Previously, there were a number of ways to assess ad campaign performance for iOS apps. The IDFA, a unique device identifier for advertisers, helped track user–ad interactions and post-install in-app activities.
With the introduction of ATT in iOS 14.5, over 69% of iPhone and iPad owners started denying access to their IDFAs. The restrictions on IDFA transfer have limited the mobile analytics options, with ad campaign efficiency now having to rely on innovative solutions for confidential and accurate attribution.
One of them is LTV forecasting.
In this article, we will explain LTV’s significance for your business, discuss SKAdNetwork’s impact on the mobile ad market, and talk about various LTV forecasting solutions for the new reality.
Lifetime Value (LTV) is the revenue generated by a user up until the moment they stop using the app.
For example, if a user viewed $USD 3 worth of ads in the first month, made a $USD 4 purchase in the second month, and rarely launched the app in the third month, making no purchases and finally uninstalling the app altogether, that user’s LTV would be equal to $USD 7.
Users installing apps by responding to ads have a Customer Acquisition Cost (CAC). If you spend $USD 1,000 on an advertising campaign that brings in 100 users, then the CAC is $USD 10.
Your campaign would be ineffective and loss-making if the users’ average LTV is lower than the average CAC.
LTV forecasting is important for an app’s long-term development. It helps predict when your marketing spend will pay off – in a week, a month, or a couple of years.
LTV forecasting allows app owners to confidently grow their business and avoid using ad channels that would result in a poor LTV : CAC ratio.
LTV forecasts can also help you estimate your ad campaign ROI when traditional mobile analytics tools are unavailable.
We'll go back a bit to explain how Apple’s new confidentiality policy has affected the mobile ad market.
Prior to April 26, 2021, attribution for iOS devices was based on the IDFA, an identifier generated by the device for the benefit of all stakeholders:
Our research shows that even before the upgrade to iOS 14.5, about 20% of users limited IDFA access on their devices, but it did not create major problems for the entire mobile ad, analytics, and attribution industry in terms of data collection and processing.
On April 26, 2021, Apple released the iOS 14.5 update, putting into effect a new privacy policy, ATT, and its own attribution framework – SKAdNetwork.
Now, when you launch an app for the first time on iPhone/iPad, the system automatically requests user consent for data transfer. According to MyTracker data, 69.1% of global iOS users deny access to their IDFAs.
Ad networks are no longer able to track impressions and user–ad interactions with a breakdown by device, which has reduced the accuracy of user attribution by analytics platforms. The attribution process as we used to know it is no more, although its individual components still exist.
Apple has come up with a solution in the form of SKAdNetwork, which helps measure the number of conversions and LTV while not disclosing the link between the ad and the user it helped to acquire. As a result, it is now much more difficult for developers and marketing experts to adequately assess an ad campaign’s ROI.
This affects the entire industry, as advertisers cut their advertising budgets for iOS, causing changes in monetization schemes for apps and games in the App Store. Hence, ad monetization for apps becomes more aggressive, and these ads might not even correspond to a user’s interests.
You can find more answers about attribution after iOS 14.5 in our FAQ or in our blog post on the 10 most popular questions about the iOS 14.5 update.
Today, analytics platforms are no longer able to attribute individual users to a specific source or campaign, but they can still predict a user LTV for various periods.
LTV forecasting can help you gauge the impact of updates on an app, evaluate the efficiency of monetization models and optimize ads, even without individual user data.
Currently, there are two types of LTV forecasting: using standard SKAdNetwork mechanisms to send Conversion Values or through predictive analytics.
Conversion Value is a number between 0 and 63 sent by SKAdNetwork to app owners. Based on this data, you can create a table matching LTVs to CVs.
Each number from 0 to 63 can be viewed as corresponding to a range:
etc.
Based on LTV, retention, and a couple of simple formulas, you can forecast an approximate ad campaign revenue.
However, this solution has its drawbacks:
A predictive model is a set of algorithms based on machine learning and designed to recreate attributions using existing data:
Using machine learning models is the quickest and easiest way to evaluate the performance of ad campaigns without having to ask for developers' help.
Predictive models don’t identify users. They seek to recreate the attribution picture to predict LTV and help developers and marketing teams evaluate an ad campaign’s ROI.
LTV forecasts work at a partner level, which is sufficient to evaluate ad campaigns soon after launch without having to waste your budget. The prediction horizons can be for 30, 60, 90 to 180 days.
Check out our blog to learn more about how to set up a SKAdNetwork-based LTV forecast in MyTracker.
All our solutions to keep attribution confidential and accurate under the ATT framework are available to you as part of MyTracker’s optimum (free) plan.
