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Obtaining Incremental Lift In the Wake of Adtech Data Aggregation

By December 28, 2021December 30th, 2021News & Updates 12 Min Read

The loss of row-level data doesn’t mean the end of accurate  performance insights

The adtech industry has been undergoing monumental changes in how it accesses, measures, and analyzes data. While there have been, and continue to be, disruptions in how advertisers receive campaign data, there are solutions to obtaining detailed insights through incrementality testing. Although traditionally cumbersome, modeled synthetic control groups enable accurate and more affordable lift measurement.

In today’s real-time attribution system, the question of causality, that is, did an ad cause a conversion, is left unanswered. Real-time attribution simply tells you the user was in the vicinity of the media served; lift measures the value of the media itself. The two are not interchangeable. 

To answer the question of impact means performing incrementality testing, which historically has been time-consuming and costly. With Kochava Foundry’s MediaLiftTM, however, we can perform incrementality testing using modeled synthetic control groups.

The benefits of using synthetic control groups

With traditional incrementality testing, an advertiser must withhold advertising from a holdout (a.k.a. control) group which is often 10% to 20% of the total addressable target audience. Withholding advertising to a portion of your audience is costly as you are potentially losing revenue from them. 

 

MediaLift avoids this by leveraging an innovative device scoring system within the Kochava Collective identity graph to build a modeled synthetic control group that mirrors the attributes and behaviors of the exposed group who received the ad. 

Device scoring also eliminates having bias creep in between the exposed and control groups, which is an inherent problem with traditional incrementality testing. When the control group is carved out before the campaign is run, the final test group that actually gets reached by the digital ad campaign often ends up looking a lot different than the control group.

Another costly aspect of traditional incrementality testing is the serving of public service announcements (PSAs). PSAs are often served to the control group as a way to compare their behavior against the exposed group’s. However, this practice is not even possible with some marketing channels such as with out-of-home (OOH) billboards. 

Because MediaLift’s synthetic control groups are based on devices that have exhibited similar behavior to the exposed group’s, no PSAs are needed to compare behavior between the two groups. This ability lends itself to channels where measurement has been based more on estimates, such as OOH and television. By applying the mobile data available in the Collective, advertisers can see how OOH and/or connected TV (CTV) campaigns influence their users on mobile.

  • Minimized opportunity cost: no “holdback” groups who may have otherwise received ads (in the case of a pure holdout)
  • No hard cost on having to spend media on a non-responsive group (in the case of PSAs)

MediaLift incrementality testing for OOH

Like many other industries in 2020, the out-of-home (OOH) industry took a hit, but it has been healthily rebounding in the past year. As the OOH industry recovers (with OOH still leading the way in comparison to digital OOH), the ability to measure it alongside mobile will help amplify your marketing strategy. The two channels no longer have to be siloed, and measurement will show their correlation.

Billboard publishers have been using MediaLift to prove the efficacy of their clients’ ad spend. To perform incrementality testing, the Kochava Foundry team defines a universe of known devices that were in the vicinity of the billboard display. This group represents the exposed group eligible for attribution.

Outdoor signage and incrementality lift

With the geo-location of the campaign billboards, the Foundry team isolates the devices that were in the vicinity of them. Using the Device Scoring system in the Collective, they create a modeled control group that mirrors the exposed group in terms of device variable composition, geography, user behavior, etc: These two groups are theoretically the same, with the exception that one of the groups encountered the OOH ad(s).

To validate the control group, the team matches the devices of both groups based on the score. Next, they look at performance to see how the two groups differ before and after the ad was displayed. In the graph below, we see the exposed and control groups overlayed with each other and it’s clear that the two mirror each other in behavior and are unbiased prior to the OOH ad campaign exposure. A separation may occur after the media is displayed. Sometimes, the separation is greater as shown below. In the graph, it shows the top group engaged but not the control group, the reason for this was a natural downtrend in the business.

MediaLift Incrementality testing

In addition to measuring impact, the team measures the number of exposure events, meaning events derived from devices exposed to the billboards, the number of times a device was exposed to the media, and lift specifically from ad exposure. 

MediaLift for CTV

Connected TV (CTV), television that runs on the internet, is rapidly growing as the primary mode to access entertainment, and likewise, brands are adding it to their media mix. With major changes in the industry regarding user data privacy coming from Apple, Google, and Facebook, many advertisers are likely reallocating some ad spend to CTV as well. 

CTV and over-the-top (OTT) streaming services were already increasing in popularity before the pandemic, but it has catapulted their growth since. CTV is arguably the next emerging market to capitalize on much like mobile was back in 2011 and 2012. The beauty of its growth now is that there are mature mobile measurement tools to measure the second screen trend capturing the influence of CTV on mobile. Additionally, the level of insight on a household basis is highly detailed.

To measure lift on CTV, the Foundry team create two similar groups of eligible devices as they do for OOH campaigns. For CTV, they can use IP addresses to determine the universe of devices eligible for attribution within a specific region and then use that group to create the control group from the Collective. The data is scrubbed of phone carriers and devices that don’t have CTV capabilities until they are left with devices by household. Once they have their exposed and control groups, they can determine lift.

Application of MediaLift results

Although incrementality testing has been MediaLift’s focus, its applications go beyond it. Advertisers can upload their lift results in the Collective to see where there are mismatches between the exposed and control groups for improved targeting. MediaLift insights also include the ability to attribute conversions to a billboard or other outdoor signage (also called a “gross match”), such as airport signs, taxis, elevators, etc. It is also useful in observing trends in a new market. 

Is MediaLift right for you?

With the right data available, MediaLift is a more efficient and affordable way to determine lift and campaign impact to answer causality. To obtain a MediaLift analysis, advertisers and publishers need to supply an ad signal (data stream of impressions and clicks) and a conversion asset (eg, install), and a way to tie the two which is typically an IP address.

Keep in mind that one analysis is not evergreen but is a snapshot of distinct moments of activity, so periodic assessments are more practical to see the history of marketing’s impact. 

For more information about MediaLift, visit the Kochava Foundry page, or contact us or your Client Success Manager.

Grant Simmons – VP of Kochava Foundry