Additional Contributor: Bill Reynolds
With the sun setting on cookie-based tracking, marketers stand to lose many of the granular touchpoints they’ve enjoyed over the past decades, and stitching together reliable insights will become more challenging as data continues to silo.
As brands contend with multiple sources of truth, the smart and strategic use of attribution, incrementality, and comprehensive, transparent, and repeatable control testing provide solutions for media effectiveness.
Attribution and incrementality provide useful metrics to inform media mix planning and budget allocation. For marketers, attribution indicates the touchpoints that contribute to a conversion. Incrementality adds insight into how many prospects convert due to marketing efforts, above and beyond what would happen if there was no advertising at all.
Here’s what’s next in attribution, incrementality, and testing to help you confidently stay ahead of the increasing data fragmentation and the eventual cookie-less landscape.
Attribution models to track conversions are evolving.
Cookies had been indispensable in dividing up conversion credits during the heyday of multi-touch attribution. Even though Google has once again delayed the target date for third-party cookie phaseout (now to 2024), their availability has already been declining for a while, and this model will soon be completely disabled. In general, attribution is impacted considerably by the loss of cookies.
This new environment calls for a novel approach. In October 2021, Google announced they’d be moving away from static, rule-based conversion models in favor of dynamic, data-driven attribution based on live algorithms they’re developing. Though complete phaseout isn’t expected until at least 2024, changes started to affect Google Ad accounts in early 2022.
In fact, the platforms themselves are still grappling with the best measurement solutions in a cookie-less future. For enterprise brands with a massive volume of touchpoints, data-driven analysis at scale has worthwhile merits. However, data-driven analysis can be trickier for mid-market businesses where the number of touches can be much more limited.
Beyond the immediate nuances of relying on data and the analysis that the major platforms solely own and control, there is an inherent risk in putting all your eggs into one basket. As with any investment, diversification is a smart strategy to both optimize opportunities—and mitigate risks.
Rule-based attribution — appreciated for the transparency of its assumptions — won’t be going away entirely. While Google’s default mode is changing, marketers can still opt to measure results based on attribution methods within the platform, such as:
Last-click gives the most credit to the last touch/last ad seen. The most commonly used method, last click negates the fact that the last touch was most likely the culmination of all prior exposures. It has been over-credited and has caused marketers to look at other methodologies.
First-click gives the most credit to the first touch.
Linear equally divides up credit among all touches.
Time-decay indiscriminately weights credit based on time-lapse, for example, 100% credit to same-day touches, 50% credit to touches that happened one day prior to conversion, 25% credit to touches two days prior and is not used very frequently.
Position-based assigns 25% credit to the first touch, 25% to the last touch, and 50% among the touchpoints in between.
Mid-sized businesses that previously relied on intuitive — albeit somewhat arbitrary and stagnant — rule-based attribution models will need to rethink their approach, given the next generation of more dynamic measurement forms.
Google’s closed ecosystem collects data touchpoints across AdWords, Gmail, and the display network to attribute conversions statistically. Any advertiser has access to Google’s custom analysis regardless of size. With the loss of cookies, data resides solely with Google and other walled garden platforms, such as Apple, Amazon, and Meta.
Incrementality testing gains importance to determine the best media mix.
Although it can be challenging to measure, incrementality paints a more comprehensive picture of your marketing success. Determining budget and channel spending is as old as advertising itself — and many of the tried-and-true measurement tactics produce reliable results for sound decision-making today.
While attribution is being reworked with the loss of cookies, incremental measurement is less affected. The difference? Cookies track what happened (attribution), not the added performance over what would have happened without advertising exposure (incrementality).
Did you drive more results – downloads, shares, sales, etc – with your campaign than if you had done nothing? Incremental measurement can answer this question and more.
One of the best methods to measure incrementality is to compare conversion rates of “exposed” vs. “holdout” groups across audiences, publishers, and creative channels. This is known as a “Control-Test Experiment” (CTE).
CTEs are an excellent way to measure incrementality because they estimate how likely someone is to convert when exposed to a certain campaign variable while also measuring how many people would have naturally converted without advertising exposure at all.
