Programmatic Display: Make the Most of Your Data With Today’s Technology

Programmatic Display: Make the Most of Your Data With Today’s Technology

By 2019, 50 percent of display ads are forecasted to be transacted programmatically. Access to data and technology is driving this growth. Advertisers have access to increasing amounts of data, including site-visitor and partner information, demographic- and business-attribute data, and even offline info such as customer-relationship management (CRM) data — and all that can be used for targeting. Advertisers also have access to advanced technologies — like demand-side platforms (DSPs), ad exchanges, and data-management platforms (DMPs) — that they can use to target users in real time across channels and devices.

In North America, $11 billion is projected to be spent on programmatic advertising this year, with forecasts of $30 billion by 2019. Yet, at the end of last year, when marketers were asked their levels of understanding regarding programmatic, only 23 percent used programmatic actively, 36 percent were aware of programmatic but were not using it regularly, and 41 percent weren’t using programmatic at all. Programmatic is in its early stages, and its level of awareness is still growing.

During a presentation I put together for Adobe Summit 2016, we explored the programmatic world and how it helped Redbox, Chegg, and eHealth meet their advertising objectives. Here are some of the insights we gained:

Redbox: Time Savings and Better Performance Through Site-Analytics Integration
Redbox deploys audience targeting via Adobe Analytics, resulting in time savings and improved ad performance.

Redbox is a movie- and game-rental business that is focused, like many businesses, on driving a greater return on ad spend (ROAS) from its digital marketing programs. They target a number of different audiences — from online users who visited and interacted with the website to entertainment junkies to families — for display retargeting and prospecting programs. This often requires that their information technology (IT) department put relevant, trackable content on their site to build audience segments.

Redbox already used Adobe Analytics, which tracks, organizes, and reports on what different segments of customers do. Instead of deploying new tags for tracking, Redbox leveraged the integration between Analytics and the Adobe Media Optimizer DSP and used the existing Analytics audience segments for retargeting.

This integration saved Redbox time when creating, launching, and testing new segments; and it drove better performance due to access to granular audiences — all while reducing reliance on their busy IT department. Using Adobe Media Optimizer and Analytics segment targeting, Redbox achieved:

  • A 30 percent lift in return on ad spend (ROAS) for retargeting campaigns,
  • A 3x lift on ROAS for a specific campaign, and
  • More than 8 – 10 hours/week of time saved.

Chegg: Using Multiple Data Sources to Effectively Reach High-Value Audiences
Insight from the programmatic process helped Chegg infer and expand its audience segments to improve returns and lower acquisition costs.

Understanding your audience segments is a key factor in programmatic advertising. In the ‘old days’, Chegg — an online business that supplies textbooks, online tutors, internship opportunities, and one-on-one help for students — bought advertisements from specific websites where they assumed students would be. They didn’t have insight into the return on investment they would receive, and they didn’t know what they were getting from their advertising buys. Insight from their programmatic ad-buying solution helped Chegg infer the data they needed.

Chegg wanted to figure out the type of content they had on the site and where students were actually going. For example, Chegg was interested in knowing whether students were searching for and viewing a particular science, technology, engineering, and math (STEM) book as well as other relevant information about the students.

Next, Chegg looked at STEM students and student grade levels. They identified both of these characteristics as being worth a certain amount of investment. However, it wasn’t until they merged the information that they actually started finding real value. They found that, while a freshman is worth about 1.5 times the lifetime value of a student, and a STEM student is worth about 25 percent more than that, the group that holds both freshmen and STEM students was found to be worth two times as much — greater than the sum of its parts.

They can now put their different groups into a ROAS curve to determine who they should be spending money on first. Overall, they saw a 22 percent drop in acquisition costs (and when you’re spending millions of dollars in this space, that’s a huge savings). Even better, the students they are targeting have the potential to give them twice the amount of returns or loan-to-value (LTV).

eHealth: Data Transparency and Integrated Ad Stack Drives Smarter Decision Making
In early 2015, eHealth — the nation’s first and largest private health-insurance exchange — overhauled its remarketing program. Since only a small percentage of people actually convert on their first visits, remarketing is very important to them. To achieve this, eHealth needed to reengage with its customers and rekindle the health-insurance conversation.

Prior to working with Adobe, eHealth was using five different platforms for display advertising. This resulted in reporting challenges such as duplicate counting of conversions, network competition in bidding, and discrepancies on audience and performance reporting. By using a combination of Adobe Analytics, Adobe Media Optimizer, and Adobe Audience Manager, eHealth has a more unified view of its audience and reporting across its media. All of the pixeling throughout the site is done uniformly, so activity is being recorded equally, allowing eHealth to drill down into its audience and form a more granular definition of who they are.

As an example, they’ve taken a look at their “quoter” audience. A quoter is someone who has visited the site and looked at various health-insurance plans but hasn’t actually purchased a plan. By knowing their customers, they’ve been able to segregate quoters who are moms versus dads. This allows them to serve up messages and creative that would appeal more to each audience segment, resulting in higher conversions.

In the end, this newfound transparency into audiences and media buying resulted in efficiencies and cost savings, whereby, eHealth reduced media-buying costs (CPMs) by $.22 during their last open-enrollment period and beat their cost-per-acquisition (COA) target by 19 percent.

To learn more or watch the full session, visit our Adobe Summit 2016 archive and look for session 201 under the Programmatic Advertising track.

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