iOS14 has certainly provided us an opportunity to re-examine last touch and how well it delivers truth in user acquisition. The cold, hard truth is that last touch doesn’t deliver truth and it is an entrenched approach to which mobile marketers have become addicted. Big thanks to Gamesforum’s John Speakman for exploring this topic with MetricWorks CEO, Brian Krebs. Visit Gamesforum Online to view this recording as well as webinars on other relevant topics.
In this webinar, we challenge the general perceptions of marketing effectiveness under last touch including its bias to SANs (self-attributing networks) and high engagement ad formats. As Brian points out, at the macro level, there has been a significant growth in the number of ad channels for marketing spend. With that, there has also been a greater overlap in audiences across these channels. Consequently, marketers are often paying twice to reach the same subset of eyeballs. Now that IDFA will be deprecating soon, it is time to adopt a new form of measurement, namely, incrementality to understand the true value of your media spend. The take-away is that iOS14 can actually be the driver to pursue a new opportunity in the form of measurement that is grounded in data science and most importantly, in truth.
Submit your questions below. You can also request a demo to learn more about MetricWorks incremental measurement solution that is a ready-to-use, turnkey, drop-in replacement for last touch. It involves no change to your marketing or UA processes. We look forward to helping you ensure that your marketing is truly effective in the post-IDFA era.
How do you know if your mobile marketing is effective? Well, the best way to ensure that is by adopting incremental measurement. View our SlideShare below to get a better understanding of incrementality. We’d love to hear your feedback. Schedule a demo or submit your comments and questions below.
Are you ready to get started before the lights go out post-IDFA? Request a demo below of the ONLY incremental measurement solution that does NOT depend on IDFA or GAID. We look forward to helping you ensure that your marketing is truly effective in the post-IDFA era.
The mobile advertising industry has always been a dynamic one that has attracted brilliant minds to embrace its constant change and bring to life businesses that wrestled with the messiness of advertising data to deliver increasing value as they evolved. Now is no different. So we think it is important that as we approach this IDFA/iOS14 cliffhanger, we commit once again to center ourselves, continue to embrace change and innovate.
Let’s begin by taking stock of the positives of this upcoming IDFA change that Apple has postponed till early next year. Firstly, it offers increased user privacy. This is in alignment with our core values as end users ourselves and as a mobile ad tech company. Secondly, it allows the industry to ditch the issues with last click attribution and forge a new chapter of measurement innovation.
Thirdly, we want to reassure you that MetricWorks’ platform, UA Command Center (UACC), will NOT be negatively affected by iOS14 and will still provide all of the value it does today. Additionally, our new measurement module will ensure that you have granular revenue visibility in order to continue to successfully optimize and execute. You will see more detail about this lower down in this post.
Our Quest For The Future Of Measurement
The planned IDFA restrictions, likely to roll out in early 2021, introduces a huge but welcome change to the mobile advertising ecosystem. After an Apple user upgrades to iOS 14, any iOS 14-compatible app that wants to access their device’s persistent identifier (IDFA), most often for the purposes of retargeting, user profile linking, or measurement, will need to ask for their permission (as shown in the example below).
This presents an interesting challenge for the industry since so many players in the ecosystem rely on the IDFA to varying degrees. At MetricWorks, we’ve focused on how measurement data will flow in the new post-IDFA world. Our ability to provide end-to-end UA automation including LTV prediction and bid optimization for our advertisers, at the country and publisher app level offered by SDK networks, is contingent on accurate, granular measurement. Based on the spirit of Apple’s new terms, we decided to evaluate this challenge with the assumption that MMPs will no longer be able to send us events with attributed channel, campaign, country, and publisher app information. In its current state, SKAdNetwork, Apple’s on-device measurement offering, does not seem to provide enough granularity, nor does it handle post-install events well, making retention and LTV prediction impossible.
Early in our search for a suitable measurement solution, we identified incrementality as an important piece of the puzzle. For those not as familiar with the concept – incrementality refers to the incremental value directly caused by each advertising touchpoint in the user journey. This is impossible to measure in the last touch attribution model (Fig.1 below), which credits the final touchpoint with 100% of the value for the user without consideration to the possibility that the user could have been acquired with fewer, more valuable touchpoints or even zero advertising as an organic install.
