Nov 10, 2021

The Disappearing User Part 3: Impacts to User Management

Brock Hardman
Natasha Solanki
Hanna Chun

Brock Hardman, Natasha Solanki, and Hanna Chun

The Disappearing User Part 3: Impacts to User Management

As privacy regulations become stricter, user data begins to disappear. Apple giving iPhone users the ability to block their Identifier for Advertisers (IDFA) from organizations has impacted how companies will conduct their marketing strategies. In the first two blog posts of this series, we discuss alternate marketing solutions to these privacy restrictions and how companies can optimize future mobile campaigns

Companies that currently rely on mobile measurement partners (MMPs) to track and organize their user data will need to make a drastic shift in how they collect and manage that data moving forward. MMPs leverage the IDFA to provide valuable attribution data across various channels. For example, some commonly seen channels in advertising are paid search results, walled garden, contextual advertising, content marketing, and mobile. 

Without it, organizations are forced to turn to Apple’s SKAdNetwork which will only provide campaign-level tracking. This will negatively affect the individual customer’s experience. Users will be exposed to less personalized ads and potentially more spam. To bridge this data gap, we recommend companies begin prioritizing customer engagement and collecting first-party data about their behavior. 

In this blog post we will explore three specific types of ways user management will be impacted by these privacy changes and what measures companies can take to alleviate disruption to their mobile marketing strategies. 

User Management Impact #1: Financial

Media coverage over Apple’s decision often circulates around Google and Facebook’s advertising agencies, potentially losing a combination of over $25 billion, but they fail to highlight what this means for small to medium businesses (SMBs). 

Challenges for Small to Medium Businesses

SMBs do not have the same capital to spend on advertising that large organizations do. By removing IDFA and moving away from personalized ads, SMBs will face adversity in using their limited capital to reach their target audiences. It is estimated they could see a cut in over 60% of website sales from ads, making it difficult for SMBs to compete with larger corporations who can spend more. 

Furthermore, these changes will go on to affect content creators. Facebook’s testing uncovered more than a 50% drop in revenue for publishers and creators when personalization was removed from mobile app ad install campaigns. 

Personalization drives revenue growth so it is vital that SMBs take action to create alternative marketing strategies. Companies with larger advertising budgets can afford to spend more money on creating various campaigns, ad sets, and audiences to test how their strategies will perform. They also get to think about personalization later. 

SMBs on the other hand, need to think about personalization first. To efficiently allocate money on campaigns that have a positive return on investment, SMBs need to understand their user base. In the past, SMBs could rely on their mobile measurement partners to give accurate attribution data but that is no longer the case. 

Opportunity to Focus on a Probabilistic Attribution Model

So how can companies limit this fiscal impact and calculate a return on ad spend (ROAS) when deterministic attribution is no longer reliable? Before answering this question, it is important to note that IDFA is not going away completely. There is still an opportunity that users could opt-in to share their IDFA through the app tracking transparency (ATT) prompt. How companies determine where and when to show that pop up in the user journey moving forward will be crucial to optimizing their opt-in rate (More on this later in the article).

Under the assumption that most users will refuse ad tracking requests through the ATT popup, ROAS calculations will need to move toward a probabilistic attribution model. According to AlgoLift, a mobile app user acquisition automation platform, there are four data inputs that go into calculating a probabilistic ROAS:  

  • User-level loan to value (LTV) projections: Through anonymous user-level data such as geographies, device make/model, app versions, in-app events, and revenue, organizations can calculate a projected LTV ratio at the user level.

  • Deterministic MMP attribution: Deterministic attribution data collected through iOS users who have opted into the ATT framework. 

  • Ad network campaign data: Data such as demographic statistics, campaign ID, geography, and device model/make. 

  • SKAdNetwork data: Companies can leverage the campaign ID, conversionValue, and the revenue data this framework provides to feed into their probabilistic attribution model.

Probabilistic Attribution Model

Probabilistic Attribution Model
Probabilistic Attribution Model

By leveraging the SKAdNetwork framework’s data, deterministic MMP attribution (from ATT opt-in users), and user-level LTV projections, companies can generate a probabilistic ROAS to optimize their budget for each ad network.

User Management Impact #2: Segmenting Users 

User identification and segmentation in the post-IDFA world will require a focus on first-party data. While IDFA value will require user opt-in, the ID for vendors (IDFV) can be used to build first-party data sets based on actions user perform within the context of the app owner's ecosystem. 

