Using Decision-Driven Analytics to Maximize Ad Revenue

By Ajay Khanna, CEO and founder, Tellius

Today, digital publishers face a myriad of challenges created by new technology and evolving consumer demands—such as offering highly relevant advertisements, understanding new formats for advertising like Apple News and Facebook Instant articles, and growing customer loyalty and retention. They are also unique in that they need to keep both consumers and advertisers satisfied. To combat these challenges and improve success rates amongst both core audiences, digital publishers are looking for enhanced analytics capabilities that will help them capitalize on ad revenue.

In the age of growing data scientist shortage, digital publishers need solutions that place the power of data-driven decision-making in the hands of the everyday business user.  Ideally, this approach would allow data experts and business teams to collaborate in real-time on key data insights so they can more effectively discuss new decisions and actions they would take. But identifying solutions that can cater to both advanced data scientists and business users—while simultaneously offering faster, more valuable insights—is no easy task.

Enter decision intelligence. Decision intelligence is a modern analytics approach that uses artificial intelligence (AI) and machine learning to help publishers move beyond high-level metrics such as CPM and CPC so they can get more granular insights into how to sell to their audience better, drive higher engagement, and boost advertiser spend. This will allow for a better understanding of how to sell their audience and engagement to advertisers. Decision intelligence gives accelerated data insights, so businesses know what metrics changed, the reasons why things changed, and subsequently prescribe the next best action to drive towards targeted business outcomes. Here’s how digital publishers can apply decision intelligence to overcome challenges related to data deluge and a variety of digital customer touchpoints to enhance their advertising initiatives and boost revenue.

Obtaining deeper demographic insights

Understanding who is viewing ads is an essential piece to selling engagement and ad visibility. While many analytics tools provide demographic information to advertisers in an effort to sell to specific audiences, decision intelligence goes beyond the high-level metrics to uncover deeper consumer insights that are more likely to engage and transact with specific ads. For example, decision intelligence can uncover what products women between the ages of 18-25 are more likely to engage with, or why certain products see higher engagement rates than others.

Not only have these insights become a critical selling point for advertisers whose products and/or services are catered towards specific audiences, but they also give teams the power to go beyond basic viewability metrics. This is instrumental to helping advertisers see value in ad placements to understand where, when, and how to push certain messages out to key audiences.

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Personalizing content to drive engagement

By obtaining deeper demographic insights using decision intelligence, publishers can leverage that data to recommend additional content to viewers. This content would be personalized to their preferences and keep them on page longer. If, for example, a platform can see that men over the age of 40 like to view content related to sports, they can engage with other sports-specific brands and speak to high sports content engagement rates. This strategy not only maximizes ad revenue, but also gives consumers more of what they want—ultimately keeping them engaged longer.

Even though data analytics within publishing is not a new trend, taking a more modern approach using decision intelligence puts insights closer to decision makers who can reap the benefits of data faster and better engage with a primary sales target: advertisers. It can help them overcome key challenges that they face today such as an oversaturation of irrelevant ads and rising publishing competition.

Setting better reserve prices in ad auctions

Predicting cost per thousand impressions (CPMs) to set reserve prices can be challenging for publishers who have complex pricing models based on hundreds of inputs. With the CPM pricing model, publishers are paid based on website traffic. Each time a visitor sees an ad, the advertiser is charged a predetermined amount. Decision intelligence streamlines this process by using automation to combine attributes—like city, monetization channel, the advertiser buying the ad space, the device, and even operating system— and uncover what makes the most profitable impressions.  With the ability to draw correlations between various attributes, publishers can use decision intelligence to uncover what truly drives the best CPM to set appropriate reserve prices and ensure they are maximizing profitability.

Companies should take a highly analytical approach to make decisions regarding how to optimize and improve their advertising initiatives. Unfortunately, in the world of digital publishing, obtaining insights from the vast data both consumers and advertisers create isn’t easy. Decision intelligence allows publishers to create solutions that bring the power of data back to the everyday business user, ultimately ensuring all advertising goals are met.

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