Since there was no way to do a holdout in last-click conversion environments, over time, marketers had lost touch with some other time-tested strategies for crediting conversions and determining media mix.
The two most common types of CTEs are Geographic Tests and Audience Split Tests:
Successfully relied upon for decades, geographic testing is one of the most straightforward ways to measure incrementality. For example, by increasing AdWords spending in one state (the treated/exposed group) but not another (holdout/control group), marketers can determine whether a change drives incremental results. Geographic tests are easier to implement than audience split tests, but they rely on the test designer’s ability to choose geographic areas that are as similar as possible. For example, one wouldn’t trust a geographic test in which the selected geographies were San Francisco and Oklahoma City due to their disparate demographic and psychographic makeups.
Audience split-tests work by deciding to target ads to only a specified audience but then splitting that audience into a group that will see the ads and another smaller group that won’t. The group that doesn’t see the ads is the “holdout” group, and it typically encompasses 10% of the overall audience. Results for the target audience and the holdout audience are measured separately. Marketers hope that the target audience will have a higher conversion rate than the holdout audience. If that happens, and if there is enough data for statistical significance, then the difference between the target audience and the holdout audience is the incrementality that’s driven by the advertising. Audience split tests can be more accurate than geographic tests because they remove the variability inherent in different geographic areas. However, they do have a higher potential for cross-contamination between the target and holdout groups, especially if the advertiser is simultaneously running other advertising that may be seen by either or both groups.
How data warehouses can help media measurement
As marketers work within different platform silos — all with their own data sets, audiences, and innovative measurement algorithms — natural competition encourages every platform to present itself in the best possible light. Marketers will need to work harder to garner objective cross-channel insights and craft the optimal media mix.
- How do we make an apples-to-apples comparison from each platform to understand the true incremental contribution of each channel, campaign, and tactic?
- How do we consolidate cross-platform insights to form big-picture strategies?
While there are inherent challenges in measuring the effectiveness of advertising and choosing an optimal media mix–both with and without cookies–data warehouses can offer a solution. Data warehouses provide a big value-add by aggregating all the various sources of a brand’s online and offline results, which is particularly useful during control-test experiments.
Warehouse partners then assess incrementality using statistical algorithms based on data-driven inputs and outputs to derive the baseline, draw comparisons, and credit conversions.
The challenge is many businesses are unable to access these services on their own due to cost. Agencies, like Amsive Digital, with access to data warehouse services through economies of scale, put these enterprise-level solutions within reach.
Agency support provides the widest range of future options
Marketing will continue to be a blend of art and science as we determine the best sources of data, methods to measure efficiency, and strategies to translate results into ad spending.
When looking for an agency partner, assess its depth of experience working with platforms and channels, use of top-tier tools for objective cross-platform measurement, access to data warehouse services, and its agency-platform partnerships, offering even more in-platform support. With the right digital agency partnership, enterprise-level knowledge and support are well within reach and can be harnessed for superior mid-market results.
For example, as a Google Premier Partner, Amsive Digital receives in-depth product education, exclusive industry-leading insight reports, invitation-only executive experiences, and early access to new product betas. These types of business partnerships allow us to offer advanced support to our clients.
In an ideal world, everything runs smoothly all the time, but the reality is that additional support is invaluable in today’s environment where issues with context, mistakenly blocked ads, or pulled-down content can arise. The ability to get a Google customer support rep on the phone to straighten out a concern in real-time is a huge asset. While medium-sized businesses may have thought it was out of their ballpark, it’s not with the right agency partner.
From the major platforms to data warehouses, access to premier partnerships and capabilities addresses data fragmentation and maximizes value. Amid a future where there are few guarantees or certainties other than more changes to shake up how the world does business, a steady and informed path forward includes flexibility and vital access. A rock-solid digital media partner can help set your strategy and optimize your budget for the next generation of digital.
The shifting media mix landscape doesn’t have to mean less impact for your brand. This is the third in a series of five articles diving deep into measurement today—unpacking what works and what’s next. So, stay tuned!
Until then, discover more about The Future of Measurement: How to Uncover Cross-Channel Insights With Media Mix Analysis.