Investing in the IDFV

The IDFV is a unique value per device which remains consistent across all applications by a given publisher. This allows for user tracking across applications by the same vendor but does not provide the ability to correlate user accounts on multiple devices. It can also be reset by users if applications are deleted or reinstalled, which impacts not only the ability to analyze performance across channels but also the ability to personalize and correlate behavior over time.

Leveraging the PPID

For applications that require a user login, an alternative approach is to associate these first party datasets with a publisher-provided identifier (PPID). Publishers can use a PPID to tie user behavior to a customer profile and tie this behavior to customer segments. This approach also protects against future restrictions of IDFV by Apple, providing flexibility and a future-proof solution to audience targeting.

While companies can turn to alternative sources for user data including contextual data such as device information and relative location, a focus on first-party data collection strategies is necessary to maintain accurate and robust segmentation while protecting from future Apple policy changes.

Finding the Right Technology

One example of collecting first-party data is by integrating a customer data platform (CDP).  This will serve as the single source of truth for your customers and will allow you to share attributes across all channels. Once you begin to collect and/or centralize all your user data, it will be much easier to provide relevant and personalized information to your customers.  This will result in higher satisfaction and increased revenue.

User Management Impact #3: User Journey 

The staggering drop in IDFA availability will result in impersonalized ads for a majority of the user base. Without integrating personalized marketing into the user journey, companies could experience sentiments of user frustration due to what seems like spam advertising. To avoid using a shotgun approach to advertising efforts, marketers should begin working closely with their user experience (UX) and user interface (UI) teams to either collect as much first-party data as possible or compel users to opt-in to the ATT framework

Using UX & UI Tactics to Enhance the Journey

As mentioned in the first part of this series, collecting first-party data in a single customer view is inexpensive, accurate, and relevant for building personalization. How companies approach users to input that data is arguably even more important. Companies must put UX at the heart of their business decisions because mobile users expect a painless and convenient experience. Rethinking the UX to revolve around how to attract new users and how to engage existing users will limit the risk of users exiting a flow before completion, resulting in an increase of collected user data. 

Organizations might consider including the following methods into their user journey:

  • Rapid registration: Provide a simple and fast sign-up method for users and limit the number of input fields they need to fill out before using your application.

  • Reduce friction: Use technology to reduce mundane tasks by leveraging biometrics and optimizing text fields to use proper keyboards.

  • Timely and educational permission prompts: Avoid spamming the user with permission pop-ups as soon as they open an application. Users won’t understand why the company requires that data and will lean toward declining the request. Instead, time the prompts for when they make the most sense within the user journey. For example, an application should wait to ask the user for camera access until the user wants to access that specific feature within the app. It’s important to be transparent with the user on why certain permissions are needed and what benefits they will receive along with them. In the case of the ATT pop-up, we recommend presenting an educational screen prior to the pop-up request, because the pop-up uses language that might persuade the user to decline IDFA tracking. This screen should be designed to incline the user to accept the IDFA tracking request by emphasizing how this can help personalize their experience and the benefits that come from that.  

  • Incentives for participation: Offer your users an inexpensive gift or reward (free to the customer) by simply filling out a short form or survey. Most people love free stuff, and they will give you good information for it. 

  • Invest in attribution technologies: There are several technologies available, such as Appsflyer or, which can help bridge the data gap. Branch offers a predictive modeling algorithm that uses historical data to assign attribution data more accurately to a user.  There are also CDP options available such as Tealium or mParticle, which can accelerate your efforts in developing an overall data solution. 

A combination of these UX/UI tactics can persuade users to willingly give organizations the data they desire. To learn more about UX/UI best practices, look at the recommendations we discuss in our Mobile Strategy Series

Centering Your Strategy on UX/UI

User experience should be central to any of your marketing strategies. There is a major opportunity for brands to attract new customers by simply providing a more engaging UX than their competitors. And while this has been the case even before the IDFA changes, there has never been a better time to capitalize on an even playing field. 

Create a Strategy to Navigate Upcoming Changes

Without IDFA as a reliable tool for gathering user data, organizations will see an impact to user experience, segmentation, and ultimately their bottom line. Developing a strategy for maintaining a positive customer experience—while accomplishing marketing segmentation needs—is vital to navigating these changes.

Want to learn more about what Credera technology consultants are doing to tackle this challenge? Reach out to us at

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