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KEY HIGHLIGHTS

👁️ The very real dangers of easily-accessible IDFAs

😇 Apple has an agenda with the iOS 14, and it isn’t pure altruism

📈 How making the IDFA useless pushes Apple’s revenue up

🧠 The ideal demographic for advertisers to target is the same that knew to turn off IDFAs

📍 Limit ad tracking actually meant more invasive tracking

📐 Measurement was always tricky

🌗 Why attribution only presents a partial picture

📊 Why relying on LTV was not great to begin with

💰 How IDFAs are going to fundamentally change app UA economics

💵 Predicting ad revenue will be uncertain for the moment

🤖 Preparing for changes in the programmatic landscape

🔦 How post-IDFA strategies will have a renewed focus on retention and reengagement

🗓️ Understanding the opportunities of the conversion value framework in SKAdNetwork

💭 4 considerations to customise your conversation value framework

🧩 How MMPs will evolve to still be a critical component of the measurement infrastructure

🏁 Why causal inference can address some of the issues with SKAdNetwork

🌫️ How IDFAs are going to change the game for publishers.

🌊 How to use web flows for UA and what to expect

🎨 Optimizing for creative hits is vital

🦾 Using automation as a part of your UA playbook

👍 Do whatever you need to do to maximise consent

🧰 Tools to have in your marketing arsenal: pre-permissioning and win-backs

📲 Why in-app bidding may carry the day

💲 Why uncertain times are not the right time to look for capital

💥 Post-IDFA is a time to focus inward and reassess the impact at a business level

FULL TRANSCRIPT BELOW

Shamanth: Here we go. Why is the IDFA a big deal for consumers and for Apple, not just for marketers? In the Mobile Growth Summit Deep Dives IDFA, John Koetsier explains:

John: So in 2015, the FBI actually went on to a plane on the tarmac, and arrested a Chinese national. He was about to take off for China, and he had a thumb drive in his possession which was full of IDFAs. He’d previously worked for Machine Zone and quit days earlier. So there’s lots of possibilities here. And of course, this was before the whole China-US massive blow up: Cold War, economic war, all the things that are going on right now.

But there’s a lot of things that can be going on here. One, there’s a commercial motive. If you have all these IDFAs, you might know who installed what game, who installed which apps, and that might give you some insight into what other apps they may be interested in. So that could be useful. And secondly, also in the commercial motive space, you might have an idea of who’s a whale; who’s a big spender in an app. So that can be super, super useful, obviously.

Another way, of course, is trackability. And the New York Times showed us, I believe it was in December 2019, how easy it is to track a member of the Secret Service who is presumably travelling along with the President. 

They are going to various areas in Florida, where the winter White House is; the White House, and other places like that in New York City. So you can actually track people really, really closely. And you may not want to do that for me or you or just an average person, but you may want to do that for somebody who is part of an entourage or somebody who’s super famous or something like that. So there’s some power in misuse of IDFAs.

Shamanth: Of course, Apple’s motives aren’t purely unselfish here; Apple is also looking out for its financial and strategic priorities. As Thomas Petit explains in the episode, How to prepare for a post IDFA world as a marketer on iOS 14:

Thomas: I would argue that one of the big reasons Apple is making the IDFA move is to push its services revenue. That goes partly through increasing Search Ads market share over other advertising platform, but mostly through mechanisms favoring IAP over the ad monetization model – where Apple doesn’t take a cut. 

The IDFA deprecation might provide more control over privacy to users, but for Apple, it’s also good business.

And it’s helpful to understand some history of how we came to the effective deprecation of the IDFA. Apple had introduced LAT many years ago as a way to help consumers guard their privacy – and in many ways this backfired. David Philippson explains in the episode, A brief history of device identification:

David: Folks who have limited ad tracking as a social demographic group are tech savvy, high net worth individuals. When you consider that, so actually the mechanic of how to set limited ad tracking on, meant that someone has to be familiar enough with their iPhone settings, and actually have the persistence to go look and do it. 

