For anyone living through a major change, there’s usually a few stages of grief:
- Denial — refusing to believe that change is happening, denying any significant adverse that may affect us / our work.
- Bargaining — acknowledging change but discounting its potential effect. “It’ll be fine, don’t worry”. Here, people might be saying “For the time being, we can rely more on Android until Facebook and the gang figure it out”.
- Acceptance — knowing change has to be dealt with, and attacking it head-on.
Where am I going with it?
There are two tectonic shifts happening right now in mobile marketing, and mobile marketers are currently at different stages of their grief. If you’re already at Acceptance – great. If not, some transformation on your part is needed (“You can lead a horse to water, but you can’t make it drink”, if you know what I mean.)
From dealing with both real-world and work-related grief, I learned that I normally need someone to slap me (figuratively!) to wake me up to what’s really going on.
As your figurative slap, Storemaven has defined for you the two shifts. We believe that if you know and understand them, you’ll reach Acceptance quickly and excel in your work.
Let’s unpack those shifts.
Check our iOS 15 Content Hub, with all the articles, guides, and webinars that will help you to better prepare for Apple’s new capabilities.
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Shift #1: Privacy-first Mobile Growth & UA
Since late 2020, the world of mobile marketing has been undergoing a shift, triggered and led by Apple, who decided to wage war on anything that works against user privacy.
There’s been a ton of implications. We relied on user-level data to track and target users, with hyper-personal ads based on anything they did — from credit card purchases, to what apps or games they liked, and what they did on the internet in general (websites visited and their interactions on them).
The above was an extremely efficient machine. From it, apps and games got a great deal: Give money to ad networks that hold that information, portray to them what the ideal user is (usually users that purchased something in their apps and games) and get back from the ad network a large audience of people with a high degree of probability that they’ll also pay (because they share behavioral and demographic characteristics).
** Important note: remember that demographical data was always weaker than behavioral data, see below.
Then Apple took away from the ad networks the ability to access, read, store, process, manipulate, and use behavioral data in their “targeting machines”. App and game developers were no longer able to share that user-level data with them (or anyone else) in the first place.
This of course broke a significant part of what ‘deterministic attribution’ is: associating between a new app/game user, and the ads they viewed/tapped on before they installed.
Of course, Apple continued to release their own attribution solution ‘SKAdNetwork’. It has loads of limitations that don’t really allow for associating users with the ads they viewed/tapped on, but it does still give mobile marketers and UA folks some ability to understand their campaign performances; it reports installs at a campaign level back to them, and some minimal downstream data on what these users did in the app in the first 24-48 hours (most of the time).
Apple’s latest SKAdNetwork 3.0 allows app and game developers to get the data back from SKAN directly, which eliminates reliance on 3rd party tools to aggregate their “new” attribution data coming in from Apple.
NEWS FLASH – this shift is not going to stop.
The truth is.. any mobile marketing, growth, and UA team that wants to continue relying on user-level data for ad-targeting and measurement purposes through things such as fingerprinting (without Apple catching up with them and banning them from the App Store forever), will see wrong ROAS figures, and will make bad decisions until they won’t be able to rely on that data anymore.
The new paradigm is: you’ll have aggregated, anonymous data at your disposal to make your UA decisions. You’ll be able to see aggregated metric data (ad-set, and creative-level data about impressions, click-through rates, installs, LTV, retention, and ROAS) without tying most of it to specific ad creatives and campaigns.
But, there is good news! You still have options.
So, what should you do about it?
There are only two possible courses of actions (excluding of course the reliance on fingerprinting, which we believe won’t be feasible and will gradually die out anyway).
Course of Action #1: pROAS
Start building your own data-science models.
Take into account all the data you do have
– top-of-funnel from ad networks (impressions, CTRs, etc)
– that from SKAdNetwork (Apple verified installs and conversionValues)
– App Store data on where Referral traffic is coming from (at a referring app level)
– as well as its performance on the App Store (product page views, installs, conversion rates, re-installs, etc).
This model should eventually spit out a probability for each new user opening the app for the first time, as to which ad creative and campaign they came from.
Once you label each new user with its ad creative and campaign source, you can continue and make decisions based on your ROAS measurement. You don’t have a problem creating this because once you probabilistically associate a user with an ad source, you can continue tracking them downstream and measure anything they do in the app (you just can’t share that info with anyone else).
There are some vendors trying to accomplish this and offer it as a third-party service. Some app and game developers, with their data science teams, are trying to develop it on their own.
Probable ROAS is a weaker course of action in our view. The tremendous investment needed to create such a model and its specificity (seeing as two different apps or games each has its own unique UA and monetization characteristics), means it will almost never work. Only a handful of companies in the world would be able to do that successfully.