A methodology known as incrementality testing is an ideal solution to prove the causal relationship between advertising touchpoints (ad buys) and uplift. It prescribes a rigorous scientific process similar to randomized clinical trials used by pharmaceutical companies where a population of users is randomly split into a test group that is delivered an ad and a control group that receives a “placebo” (often an unrelated public service announcement or PSA). However, it can be costly since, if you’re displaying PSAs, you still have to pay for those impressions. There is an even bigger problem with the advent of iOS 14. Most forms of incrementality testing require a large list of device IDs so that the audience can be split. iOS 14 will make this difficult to accomplish.
Another powerful tool in the measurement toolkit that also considers incrementality is media mix modeling (MMM). This technique uses regression models to find correlations between ad spend and business value. As a “top-down” technique (Fig. 2), versus a “bottom-up”(Fig. 3) approach like last touch attribution that works at the device level, it eschews device IDs in favor of aggregated data, and is therefore naturally aligned with user privacy. As you can see in Fig. 2 and Fig. 3 below, both top-down and bottom-up measurement techniques attempt to allocate the same users and app activity data (installs, opens, revenue) to the same four campaign/publisher app combinations in the proper proportions, but come up with different answers.
Key Idea:Based on the measurement output of the two different methodologies, we can see that they somewhat agree on the value of ironSource campaign B, publisher C (red). However, ironSource campaign A, publisher A (purple) didn’t get credited much in the bottom-up last touch methodology (just 1 install with little revenue) while top-down shows that, even though it might not be getting the last touch, it is providing significant incremental value. On the flip side, bottom-up attribution gives Vungle campaign A, publisher A (orange) a solid amount of credit for last touches, but statistically, it is providing almost no incremental value. We would have acquired those users either through other campaigns or organically anyway.
Coming back to the top-down media mix modeling (MMM) technique (Fig.2), let’s look at its advantages:
Requires few inputs – mostly just a time series of spend and target outcome measurements.
Robust to incongruities among ad channels, both online and offline, in terms of functionality and data availability.
Can be used to predict the change in outcomes as a function of different spend inputs which is quite handy for planning purposes including what-if analysis.
Can also be used in conjunction with other algorithms to optimize towards a given goal, which can be used for budget allocation optimization.
MMM has its own problems though, which also eliminated it from contention in our quest for the future of measurement. When we attempted to apply it to mobile app advertising, some major issues became apparent including:
Requires several years of data at a minimum due to aggregation at the week or month granularity.
Not used for quick decision making since it takes weeks or months to update a model with new data.
Usually custom built for an individual advertiser by very expensive specialist consultants.
Can only prove correlation, not causation (eating seafood may be highly correlated with personal wealth, but that doesn’t mean eating a lot of seafood will make someone more wealthy).
While MMM wasn’t a perfect fit, we recognized early on that the MMM concepts held a lot of potential. The key is that we found that most of the downsides could be mitigated through a combination of creative feature engineering and automated model validation through constant backtesting and live experimentation.
Keep in mind that the entire measurement process including modeling and validation must be completely automated in order to be scalable. No need to worry though because our existing automation technology in UACC is being enhanced to make this a powerful reality for you.
Our Solution For Mobile Measurement
Thank you for bearing with us and making it this far. Let us now look at the high level overview of our solution:
We believe that measurement of campaigns at the country and publisher app level (when applicable) remains critical for UA decision-making.
Compatibility with the current iOS14 technical specifications and alignment with the spirit of the new rules are equally crucial for any future measurement solution including ours.
Regression models similar to those called for by media mix modeling can be augmented with techniques that address problems unique to mobile app advertising in order to finally deliver a measurement solution that considers incrementality.
Using daily data helps solve the data volume issue inherent to MMM and allows the model to be updated more quickly so it can inform the types of fast decision-making required by UACC’s LTV prediction and bid optimization features.
Backtesting allows us to automatically evaluate a wide array of approaches and select only the most accurate models based on their ability to predict known historical outcomes.
Automated controlled experimentation enables live testing of incremental value predictions in the real world so that models can be validated and improved by rejecting poor models and feeding results back into the next model.
iOS14 will not handicap any of the existing capabilities of UACC. All of our UACC platform capabilities remain intact including analytics (dimensions, metrics, etc.), LTV prediction, and bid optimization. The only difference is that we will be providing a new measurement module (based on our analysis outlined above) that ties aggregated cohort activity with the channel, campaign, country, and publisher app dimensions, instead of the MMP.
Our measurement module will be provided as an option at the app level so that advertisers can rely solely on MMP measurement for their Android apps, should they wish.
We will continue to share details of our progress and invite you to connect with us to ensure that your concerns are addressed, and that you have a chance to shape our solution.