If you’re an advertiser, that is a good demographic to target ironically, and almost amusingly, perhaps unfortunately, because it may have led to some of the deprecation we’re facing today. 

Limited ad tracking actually backfired because those users were targeted even more and some were still tracked with fingerprinting. Some of these dynamics have led to Apple’s most recent policy change, so that “do not track” really should mean do not track and try to target them even more, and then track them with fingerprinting.

Shamanth: And when you think about it, this level of granular availability of users’ private data can be surprising, even shocking to folks looking at the mobile app industry from the outside. David explains:

David: What our industry has grown up doing is taking device IDs from all advertisers, putting them into a device graph, and bidding for UA on what you know about the device. So even UA prospecting in-app is a form of retargeting — you’re targeting someone because you know something about them.

Whether it’s cookies, or iOS IDFAs, the negative effect is on behavioral advertising. Now, the difference with apps is nearly all marketing dollars are going to a form of behavioral targeting whilst that wasn’t the case in desktop. I’m expecting and predicting that the impact of iOS 14 is far greater on ad tech players in-app than ITP was on desktop.

This is because nearly all user acquisition, nearly all campaigns are a form of retargeting. This is the difference between what was a more web based world and what app is. 

It’s one huge retargeting campaign. When I speak to some desktop professionals, ad tech professionals that aren’t familiar with that, they look at me with shock and say, “What? Everyone’s sharing everyone’s data?” Well, yeah, some people put their head in the sand and pretend it hasn’t happened. Some people say, “Yeah, we know it happens. We’re addicted to the performance and the ROAS is great.”

Other people will say, this isn’t sustainable. It’s got a huge cannibalization effect. Apple will probably want to stop it, and it’s probably non-compliant. That’s the route it has gone.

WHAT ARE THE IMPLICATIONS OF THESE CHANGES?

Shamanth: There are far reaching implications of these changes – across every dimension of your growth stack. 

Firstly, the very notion of measurement changes fundamentally. In the current paradigm, there are significant problems with how attribution & measurement work, as Thomas Petit pointed out in the episode, How to prepare for a post IDFA world as a marketer on iOS 14:

Thomas: The deterministic solution, we had a lot of people believe that this was attribution. It’s partly a fallacy, attribution is very complex. That’s why when you look in App Store connect, and Apple is telling you that 70% of your downloads are organic and then you look in your MMP and it’s 30% organic — something is wrong here. I don’t know who’s right, but I know something is wrong. I’m not saying Apple got it right either. But in a way, it might be good because maybe it will make us rethink attribution in a very different way.

Shamanth: Another problem is that the notion of LTV has always been problematic. As we said in the episode, The LTV metric will die – but it was always an imperfect metric to begin with:

We’ve seen companies where different teams calculated LTVs differently – and this was the basis of significant political turf battles when it came to fighting for internal resources – both financial and headcount. These are just some of the contentious questions that we’ve seen:

• How do we compare LTV or the payback period for an app in our portfolio with front-loaded monetization vs. one with long-tail monetization?
• Should we use a longer payback period if an app has strong long-tail retention – and if most of its revenues come after year 1?
• Which statistical model should we use? Should we use conservative or aggressive estimates in our models?
• Should newer & unproven games get bigger ‘launch budgets’ until they prove their metrics?
• Should we allocate disproportionate budgets to a game that is wildly profitable from its legacy users but has low LTV from new users?
• Should we use a liberal or strict LTV/CPI approach (or in other words, should we discount the target ROAS numbers for games that have strong organics)? How much of organics should we attribute to paid installs?
• Should we have a more liberal ROAS model if we have a wildly viral game?
• Should we hit our quarterly profitability numbers – or sacrifice some of it for longer-term growth?

Maor Sadra from Incrmntal tells us more about why last-click attribution was the cause of some of these problems.

Maor: Most products’ marketing funnel requires more than a single interaction to cause users to convert – for them to become paying customers requires significantly more touches.

Last touch attribution does not provide insights over causality – it only shows who was the last to generate a user click. And while there is great benefit in knowing this – it may only provide a correlation, not causation.

