The Ultimate Guide on User Acquisition Post-iOS 14.5

Introduction

iOS 14.5 has enormous implications for privacy around advertising and has even the most successful companies worried about their business model and technology strategies. Changes to Apple’s IDFA present new growth marketing challenges and will require quick adjustments and experimentations.

What is changing

For the marketer, this makes automated targeting that we have all come to rely on much harder to use, and less effective because user-level marketing data availability will decrease tremendously. Since user tracking has changed from opt-out to opt-in, early reports show that only 4%-10% of iOS 14.5 users are opting-in. This means that most users might soon not be tracked as before.

• User-level marketing data will not be available for the majority of your iOS campaigns.
• Retargeting gets a lot more competitive due to higher competition for the smaller potential audience, and therefore will be more expensive and yield a lower ROAS.
• Lookalikes are a new challenge as advertisers will have to get sharp on signal selection to build lookalike campaigns, maintaining a quantity—quality balance.
• Accurate attribution will become a big challenge due to attribution cut to 7 days. By day three, you need to be able to predict what your 90-day revenue is going to be by using predictive LTV.
• There will be fewer signals to rely on due to the event limit and event prioritization system.

What should marketers do?

There are a lot of different areas where marketing teams need to act now, starting from experimenting with different messaging on the ATT request, going through rethinking and redesigning the conversion flow, and finishing with signals and campaign structure optimization.

• Reevaluate your current conversion flow and adjust. As conversion windows got much shorter (1-7 days), marketers won’t be able to optimize for conversions that happen later on the funnel. This means that some companies will have to revisit and redesign their conversion funnel. If your product offers two weeks free trial, you should consider shortening the free trial time to 6 days or rewarding users who purchase before the end of the free trial.
• Learn more about your users quickly. As the data available for the marketer on 3rd party platforms decreases, the importance of obtaining rich, meaningful 1st party data increases. Data such as personal details and preferences related to your product/product usage are crucial and will support marketers in future analysis, modeling, and campaign optimization. If your product flow doesn’t aim to obtain those data points today, you should consider adding an onboarding flow or a questionnaire.
• Start consolidating campaigns and test, test, test… For advertisers, there is a new need for consolidation via fewer campaigns with broader targeting. There will be campaign restrictions and data limits. Create your campaigns now and test them across key metrics as you can still test your theories without reporting to the SKAdNetwork. The key to tackling this challenge is starting early - if your campaign is successful, chances are it will succeed once the changes are live.
• A/B Test between campaigns with and without SKAdNetwork reporting. Once you examine and experiment with different consolidations, the next step should be to understand the expected performance with SKAdNetwork reporting limitations. While it’s pretty clear that SKAdNetwork reporting will result in different attributed results compared to the current reporting, what is not clear is how. You can test this yourself — by comparing a SKAdNetwork campaign with a none SKAdNetwork campaign. First, create two identical campaigns with the same setup, optimization, targeting, and creatives. The only difference should be the SKAdNetwork effect.
• Shift from user actions to user value to drive optimization. With fewer users opting in, sending the right signal for the marketing platform to optimize becomes a break or make for your campaign’s success. The challenge is to balance quantity vs. quality. As the changes affect the mid-lower funnel, which is precisely where we usually find the events that drive so many of the conversions where a long funnel exists. Finding a way to extract value from the events happening earlier in the engagement cycle is the challenge to tackle. If previously, you could follow the user from early engagement signs (such as registration or free trial) to conversion, noting what event caused them to convert. With iOS 14.5, you no longer have that option. Following iOS 14.5, you should think of how to create hierarchical events that correspond to a score. For example, an amplification event where the user has a free trial but uses the app daily would be given a high score.
• Use predictive models to drive optimization. Leveraging AI for prediction-based signals and predictive modeling will take your optimization capabilities to the next level. When using proxies, the signal’s strength and accuracy are limited because they usually fail to represent the user’s actual value. Throwing historical data into the mix means you’re not even sure to get a representation of changes in the product or user’s behavior. Using predictive signals allows marketers, using the same single signal, to truly embody the user’s value based on the complete set of actions and behaviors. This will enable marketers to understand better which users are predicted to be high-value and the real campaign performance. Predictive signals will put the concept of user scoring into focus, which is an evolution from user action-based decisions.
• Build models that help send the right signals to the ad network. If you’re not using server-side conversions at the moment, you should definitely begin to develop technological capabilities and marketing know-how to support sending signals to indicate the user’s value or score. Server-side signal capabilities will become key to allow brands to send those more complex signals to the platform rather than a single event. FB, Google, and Snapchat already support Server Side conversions, Ironsource has this in beta, and we expect that other networks will develop this as well.

‍Contributors:

Ido Wiesenberg - Co-Founder & CEO of Voyantis.

Over 15 years of expertise in building growth and marketing teams. Before Voyantis, I co-founded Tvinci that was acquired by Kaltura. Selected by Forbes as one of the most promising entrepreneurs in Israel.

‍Eran Friendinger - Co-Founder & CTO of Voyantis. Father, husband, engineer

20 years of dabbling in Big Data, AI, and marketing. Previously co-founded Adience, a deep-learning-based Mobile Analytics solution, acquired by Market.com.

‍Ori Klein - Digital Marketing and Product @ Lennar

Growth executive with over 15 years of experience. Lead the Growth team on Via and Eight.

‍Mike Prasad - Founder @ Marketing Club. He has diverse experience as a technology entrepreneur, investor, and strategist with expertise in marketing, branding, UI/UX, product development, cross-market finance, and platform creation