With the goal of educating ourselves and industry stakeholders on the implications of iOS14 and the privacy announcements, we started a living FAQ. This will be updated as more questions are asked, and more answers are revealed. If you’d like a question answered please email us at firstname.lastname@example.org
1. What does iOS14 mean for measurement of user acquisition campaigns?
At a high level, Apple made several privacy-centric announcements with their iOS14 with respect to the measurement of user acquisition:
Over the past few weeks, we have presented and built a probabilistic attribution model that will allow us to continue optimizing marketing spend for maximum long-term return on ad spend (ROAS) once iOS14 effectively deprecates the IDFA. Today, we are excited to share some preliminary validation results.
We use three data sets to probabilistically attribute installs:
As we continue to develop our probabilistic attribution solution for iOS14, we want to share our thoughts on the optimal use of SKAdnetwork’s ConversionValue. It’s an important topic, as the performance marketing ecosystem seems concerned about the limited scope of modeling post-install performance using this value. In this post, we’ll talk more about what ConversionValue is, what it allows for, and what we believe the optimal usage is.
What is ConversionValue?
With the iOS14 privacy update, Apple announced an API called SKAdnetwork, an attribution solution provided for when users don’t explicitly share their Identifier For Advertisers (IDFA). SKAdnetwork removed the…
Over the past couple of weeks, we’ve shared a proposed solution for probabilistic attribution on iOS14. It may surprise some observers of the mobile marketing ecosystem to learn that we’re building an attribution model. In this post we wanted to discuss why we’re building this solution, and the benefits it will bring to our clients.
Deterministic attribution, or the task of matching an install to a marketing campaign, is a data engineering problem which has historically been solved by mobile measurement partners (MMPs) in partnership with ad networks. The Identifier for Advertisers (IDFA) provided a persistent ID that enabled MMPs…
It has only been a few weeks since Apple’s iOS14 announcement, but the performance marketing ecosystem is already adapting to its new privacy features. Deterministic campaign attribution, a cornerstone technology in running and evaluating performance campaigns, will be replaced with Apple’s SKAdnetwork. Granular, persistent, cross-device performance analysis will be made more challenging by a series of limitations designed to obfuscate user identity.
AlgoLift is developing what we believe is a robust and mathematically sound approach to accurately attribute predicted revenue across campaigns using probabilistic attribution. Underpinning this approach are 5 key hypotheses:
We previously discussed a promising solution to the reduced efficacy of deterministic attribution inherent in iOS14. In that piece, we outlined our conclusion that deterministic attribution will be significantly impacted by the changes put in place by Apple to protect user privacy.
In this follow-up, we would like to elaborate on our approach and the use of attribution data.
Attribution for Campaign Valuation
AlgoLift’s primary use-case for user-level campaign attribution is campaign pROAS (predicted ROAS) estimation. We perform user-level LTV forecasts which we then aggregate to campaign level predicted revenue. We then compare the predicted revenue to the observed spend…
Apple stunned the mobile marketing industry with privacy updates announced as part of iOS14. At AlgoLift we anticipated and welcomed these changes, having previously made the decision to never utilize IDFA (or GAID) in our data. As a result of the news, companies at every corner of the mobile ecosystem have been mulling over the implications of the new privacy protections.
The announcement raises some challenges for attribution, LTV prediction and campaign automation:
We’re delighted to announce the launch of AlgoLift Intelligent Budget, a cross-channel, media mix model budgeting tool available to AlgoLift Intelligence and Algolift Intelligent Automation clients today.
As app-based companies scale their marketing budgets across multiple mobile user acquisition channels, they look for ways to effectively understand how and where to invest their marketing spend to hit their long-term return on ad spend (ROAS) business goals.
Now with AlgoLift Intelligent Budget, teams from finance, analytics and marketing are delivered an optimal monthly budget based on hitting their long-term ROAS target. This budget is updated on a daily cadence based on…
Previously, in An Algorithmic Approach to User Acquisition Automation, we explored the mathematical foundation of programmatic campaign management on Facebook and Google. In some cases, automatic campaign management is not possible for lack of a management API or granular performance reporting. In this case, the same mathematics and approach can be used to steer channel-level budgets based on higher granularity ROAS (i.e., network or geo) and with manual management. This case study describes results from 6 months of channel-level budget allocation encompassing tens of millions of dollars of user acquisition spend. …
We’ve spent the last 4 years building tools to understand our clients’ total user acquisition ROAS including algorithms to support IAP, ad revenue, and subscription business models. Today, we’re delighted to announce the launch of AlgoLift Organic Lift, available as an additional service to AlgoLift Intelligence and AlgoLift Intelligent Automation clients. This product estimates the impact of paid advertising on organic revenue and is the final piece in understanding paid UA ROAS.
With AlgoLift Organic Lift, companies can now understand the impact any paid UA channel at scale can have on organic installs at the channel and platform cohort. Organic…