Apple’s coming change eliminating the ability to create a 1:1 match between a click and a user encourages marketers to evolve their strategy.

WHAT CHANGES IN YOUR GROWTH STACK?

Shamanth The way your UA operations work on a day to day basis change fundamentally – mainly because your user economics get altered in a fundamental manner. As Eric Seufert explains in the episode, How to prepare for a post IDFA world as a marketer on iOS 14:

Eric: We know that the distribution of spend is a super long tail, especially in gaming, people that monetize to a very high degree. We know that there’s a big fat portion of the curve at zero, and so then when you say we can no longer target that super long tail, it’s not like efficiency drops by 20%. Maybe say, “Hey, our costs drop by 20%.” If the efficiency drops you might not be able to spend anything because you’re no longer able to target those high monetizers, and you’re trying to average out their contribution via 5% of a broader pool of people that you’re onboarding versus now where Facebook is able to increase that representation. And it’s still really hard to scale.

It’s still really hard for gaming companies to scale even though they’re using the most sophisticated sort of machinery in the world to hyper target the people that are most likely to spend in your apps — it’s still hard to scale. So without over indexing on those people that do spend money, I don’t think you can say, “Well, we know that we’re bringing in 100 users, and that for every hundred, one will spend.” I just don’t think user economics work that way.

Shamanth: How is your ad revenue and monetization impacted? Nimrod Zuta from ironSource explains:

Nimrod: As of now, it’s still difficult to predict the full impact on ad revenue, which will largely depend on two variables that, until Apple’s changes are implemented, will remain unknown – first, the rate of users who opt-into tracking and second, the ability of advertisers to efficiently manage their marketing campaigns.

Given this uncertainty, it’s likely that in the beginning some advertisers will pause spending while others may take advantage of the new situation and increase budgets. This contrast in advertising approaches means ad monetization CPMs will fluctuate at first. That said, once industry players have a better handle and understanding of what Apple’s changes means for their business, we can expect prices to eventually stabilize. 

Shamanth: How will the programmatic landscape change? What will DSPs and programmatic advertisers optimize for? Pau Quevedo explains in the episode, How to navigate programmatic advertising in a post IDFA world:

Pau: There will be variables like what device it is, what brand it is, what’s the size of the screen. How many megabytes of RAM do they have? Even how much space would they have left on the phone? Depending on the vertical, you will be interested in different variables. Let’s say you’re in the food industry, you might be more interested in the location exactly — the GPS. For gaming, particularly, we’re interested in the device, time of the day, publisher.

Shamanth: How will your retention & reengagement strategies be impacted? Andy Carvell explains in the episode, How IDFA changes in iOS 14 will impact retention and engagement strategies:

Andy: At a high level, the deprecation of IDFA, and the increased privacy changes that are coming in with iOS 14, shouldn’t be a huge impact to most people doing engagement and retention work, mostly because most of the tools that they’re typically using: their analytics tools, customer engagement platforms, or CRM, and maybe A/B testing frameworks: typically, these tools use what are called scoped identifiers to identify users. They would typically have an SDK in the mobile app that would assign an ID to a user the first time that they see that user, and then they know that’s the same user when the user is back again. So in the case of analytics, whenever the user is back in the app, they’re sending events, they’re sending this ID along with it. And the tool or the platform—whether that’s internal or a third party solution—will use this tool-specific ID to identify that user. And there’s no reason why those tools should be using the IDFA for that. 

However, some tools, just the way that they’ve been engineered—or possibly there are internal tools—they may have been built using the IDFA; for whatever engineering decision was behind that. And maybe they thought that this is always going to be there, and it’s the perfect thing to use to identify the user. I think it would have been a bad engineering choice actually, particularly in retrospect. But even before iOS 14, we saw people opting out of the IDFA. So it’s not a good universal identifier to be trying to track every user anyway. And certainly, now, it’s not something you should use to generate those sort of scoped identifiers. But the good news is that there are lots of other ways that you can create an ID and assign it to a person for a specific use case like analytics, and all of the commercial tools are releasing new SDKs if they need to rework anything.