The measure of success here is: how valuable are those new probable ROAS figures in making UA budget allocation decisions? At the end of the day, do they lead to more (profitable) revenues?
This can easily be measured at an app-level (looking at its P&L report over time). If the data isn’t accurate enough to make these UA budget decisions, this course of action won’t lead to much value for a mobile-app-driven business.
Course of Action #2: Media mix modeling
Adopt a new paradigm which is called media mix modeling (MMM).
This approach might sound complex, but it’s quite simple. Even if the image below seems frightening.
View and analyze your aggregated mobile marketing data over time, identifying how changes in the marketing input are impacting the output.
So, how does UA spend across channels and campaigns, search ranking improvements, search ads spend, featuring, product page creative changes, etc affect volume of installs, revenues, retention, registrations or other downstream metrics, etc.
This is only possible by creating one source of truth related to growth that contains all of your ad networks data, attribution data, App Store or Google Play data, downstream data, search performance data, Search Ads data, and everything else you might have access to.
Moreover, this ‘growth database’ has to unify all the changes you make to your marketing input so you can analyze the impact of each on the output metric over time (such as changes in UA spend, changes in creatives, keyword targeting, featuring dates, etc).
This would allow you to get a pretty good picture of the impact had on your main KPIs from the different marketing activities you engage in.
Let’s move onto shift #2.
Shift #2: App Store Centric Mobile Growth & UA
The second shift is just as important as the first. The privacy moves that Apple is taking are a part of a much bigger picture.
A long time ago, Apple decided that it wanted its App Store to be the main way users discovered apps and games, and wanted app and game companies to treat it as a user acquisition engine on its own.
Taking a step back from the privacy-related updates with iOS 14, it’s not unlikely that Apple will use its newly formed position to become the only company in the world that has access to user-level data, and also sell ad space to app and game marketers.
This will take the form of “Apple Ad Network”.
We can see the first steps of this if we look at Search Ads and how it’s evolving to include inventory from other Apple apps (News and Stocks). On the face of it, Apple is positioning itself perfectly to release an Ad Network service line, and offer to show Apple Ads inside their products to all apps and games. You can envision the marketing material Apple will put out: “The only privacy-first ad network in the world”.
More hints come from the launch of Apple’s own attribution solution (SKAdNetwork), and their very developed attribution solution for Search Ads (the new AdServices framework that’s replacing iAds and provides deep & granular Search Ads attribution).
It will also be an ad network with the least amount of fraud, or close to zero fraud, on the planet.
Besides Apple’s efforts into the business of opening an ad network (with iOS 15 and the introduction of CPPs, IAEs, and PPO, plus new and more granular sales/retention/average proceedings per-user data in App Store Connect), they’re also telling the world they want the App Store to become the acquisition and discovery engine of the App Economy (not Facebook, Google, or the other self-attributing networks).
This shift will mean that any mobile marketers managing mobile growth without looking at App Store data, will fall behind.
Apple is basically making the App Store:
- The main place where users are discovering apps through editorial content, search and in-app events (for the more than 500M, or half of the world’s population of iPhone users, that visit the store each week).
- An inseparable part of any paid UA funnel with CPPs, and almost the only party in the world that shared marketing on the performance of these funnels (in an aggregated way).
- A paid user acquisition channel in itself, with growing inventories of Search Ads.
- A crucial junction to improve growth, with additional tools to perfect and test product pages such as Product Page Optimization.
- An inseparable part of your mobile marketing database by being the only party in the world with valid and accurate data.
This can be summed up by saying: Apple is pushing you to build contextual funnels in your Browse, Search and Referral (UA) channels. Each funnel will have its own CPPs with the right messaging, as well as monitor and measure the performance of these funnels with App Store data.
What should you do about it?
Lean into this change. Understand where Apple is going, and invest early on to ensure you’re well-positioned to maximize growth when the App Store becomes a more significant place to find it.
- leveraging the new iOS 15 capabilities (in-app events. Custom product pages, and Product Page Optimization)
- building unique and highly-performing funnels with the App Store as a critical step
- and making sure App Store data is a key part in how you make marketing, growth and UA decisions.
You don’t really have a choice
- You can’t rely on fingerprinting
There may be some companies still in the denial phase, believing that fingerprinting is a valid way to continue to target users with ads, and measure their performance.
Trust me – this is one of the riskiest moves for a mobile company or a marketing team. Apple has made it very clear that fingerprinting is a no-no. The cost of engaging in this activity is insane, as it could result in a mobile business disappearing (Apple has shown it has zero issue banning apps from the App Store for years).
Moreover, once these companies wake up to the realization the game has changed, and need to transition to the aggregated data + App Store centric phase of mobile growth, they’ll find it very hard to reach their competitors who would’ve enjoyed a big head start.
