives can easily access and manipulate up-to-the-minute audience-overlap information on their laptops.

The challenge

This well-known international brand wanted to track individual user behavior – across shows and over the course of a 12-month period – so the marketing team could understand the strengths between different audiences and different properties. They were especially interested in understanding where different shows had common audiences, both to drive efficiency in its tune-in and marketing initiatives and to inform its content-creation and distribution strategies. The brand was one of many in a large network that had grown both organically and through acquisitions. As a result, individual brands are rather siloed and access to corporate resources was often limited.

The analytics team has access to detailed user data through its Adobe Analytics system. but the large volume of data presented a challenge. Each month contained three gigabytes or more of user data and the quarter contained almost eight. The team wanted a full year’s worth of analysis to get the best picture of audience overlap. Although the parent company was building the infrastructure necessary to manage so much data, the brand wasn’t scheduled for onboarding for another 12 months.

The team tried building an audience-overlap matrix in Microsoft Excel, doing the aggregations in Adobe Analytics. But that presented several challenges: it was time consuming, offered limited ability to slice and dice the data, and it didn’t help them look at their data over time. As a result, they were locked into a high-level summarized view of their data.

The network engaged us to accomplish three goals:

  1. Create a proof-of-concept using two full months of user data.
  2. Design an audience-overlap matrix that could be viewed and manipulated on laptops, enabling a wide swath of people within the network to use them.
  3. Implement a transition strategy that would allow the in-house analysts to complete the remaining 10 months of data themselves.

The solution

We used R to do the data munging and overlap analysis, a program capable of ingesting large datasets and performing the calculations needed to understand the audience overlap. The data was then fed into Tableau, a program that offers the rich visualization capabilities to display a robust audience-overlap matrix.

Ensuring that the matrix could run on a laptop was important so it could be used widely within the organization. As is the case with many companies, the research groups work closely with the site and editorial teams. The overlap analysis helped the site team in design and helped editorial get insight back to the content producers.

Finally, we documented every step of the process, and trained the client’s internal analysts on how to import the data into R, run the calculations, and create the matrixes in Tableau.

How did it work out?

The analytics team achieved its key goals:

  • Deep understanding of audience overlap – With a deep understanding of its shows, the network is now able to discern which signals are meaningful and should be pursued. This has allowed the marketing team to significantly improve efficiency.
  • Immediate solution in place – The network gained a complete solution to perform the analysis as they waited for the parent company to build the next-generation infrastructure.
  • Shared insights among internal teams – Because the Tableau matrixes are easy to use, the data and insights they contain are now freely available to the editorial team and content partners.
  • Three-month engagement and hand off – Perhaps most important of all, by taking over the project, the brand’s team got smarter and learned new data skills that make them more self-sufficient.

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