As the advertising landscape keeps evolving toward user privacy, the methods we used until now to measure the effectiveness and value of our ad campaigns also need to be rethought and adapted.
For the longest period, deterministic attribution models were the solution to the measurement question. But the traditional last-click attribution models no longer provide an accurate picture of a campaign’s results in all scenarios. With Apple’s introduction of SKAN (SKAdNetwork), last-click attribution is extended to LAT audiences, but the flexibility and completion of methods used to evaluate a campaign and to attribute overall results change.
Uplift measurement has long been fully dependent on last-click and/or device identifiers. This article discusses the pros and cons of the various ways of measuring programmatic advertising campaigns in scenarios with and without user identifiers.
Attribution Models
Advertisers can choose from different models as e.g. first-/last-click or multi-touch with linear/W-/U-shaped/time-decay distributions. They all try to solve the question: to which channel and in which proportion to attribute campaign success? However, traditional attribution models are naturally not 100% trustworthy when it comes to proving incrementality.
In a LAT environment without the availability of user identifiers, deterministic methods don’t hold. The only available attribution model for this scenario is SKAN (SKAdNetwork), which is the only real last-click MMP made available for iOS. SKAN submits postbacks without IDFA or any advertising ID, and its integration is essential to receive last-click attribution and performance insights for no-IDFA campaigns.
In a LAT environment without the availability of user identifiers, deterministic methods don’t hold. The only available attribution model for this scenario is SKAN (SKAdNetwork), which is the only real last-click MMP made available for iOS. SKAN submits postbacks without IDFA or any advertising ID, and its integration is essential to receive last-click attribution and performance insights for no-IDFA campaigns.Attribution presents some challenges and gives us a skewed point of view on the profitability of campaigns. Incrementality or impact measurement allows advertisers to have an accurate perspective on the campaigns’ contribution without relying on deterministic methods. Therefore, it is a better albeit more laborious option for advertisers keen on having a pulse on the incremental value that their advertising strategies generate.
Reasons to Use Attribution Models
Straightforward Calculation
Attribution models follow a predefined and simplistic calculation of the credit for each channel. Suppose you are running a user acquisition or retargeting campaign with limited resources to crunch and dig deep into data, the attribution model can be a good choice as long as you’re aware of its limitations.
Ease of Use
Regardless of the size, duration, and budget of a campaign, advertisers can rely on MMPs to follow their attribution method of choice and obtain results in a timely fashion.
Equality Across Channels
Despite the many discussions around the benefits and downsides of each attribution method, deterministic attribution as a whole has been the standard practice among advertising players.
Why You Need to Look Beyond Attribution Models
Assumption Dependency
Deterministic attribution models rely on certain assumptions of the value of touchpoints. Some give a maximum share to the last touchpoint, some distribute the share among different touchpoints. As marketing analytics move towards a standard where decisions are data-driven and need a higher level of accuracy, the assumption-based approach entails unnecessary bias.
More Click-focus, Less Intent-focus
The number of clicks remains the fundamental block of attribution models while ignoring the conversion intent of the user. It limits the marketer’s approach to understanding user behavior holistically and linking it to campaign efforts and impact.
Ignorance to Immeasurable Contributors
There are various indirect contributors when running an advertising campaign, including your existing brand value, word of mouth, TV, offline campaigns, etc. Most attribution models often turn a blind eye to media-mix contributions to the success of a single digital campaign.
Traditional Attribution Models Aren’t User Privacy Ready
As mentioned before, attribution models that rely on tracking user IDs won’t apply to LAT traffic. SKAN is the last-click option still available for iOS, and it comes with some limitations of its own.
Incrementality Measurement
Uplift measurement might not represent a pragmatic day-to-day replacement for attribution, but it is a preferred method to evaluate the real incremental contributions of advertising campaigns.
Measuring incrementality provides more in-depth insights and better strategic orientation, for accurate optimization and budget allocation. Uplift measurement also allows advertisers to measure their organic baseline against paid results to avoid cannibalization.
Different methods for calculating incrementality
Different methodologies are used to calculate uplift: ITT (Intent-to-Treat), PSA (Public Service Advertisements or Placebo Ads), Ghost Ads, and Ghost Bids. Incrementality methodologies have evolved to provide a solid measurement, the lowest noise possible, and the lowest selection bias so advertisers get the highest level of accuracy.
The methodologies differ, but they’re all founded on the same principle. Similar to how TV ads are measured, uplift is based on running ad campaigns that can be measured using identifiable sub-markets (treatment group), while leaving other sub-markets unexposed (control group).
In simpler terms, this data-driven approach relies on only exposing a test group to advertiser ads while leaving users in the control group untouched. The difference in outcome between the test and control group is the incremental lift.
Measuring Incremental Uplift in the Post-IDFA
Uplift measurement is also a methodology that supports advertisers in measuring their advertising efforts in a user id-less world in a more comprehensive fashion.
Since SKAdNetwork is the only MMP for LAT campaigns on iOS. It provides partially deterministic attribution for installs only. Measuring incrementality becomes critical to get a real picture of campaigns’ results.
The uplift measurement methods mentioned above rely on IDFA and user-level information. For LAT audiences, traditional uplift tests are not possible as they rely on device IDs to distinguish test and control groups. However, the methodology can be adapted by basing itself on synthetic control groups.
Time series analysis and synthetic control groups can provide valuable information about the ROI of your advertising campaigns and overall mixed media model performance. They have to be implemented on the advertiser side as they rely on back-end data. And they give advertisers control over measurement and provide relatively fast feedback depending on the app product, campaign incentives, and usual conversion turnaround time.
Completely SKAN ready, RevX embraces the user privacy era and is prepared to consult any app businesses on setting up, conducting, and analyzing uplift measurement. This way, advertisers are always sure to have real data insights on their side to craft better strategies and optimize their campaigns for incremental results.
Why Incremental Uplift Is The Way Forward
Incrementality is a sturdy tool to have in your app marketing arsenal. One of the factors of resistance to measuring uplift is its perceived level of complexity but, with the help of an advertising partner, is very much possible to conduct as a standard practice.
As the advertising industry demands and upholds ‘gold standards’ of analytics, gathering these granular campaign insights is important. Incrementality can track lift by channel and in addition consider media-mix spillover effects.
Incremental uplift measurement vs. attribution: a recap
Attribution vs. incrementality measurement is not a traditional ‘Android vs. iOS battle’, where one needs to pick aside. These two approaches are both available to support your campaign measurement and analysis needs. The decision on which one to use depends on what an advertiser needs to track to understand the validity and relevancy of its marketing investments.
Attribution is a helpful methodology to track the performance of different channels in your campaign while incrementality takes you a step closer to precision in ROI tracking.
When you run a more extensive range of campaigns across diverse channels that involve deeper audience insights and multiple touchpoints, uplift measurement gets the upper hand. As an app marketer with a clear focus on ROI, incrementality is a clear winner. When it comes to the real impact of your campaigns, how you measure that uplift makes a world of difference to the success of your strategy. After all, nobody wants to spend their budgets blindly.
Mobile marketers need certainties, and incrementality measurement provides the insights needed to find the link between your programmatic advertising efforts and increased ROI. Use incrementality measurement to your advantage and pave the way to future and sustainable app growth.
Shivani Salhotra