- You can’t operate in the “old way” with tools that were designed for the “new way”
There are also companies trying to leverage the new SKAdNetwork tools and conversionValues, to “hack” their way into operating the same way they did before.
As we mentioned with probable ROAS, unless you’re one of those teams who can afford the investment it costs to build your specific model, there’s a very low likelihood you’ll be able to use SKAdNetwork to make UA decisions in the “old way”. You won’t be able to look at campaign and ad performance data down to the ROAS level, and allocate budgets.
The data coming from SKAdNetwork will never allow you to, because it was designed with an aggregated data mindset, with Apple pushing against you continuing in the old way. Do you really want to take the opposite stance to the company that’s responsible for 80% of your revenues?
Storemaven’s new platform to get you there
- A solution for shift #1: Data feed
So for the first shift, your optimal vision is to create a unified Growth Database that contains all of your marketing data, from all possible sources, including App Store data.
If your goal is improving your KPIs, this growth database isn’t that useful if you haven’t got the expertise to create the right “views”, and analyze it in the right way to surface valuable insights.
We are more than excited that we’ve created The World’s First Growth Database.
An easy-to-use, integration-less Growth database that removes the need for any complex data engineering and integration work, and creates your one source of truth for anything mobile marketing, growth, UA, and ASO.
Once you have in one place all the data you need, you can use it to feed any internal BI platform you have, or any third-party data visualization and exploration platforms, such as Looker or Mode Analytics. You could even take that data and quickly analyze it in Excel, Google Sheets or Google Data Studio.
- A solution for shift #2: App Store funnel analytics and full-funnel optimization to maximize growth
As we accumulated more years than I want to count in the world of ASO and mobile marketing (cos how old does that make me?), we invested tremendous resources and worked with some of the world’s top mobile growth experts, analysts, managers, and leaders to create The World’s First Mobile Funnel Analytics Tool – the optimal solution for:
- Identifying all of your mobile install funnels — be it organic search, paid search, specific contextual groups of keywords, contextual groups of app referrers from your UA campaigns (i.e. a match-3 funnel). This takes hundreds of hours of non-scalable work and allows you to access the same insights with a few clicks of a button.
- Monitoring and analyzing your funnel performance 24×7. It’s like having the world’s best mobile growth analyst working alongside you to keep your efforts directed at the mobile install funnels that will yield the best impact for the KPIs you’re measured on.
Additionally, to align yourself with the world of mobile marketing that’s becoming more App Store centric, you would need to plan, design, operate, automate, and analyze a significant number of custom product pages to maximize growth. Plus, implement a full-funnel optimization approach for your mobile growth moving forward.
So I’m even happier to say we’ve created The world’s first App Store Product Page Platform.
This is a platform that embeds the seven years’ experience that the Storemaven team has accumulated, and allows you to:
- Plan your product pages. After you’ve identified the funnels you want to invest in, you’ll seamlessly be able to create design briefs. All based on data-driven hypotheses and everything we learned from sampling more than half a billion users on App Store product pages to date.
- Design product pages. You’ll have access to our team (the only team in the world that designs product pages for the App Store based on data, not a hunch) who will design your product pages in a way that’ll feel like an extension of your team.
- Deploy and manage product pages. Because managing hundreds of product pages won’t be easy for any team, we’re enabling you to manage all pages, deploy changes in an automated way, and even schedule product pages smartly.
- Test your product pages. No matter how you test your product pages, whether Google Experiments, Product Page Optimization (native a/b testing for your organic traffic) or an in-depth install experience test on Storemaven’s replicated environment, we will allow you to run the most accurate tests. By leveraging our unique testing algorithm that’s trained on tens of thousands of tests to date, you can make the right decision every time you deploy a winner to the App Store, and have a repeatable process to improve conversion rates for any funnel, organic or paid.
- Analyze the performance of each product page. With the power of the Growth Database we mentioned, you’ll be able to easily analyze and report on the performance of each funnel and product page, and how well they’re able to convey the specific audience that arrives to them. Plus, with “smart alerts”, it will feel like you have an ever-present assistant (who doesn’t ask to join you at Burning Man) that’s always making sure you detect any performance issue with your product pages. Smart alerts act in real-time (hopefully not when you’re at Burning Man) instead of catching conversion rate drops months after they occurred.
A final conclusion
So that’s it. In only 3,000 words, you’ve been guided through the data challenges headed your way, the actions you can take for a smoother transition, and you’ve been introduced to Storemaven’s new products (and vision) that will make your job easier.
We believe that with a bit of research, a clear strategy, and support from your teams, you’ll be able to handle everything that’s coming and more.
To learn more about Storemaven’s solutions, and how we can help with your 2022 mobile marketing strategy, feel free to contact us or book a demo.