HOW TO COPE AND ADAPT

Shamanth: How do we adapt to this brave new world? Apple’s solution SKAdNetwork is a good starting point for ensuring that you are able to track the effectiveness of your UA spend in the best possible way. 

One idiosyncrasy of SKAdNetwork is the conversion value – which is critical to get right to succeed with UA. Kevin Bravo describes how to think about conversion values in the episode, How to design a conversion value framework for succeeding with SKAdNetwork:

Kevin: I like to think about it like a signal. You can add kind of a context to the user. It’s like answering the question: “How valuable is this user to me?” You can call the method as many times as you want. The way it works is that it resets the timer—the first one of 24 hours. So as long as the user keeps coming back into your app, you can still keep calling the conversion value method. 

There are a couple of things to consider: First one, it can only be higher. So if you log a conversion value of 10, the next time you call the method, you have to call 15, for example, or 20, and so on. So that’s the first thing to keep in mind. The second thing to keep in mind, as I said, is that every time you log it, the timer resets. And since you don’t have a time of the install timestamp for the postback, it means that every time you call a big conversion value, you are making it harder for you and for the network to understand when the install actually occurred, because you cannot delay the moment of the postback being sent. 

Shamanth: What are the considerations you use to decide your conversion value structure? Kevin adds: 

1. Your business model: Are you subscription-based, ad-based? How are you making money? 

2. Vertical you are in: You’ll see like many companies in fitness, for example, are going to do the same thing and so on. So that’s something you can get inspired by. 

3. How you differentiate between low-value users and high-value users in their first session: try to understand what does a user need to do for me to feel he is valuable, because it’s early, so you don’t have that much time. But you can still get some signals. 

4. How were you historically optimising your ad campaigns? I don’t think you can just switch and keep the same system, but maybe you can learn from the past, the way you are doing it, so you can build a new model for this new solution.

Shamanth: What measurement infrastructure will you need in iOS 14? Could you build a system in-house because SKAdNetwork appears simpler than deterministic measurement? What will be the place of the MMPs in the new paradigm? Gadi Eliashiv explains in our episode, How mobile measurement will work with iOS 14 in a post-IDFA world:

Gadi: I’ve seen people in the past try to build some of these things in house, and it always eventually fails. It sucks. One person wrote it, he leaves the company, they’re stuck. It’s not standard, it falls behind, etc. So I don’t think this is any different. There’s been examples of companies who built their own MMP stack internally, and it would have a lot of holes, it wasn’t really smart, and it had fraud, etc. I think it’s gonna be the same thing, but just different because you will still need to figure out: “How do I receive all this SKAdnetwork data?” Let’s say that Apple agrees, and you now get it as postbacks. So are you going to build your own real time server to get these postbacks, and then forward to other ad networks? You could, but it’s insane, you can do that. Then you need to make sure that they are not deduping. They’re not sending duplicates. So you need to start setting up your own fraud mechanisms and then also do the BD with ad networks to talk to them and say, “Hey, you’re sending me duplicate postbacks, you’re trying to cheat me.” You could, but it’s intense. You need to build the layer that manages the conversion value. 

When you send events, you need to translate that to conversion value, and then also tell the ad network what it means. So can you do that? Yes. But again, it’s intense. You need to aggregate all the data, all the spend from every single channel you’re using on the campaign. You need to map the campaign ID from SKAdNetwork to something readable, you need to build the reporting. Again, all these capabilities, the world doesn’t change as much to me because of the necessity I think MMPs always had is they solved the technical problem that was pretty hard to solve. And yes, they were in a central position. But a lot of the power of the product came from just like how you buy any other SAAS product. 

I think advertisers will still kind of hire an MMP. They’ll say, “Hey, I need to figure out all this mess. Please be my technology provider,” which is how you buy SAAS software, and help me sort out all this mess. And yes, maybe the way that clicks were being forwarded to the MMP before, some of it may change. Actually, that’s not even going to change because what if you get partial consent, then are you not going to look at the IDFA? You probably still want to have it, so you’re going to have the MMP just for that portion. It’s just gonna be such a complex universe, which is why I think you’ll still need it. You’ll still need to have someone help you with all that stack. 

Shamanth: Of course, measurement via SKAdNetwork in and of itself may not be enough. Understanding incrementality will be critical to understanding the true impact of your UA channels. As Maor Sadra from Incrmntal explains:

Maor: Utilizing causal Inference to measure the effect of paid marketing campaigns over user acquisition allows a marketer to identify causation. Understanding causation helps a marketer know what is the value of campaigns, what the value campaigns have on one another, and which campaigns do not bring any value, but are taking credit for users that would use their product organically. 

To apply causal inference to digital advertising requires an understanding of the marketing world. Data in marketing is noisy. There are many internal and external factors influencing one another: seasonality, competition, featuring, weekdays, special days and so on. Marketers are often running campaigns across many countries and they need data to make decisions. 

Applying causal inference in marketing is difficult. Reaching confidence may need active intervention in the marketing. It is impossible to isolate internal and external conditions, which is why a practical use of difference in difference (DID or DD) is applied in parallel with Shapley Value, a concept from cooperative game theory, to continuously provide actionable insights.

Shamanth: How do you adapt if you’re a publisher – or you’re a developer working with one? Nebojsa Radovic from N3TWORK explains how things could change for you – and how you should pick a publisher in the new paradigm, in the episode, How to prepare for a post-IDFA world as a marketer on iOS 14:

Nebojsa: For publishers that have a lot of payer profiles, the impact of those payer profiles will diminish — it just won’t be as big as it was before. So it might make more sense to work with a publisher that’s really good with technical UA, understands incrementality, understands how to do a probabilistic distribution than work with a publisher that has a lot of IDFAs that they could retarget because that won’t be possible anymore. The good thing about payer profiles is that Facebook and Google are already moving away from them. Facebook is testing AAA ads that don’t really leverage look-alikes. That means that they won’t be as necessary.

I think the key point here is that if you’re a small developer, you should really look for a publisher that’s really good at technical UA, that understands how to find players without AO and VO, how to do the proper campaign setup in the post SKAdNetwork world, and understands incrementality lift studies, etc. 

Another interesting thing is that it’s questionable whether DSPs will exist. They will to a certain extent, but without the ability to target specific IDFAs or to be more precise to filter out specific IDFAs that are never going to install your app, it’s likely that the cost is going to go up. Publishers with a DSP, at least in the short term, might not really have that competitive advantage. 

Shamanth: UA flows may themselves change completely. Web based UA flows might become more prevalent – and it might be worth actively thinking about these. As Colette Nataf explains in the episode, How to drive web-based user flows for your iOS 14 user acquisition:

Colette: So we work with a company that is a privacy app. And so what that means is that they’re all about data protection. 

And so this company, it would go very much against their fundamentals to install the Facebook SDK, and they’ve actually taken it a step further, they don’t have any MMP, they have no SDK installed in their app, which is pretty incredible. So they built everything in house. And what that means is that, you know, because they don’t have any SDK, because they don’t have the Facebook SDK, they don’t have an MMP, they need to do things flying blind, and figure out ways to understand attribution, without that sort of data. 

So this is really my example of the worst case scenario, because they’re already operating in a worst case scenario world where we don’t have any attribution at all. And so what they do, and what they’ve found is the solution is that they run Facebook ads, on mobile devices, but to a landing page on mobile web. On the landing page, they get their users’ phone number, and send a text message with a link to install and download the app. 

So what’s actually happening is that they’re going way back in time, before any kind of mobile tracking was a thing. And they’re putting UTM, URL parameters into that into that URL, they have your campaign name, the campaign ID, etc. And they’re linking that to the cell phone number and figuring out that somebody installed the app. That’s that’s how they marry those two data points together. 

It’s going to be imperfect – maybe somebody gets their cell phone wrong, maybe they leave the page and go directly to the App Store. But at least you have some data. So you’re able to optimize campaigns, you’re able to figure out, which of these ads is the best performer. And then they can actually use conversion optimizer on Facebook with the pixel so on when somebody enters in their cell phone number, and confirms that’s the event that they target. 

Shamanth: How will the economics of web-based campaigns be different from right now? Colette adds:

Colette: In terms of CPM, I haven’t noticed a huge difference there. Mobile app installs versus mobile web, the difference really comes down the funnel. And it’s really in that install event, there’s more steps to complete, you’re not going directly to the App Store. And so the install rates are low. And that’s just going to be true. Like, there’s no way to avoid it. Anytime you add steps in your funnel, you’re gonna see drop off. 

And I think their install rate is probably even like half of what it would be if it were running directly to the App Store…

So there are going to be changes, not necessarily on a CPM level, maybe not even click level. But at that, at that conversion rate to install, you’re gonna see a drop off.

Shamanth: How does your creative strategy change? Gabe Kwakyi spoke about the process of improving his team’s creative hit rate – this process becomes even more critical post iOS 14. As Gabe explains in the episode, How to improve your team’s creative hit rate:

Gabe: Once you do achieve that huge spend, and hits also hog spend, you’ll see all the spend starts to go to this one asset. When that happens, and it pulls away from everything else, you know you probably have a hit if you’re looking at daily and even hourly trends.

We have our North Star as you mentioned is hit rates and that’s what we look at at the team level and the company level. For individuals, it’s a lot of focus on more concepts than iterations to begin with. This actually goes a little bit contrary to some of the ad network suggestions that you build out every size, every dimension, multiple iterations before you start, but you have limited ad slots in some cases, and you have limited spend in other cases. As you mentioned, the networks will start picking things up, it may not be a good performance on all the dimensions or assets or iterations at first. 

Another step has been more concepts testing broader before we start to really iterate. Another is really just having more discussions with our clients and planning our process so that we’re trying to establish a monthly cadence for brainstorming new ideas and then a weekly basis. We’re trying to launch and then analyze creative performance. Using metadata tags is also important, where we tag the theme, and then within that we have the sub theme, which might be iterations, and just trying to improve the metadata that gets passed through to the analysis stage. So there’s a few things that we’re doing to improve our infrastructure and team process for generating hits.

Shamanth: How do you change your approach on rewarded video networks? You replace ROAS or CPA based optimization by conversion value based optimization – and you lean on automated rules to optimize ad networks by individual source apps. As Warren Woodward described this approach for a pre-iOS 14 world in the episode, How to build your own algorithm to drive performance on rewarded video networks, and how the same approach can be modified for a conversion-value based post-iOS 14 world.

Warren: We built a system based off of rules. So just coding some simple logic for how you bid for individual placements on the network, and then building a script to run that logic perpetually, to optimize your campaigns without using human oversight. So really, what we are trying to do is, take a really great UA manager’s day to day work, codify it into a set of rules, and then build scripts to run those rules.

The one that probably everyone will get is, you know, ROI or ROAS based rules. So, looking at all of the individual placements on a network, looking at the value of the users that are coming from those individual placements, and using that to make judgments on the appropriate bid and the expected lifetime value of different sub segments. Another one that probably is universal to all apps – and I should probably clarify, when we started this, we intentionally wanted to build really basic rules that you could apply to any app that weren’t specific to gaming only or a particular product. So, retention is another really common one that you can easily build rules around – like your day 1 retention, day 2 retention can be very early signals. And another one is like onboarding events like, completing a tutorial in a game or completing a free registration, another might be buying in a subscription app, for example.

Shamanth: A big part of adapting will be in maximizing consent to access IDFAs from users who opt-in. Andy Carvell explains some of the key ways in which you could maximize consent in the episode, How IDFA changes in iOS 14 will impact retention and engagement strategies:

Andy: So there’s a couple of sort of textbook techniques, right. One is to ask people to opt in for stuff like this at a sort of a happy moment. When they’ve just had a really good experience, you can kind of infer that they really love the app, and hence, it’s a great time to ask them. Yeah, that works to some extent. 

But actually, what I found to work better is first of all, using a pre-permission. It’s basically an in-app dialog, which you control as a marketer or as a developer, where you ask the user: “Hey, would you like to opt in for this?” And you can test, of course, with that different benefits and things to optimise the click rate. And when people say yes, then you show them the system dialog, which Apple only allows you to show once.

If they say no, you can try again a bit later at a happy moment or with different benefits that you’re offering. So you can keep trying. And then you sort of save that one shot system dialog for a time when you really think that the user is going to say yes. So that’s very effective. That’s what we call pre-permissioning. And then on the other side, what are called win-backs, is going back to your loyal users, the users who are still in the app after two or three weeks, but they’re opted out. And you ask them: “Hey, actually, now would you consider opting in and we can take you straight over to settings.” And you can actually, like link them straight over to the settings menu and have them turn it back on. That’s actually crazy effective.

Shamanth: How do you adapt your ad monetization strategy? Nimrod Zuta from ironSource explains:

Nimrod: It’s a good idea to turn on or start leveraging in-app bidding as a monetization management model. Especially in dynamic or volatile situations, in-app bidding can ensure that your monetization operations are fully automated and optimized. 

A good (and safe) way to get started is to set up an A/B test, testing the performance of an in-app bidding waterfall against a traditional waterfall to ensure that in-app bidding is increasing ARPDAU. 

Shamanth: How does all of this change your financial and strategic planning? What should you account for in your financial planning? Mark Loranger explains in our episode, How to finance your app’s growth without accessing venture capital:

Mark: My recommendation in this case is similar to what I said earlier for a company that’s just starting to hit some scale with a new ad platform or a new UA channel, but doesn’t have any certainty that it’s going to be successful in two months. It’s the same situation here, which is, I would be careful to rely on making really long term commitments to whatever plan that you had previously and to whatever assumptions that you took into your modeling previously. 

I would say that in the context of funding decisions, I would caution companies against committing to raising lots of equity with a goal and a plan in place that you believe will be driven primarily by user acquisition because it’s unclear what that’s going to look like. 

This reminds me a lot in a less globally impactful way of the pandemic when it hit most seriously in February, March. We were thrown into this situation of uncertainty. The question was, how do we plan for the long term? Most people were saying you need to focus on the fundamentals. You need to try to find ways to just continue to create economic value in your business without necessarily committing to things that you won’t be able to fulfill in a longer period of time until you have a better sense of how you’re going to execute. 

I think it’s optimizing for short term commitments, while you learn how your business will work post iOS 14. That’s the best answer I can give. It’s about being a little bit more conservative probably. And certainly, that’s going to hurt my business if customers are super conservative because they’re not going to want money. In the context of long term value creation, you just have to be a little bit conservative sometimes in committing to things that may or may not be able to materialize once we figure out how to survive and hopefully thrive post iOS 14, but it’s going to be a period of time where things go badly if you are monetizing through ads. 

Shamanth: That is perhaps a good place for us to wrap up. We don’t know what precisely will happen, but we have perspectives from these amazing folks that we hope will guide us through the world that is coming.

As you go into this brave new world, we hope that the advice and wisdom of these folks help you navigate the massive changes that are coming. 

All the best!

A REQUEST BEFORE YOU GO

I have a very important favor to ask, which as those of you who know me know I don’t do often. If you get any pleasure or inspiration from this episode, could you PLEASE leave a review on your favorite podcasting platform – be it iTunes, Overcast, Spotify, Google Podcasts or wherever you get your podcast fix. This podcast is very much a labor of love – and each episode takes many many hours to put together. When you write a review, it will not only be a great deal of encouragement to us, but it will also support getting the word out about the Mobile User Acquisition Show.

Constructive criticism and suggestions for improvement are welcome, whether on podcasting platforms – or by email to shamanth at rocketshiphq.com. We read all reviews & I want to make this podcast better.

Thank you – and I look forward to seeing you with the next episode!