How DPG Media optimizes the audience experience in real time with Snowplow

Background

DPG Media is a Belgian-Dutch media company (also active in Denmark) which comprises over 40 unique brands over multiple channels. With over 15 million daily viewers, listeners, and users of its content, they are one of Europe’s largest media providers and continues to grow at a rapid pace.

To fuel this growth and survive in the ever-changing news media landscape, they needed to streamline their data stack with best-in-class tools. Weighed down by inflexible tech and held back by the absence of real-time data delivery, DPG was missing out on key opportunities to maximize the value of data.

With Snowplow, they placed behavioral data at the centre of their business model and forged a competitive edge. Armed with high-quality, structured data in near real-time, DPG’s data team has been able to empower stakeholders across the organization and drive business forward.

We can only make automated data-driven actions on our platform right now … if you get those insights a day later, it’s already too late.”

WANNES ROSIERS | DATA AND NEWS PERSONALIZATION MANAGER AT DPG MEDIA

Challenge

For media companies to succeed today, it is imperative to have a real-time view on what their audience wants to read or watch, and to be able to respond immediately. With so much competition for the audience’s attention over multiple platforms and channels, organizations need to provide media that’s relevant, compelling, and personalized. Failure to do so doesn’t just lead to lower revenues, but actually risks their very existence.

Prior to implementing their own data stack, DPG lacked a detailed and complete real-time view of their users’ behavior. With limited visibility on how their audience was engaging with their online content, they weren’t able to optimize the user experience. This meant that they risked losing their audience to competitors who did, along with the associated revenue.

Solution

To empower multiple stakeholders across the business with behavioral data, DPG’s data team considered a number of different options. One of these was the fully-fledged adoption of a packaged analytics platform, which was already being used to some extent. Yet while the platform had an intuitive interface and could be used across the organization, it lacked the flexibility and visibility required by the data team. More importantly, it didn’t allow them to deliver use cases in near real-time. Nor could the team gain a granular view of user behavior or ask organization-specific questions.

As an alternative, DPG considered a Customer Data Platform (CDP), but this solution presented different drawbacks. Given the number of use cases DPG

wanted to execute, an end-to-end CDP would have proved too rigid, restricting their use of behavioral data for different purposes.

Snowplow, on the other hand, provided a far more flexible solution. By integrating Snowplow into their data stack, the data team was able to deliver high-quality, well-structured data into their warehouse, ready to power near real-time use cases.

Having full ownership of their pipeline and full flexibility over the implementation of tracking further convinced DPG’s data team that Snowplow was the right solution for them. Through the use of Snowplow’s JSON schemas, all data entering the warehouse would be in a single, unified format (making it easier for querying).

On top of this, Snowplow’s intuitive console provided an easy-to-use interface for the data team, freeing them up to work on more impactful projects.

At a more general level, Snowplow also aligned with DPG’s vision of a decentralized data team, or ‘data mesh’. According to this vision, data capabilities are decentralized in order to accommodate domain-specific expertise, while data governance and quality issues are centralized.

This set-up facilitates the use of data across multiple domains (i.e subscription insights or product development), while maintaining a unified, high-quality data set. For separate domain teams relying on behavioral data, Snowplow would provide a single source of truth.

We’re going all in with Snowplow because it allows us to have real-time data… with the entire pipeline from Snowplow to Snowflake, it is usually minutes and not more than that.”

WANNES ROSIERS | DATA AND NEWS PERSONALIZATION MANAGER AT DPG MEDIA

Results

The data created with Snowplow is delivered in near real-time and empowers various stakeholders across the organization. The editorial team, for instance, can respond to reader engagement metrics within 60 seconds of an article going live, ensuring that it resonates with the audience. Content personalization and recommendation models, furthermore, work to keep readers within DPG’s content ecosystem, whilst subscription optimization helps to increase the number of subscribers.

These real-time use cases, powered by data created with Snowplow, have been credited with an expected increase of 50% in the login rate.

DPG has also enhanced its online advertising platform, DPG Ads, using data created with Snowplow. With the introduction of Datalab, a new self-serve tool on the platform, customers can create, tailor, and segment ads based on first-party data collected through DPG Media. Not only do they have GDPR-compliant access to behavioral data in real-time, but they’re effectively able to navigate the challenges posed by ITP, ETP, and the ‘cookiepocalypse’.

This represents a value-add for DPG’s ad platform, and is expected to raise advertisement revenue by millions of euros in the coming years.

As DPG Group continues to acquire more brands, their decentralized data team will prove more and more important. By standardizing the use of Snowplow across the organization, behavioral data will continue to power their competitive advantage and fuel rapid growth.

How you can get started with Snowplow

To learn more about how Snowplow can empower your organization with behavioral data creation, book in a chat with our team today. Alternatively, Try Snowplow is our free, easy-to-use version of our technology, which allows you to create your own behavioral data in under 30 mins.

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