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A number of publishers are harnessing first-party data in new, innovative ways, especially using look-alike modeling. The result? Increased revenue streams. BlueConic’s Sam Ngo explains more…
Much has been written about the impact of third-party data deprecation, growing consumer data privacy regulations, and the urgency to move to first-party data. In some ways, media and publishing companies are leading the charge on the type of transformation that businesses in all industries will need to implement to both endure and stay competitive in the post-cookie, privacy-first era. That’s because they’re on the frontline of feeling the impact on their revenue.
With an estimated $10 billion at stake, these companies are rethinking legacy organizational structures, auditing their technology stacks, and adopting new solutions, like a customer data platform, as the enabling technology for collecting, unifying, and activating first-party data and reader-centric strategies.
Let’s take a closer look at how some forward-thinking publishers are already using unified, privacy compliant first-party data to power their customer engagement and audience monetization strategies, and ultimately, create new revenue streams for the business.
Putting the reader in control
Over the last few years, there’s been a surge in publishers using algorithms to recommend content to their visitors. Often used to promote breaking news or viral content, these algorithms can be an effective way to increase page views and user engagement.
The problem is that people aren’t algorithms. Suppose you’re a Boston Red Sox fan who keeps getting recommended articles on the New York Yankees, or a vegetarian who keeps receiving recommended recipes made with meat? With black-box algorithms and no feedback mechanism to let the publisher know what you’re interested in (and what you’re not), you’ll continue to get irrelevant content. By using first-party data, including interest data, web behavioral data, and preference center data, media and publishing companies can give readers more control over their experience and therefore, increase their engagement long-term.
For example, one large, European-based multi-media company encourages readers to create a free account, where they can declare their interests and preferences. This information is then used to serve a more personalized, engaging experience on their site and in their mobile app that drives engagement, loyalty, and subscription revenue. Equally as important, users’ consent and data privacy preferences are collected in the same place – making it easy to suppress personalization if requested by the user.
Building high-value advertising audiences
While tactics like the one above may seem simple, determining who unidentified readers are and how to deepen their engagement is a critical aspect of a publisher’s ability to drive digital revenue. That’s because this rich first-party data can not only be used to deliver more personalized experiences for readers, but also to create a vastly higher-quality ad product for advertising partners.
Take a popular, U.S.-based multimedia company with radio, digital, and print publications in its network. Like many media and publishing companies, it experienced a decline in ad revenue at the start of the COVID-19 pandemic when some advertisers cut back on spending. In response, this business immediately started using the rich first-party data it had been collecting and unifying for years to develop new audience segments that were then sold to advertisers at a premium – effectively mitigating the revenue impact of ad dollars being pulled out of marketing at the same time. These tactics also created value for readers and advertisers: audiences were shown more relevant content, while advertisers saw better engagement with their ads.
Leveraging look-alikes
While audience monetization methods can help drive healthy, sustainable growth, some publishers may have audiences that are too small and specific for advertising partners. By leveraging first-party data for look-alike modeling, these companies can effectively increase the size of their audience pool.
For example, a large, multimedia company operating in Europe uses AI to score both known and anonymous visitors as they browse their website. The look-alike model is run on top of their entire first-party data asset and embedded into their segmentation and targeting capabilities. Using look-alike audiences, the company increases the reach of their advertiser’s content while providing a relevant experience for the user. Based on the success of these look-alike models, the publisher has since launched a dedicated look-alike model product that helps their advertising partners define target audiences, create custom segments, and activate those audiences across ad platforms.
Accelerating affiliate marketing revenue
Publishers and media companies that are already using first-party data to increase subscriptions and build high-value advertising audiences can also use this same data to support their affiliate marketing programs. Since they already know what content their readers are most interested in, they can target them with more relevant ads and product recommendations and earn revenue when those readers make a purchase from an affiliate partner’s site.
One large publisher that had already expanded into affiliate marketing to drive revenue took their program a step further by applying Recency, Frequency, and Monetary Value models to their first-party data. Using this sophisticated modeling approach, the publisher was able to target only those individuals who had made high-value purchases and those who purchased frequently – significantly reducing ad waste and increasing their paid advertising return on investment in the process.
The key to future growth
Although subscriptions and advertising will continue to account for the bulk of revenue for media and publishing in the years to come, the potential of first-party data to create new or supplement existing revenue streams offers significant opportunities. With third-party data deprecation, ever-increasing privacy regulation, and other disruptive forces putting their business at stake, now is the time for media and publishing companies to take control of their own destiny and focus on how they can leverage unified, privacy-compliant first-party data to create a more solid and lasting growth trajectory.
Sam Ngo
Director of Product Marketing, BlueConic
BlueConic is the market-leading pure-play customer data platform. Leading companies use BlueConic to transform the way their business teams understand and interact with customers in the privacy-first era. By making unified, actionable, and consented first-party data accessible across systems of customer insight and activation in real-time, business teams can deliver timely, personalized cross-channel interactions and drive positive outcomes from their strategic growth initiatives. Headquartered in Boston, U.S., with a European HQ in Nijmegen, NL.
