German news publishers got a “wake-up call” a few years ago when several Nordic news publishers such as Schibsted, Bonnier, and Amedia shared their digital reader revenue strategies.

Meinolf Ellers, chief digital officer at DPA, shared during an INMA members-only Readers First Meet-Up on Wednesday that many of the German publishing staff in attendance were shocked by the sophisticated levels of data analytics that underpinned the Nordic organisations’ transformation: “We all felt that we are far away from this. We all felt we have to move.”

The result was that in the two to three years since, German regional publishers began to conduct numerous pilot projects and hired data analysts to move forward faster in this transition to a successful reader revenue model.

“Then they began changing,” Ellers said. “The began to change structures, they began to change workflows.”

While the German publishers are still in the middle of this process, they have seen a number of success stories and encouraging results. Last year, eight of these regional publishers teamed up to share their audience analytics best practices and what they’ve learned about boosting reader engagement and online subscriptions.

The importance of cross-functional team roles

This journey went in two basic directions, Ellers said: “If the central goal is to dramatically raise digital reader revenue, it basically comes down to two tracks.”

One track: What does this mean for the newsroom? Data-driven news publishing led to many conflicts within German newsrooms due to resistance to this new model from some.

“This is still ongoing, this conflict,” Ellers said. “We haven’t integrated and won everyone’s heart yet.”

The other track: sales and marketing, which now requires new skills around data and audience engagement.

“People have to accept that this is a new game. Selling digital subscriptions is different from selling print subscriptions, and you can only achieve significant successes if these two tracks come very close together and interconnect.”

What German news publishers saw at that meeting with their Nordic peers three years ago was the marketing people working side-by-side with the product development people and the journalists — and all of them were working closely with the data analytics.

“What they have to accept is that this is not about betraying journalism,” Ellers said. “It’s about learning what really reaches out to people and is of relevance to people, that we want to convince to pay for our services.”

A central data bank

“If you want to build powerful algorithms, and if you really want to identify reliable patterns, you need a vast volume of data,” Ellers said.

This led to the creation of a central data link for all the regional and local news publishers in Germany, providing a huge data set that enables the publishers to build relevant algorithms.

These algorithms are not making a difference in competition, however, Ellers shared.

“The algorithms are agnostic. They work in Berlin, in Hamburg, in Munich. They would even work in Singapore or Sydney. Therefore, we said let’s join forces, because it’s so expensive to build these algorithms with high-performance Artificial Intelligence that we only do it once, and then everyone has agreed to adapt them to his single business model.”

Promoting a data-driven culture in the newsroom is a critical aspect, Ellers emphasised — one that is much easier to achieve when various media organisations join forces.

“You see that others are making progress, then you take the good example from the other publisher and you tell your people, ‘Look, they have already achieved it and we can learn from them.’”

DRIVE: Digital reader revenue initiative

Dr. Christoph Mayer, associate partner and AI lead at Schickler, explained the joint initiative, DRIVE, put together by the eight regional publishers in Germany.

“DRIVE started with a lot of different questions to local publishers,” Mayer said. The needs were all very similar, revolving around how to understand readers’ behaviours and interested, how to collect and understand the data, and how to use that in a successful content plan that drives subscriptions.

The goal of DRIVE is to solve these questions for all the publishing partner companies.

“We can share costs, we can share best practices, and we can gain much more speed,” Mayer said.

DRIVE collects first-party data, performs analysis and experiments, optimises workflow in newsrooms, and provides dashboards and APIs.

“Everything we do with the data orients in three dimensions,” Mayer explained. “We are actually interested in which user consumes which content and in which context.”

From those three main data areas, more details are analysed such as content topics, time and day of week of consumption, devices, and trends.

“Everything we look at has direct impact on the newsroom. How can we work differently to provide a better product?”

DRIVE partners access the data through dashboards, which allow for direct comparisons between publishers. One of the key metrics identified is media time, which is the cumulative time an individual user spent on the Web site or app in the last seven days.

“It’s super simple, but it’s a great metric because it contains a lot of other metrics,” Mayer said. “It’s easy to understand for editors, and it’s directly actionable.”

Media time can also be used for personalisation, and is a very good predictor for both conversion and churn. Based on media time, DRIVE defines six user segments:

• Non-engaged: less than 30 seconds per week.
• Fly-bys: 30-120 seconds per week.
• Low-usage irregulars: less than three active days and 120-300 seconds per week.
• High-usage irregulars: less than three active days and more than 300 seconds per week.
• Loyals: minimum of three active days and 120-1200 seconds per week.
• Champions: minimum of three active days and 1,200+ seconds per week.

DRIVE is a joint project owned by all the publisher partners, and delivers actionable insights that transfer into the newsrooms, dashboards and open exchange of data, and algorithms and APIs for personalisation, Mayer summed up.

Case study: Using DRIVE for data-driven reader strategy

Mittelbayerische Zeitung in Regensburg is one of the publisher partners of DRIVE. Manfred Sauerer, chief editor and managing director of the company, showed INMA members how the joint initiative works at his organisation.

“Our journalists are working data-informed at the intersection of their expertise, and the dashboards and insights coming from data analytics,” Sauerer said.

The user segmentation is very important, he added: “These segments help us to focus on the most valuable user groups.”

Once the segments are identified and the data from them gathered, the team can put it to the test in terms of conversions.

“A champion converts 22x better than a fly-by,” Sauerer reported. Most champions evolve from the loyal and highly-engaged segments. This knowledge helps Mittelbayerische Zeitung determine where to put its marketing efforts and which user groups to target.

What about editorial?

“We don’t want to confuse [editorial teams] with a whole lot of metrics,” Sauerer said. “They have dashboards, but we see that it’s not easy to understand for non-tech-savvy editors.”

Therefore, the metric most heavily focused on by editorial teams is that of media time. It’s a simple metric for measuring progress, Sauerer explained, reflecting volume as well as frequency, and is easy for editors to comprehend.

“Editors get an easy indicator of how their articles performed. Specialised teams, of course, need more specific metrics to control the Web site.”

Those data-savvy specialised teams work with additional KPIs such as conversions.

“A greater data lake is the best thing for us to come very near to our users,” Sauerer said. “The next generations don’t want to subscribe to a newspaper — they are used to Spotify [models]. How can we have the next generations in our reach as paying, loyal subscribers? And the idea of DRIVE fits great.”

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .