Mobile Dev Memo https://mobiledevmemo.com Mobile advertising and freemium strategy. Mobile marketing, analytics, and monetization. Wed, 08 Jul 2020 20:00:31 +0000 en-US hourly 1 58872998 Podcast: Surfing in a Squall: the future of mobile without an IDFA, with Maor Sadra https://mobiledevmemo.com/podcast-surfing-in-a-squall-the-future-of-mobile-without-an-idfa-with-maor-sadra/ https://mobiledevmemo.com/podcast-surfing-in-a-squall-the-future-of-mobile-without-an-idfa-with-maor-sadra/#respond Wed, 08 Jul 2020 19:44:55 +0000 https://mobiledevmemo.com/?p=30141

In this episode of the Mobile Dev Memo podcast, I speak with Maor Sadra about why the deprecation of the IDFA (announced at this year’s WWDC event) should not have been surprising to people who follow the mobile ecosystem, why deterministic app install attribution wasn’t as reliable as people thought in the first place, and what the future of mobile looks like without the IDFA.

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Dear Apple: These changes will improve SKAdNetwork for advertisers https://mobiledevmemo.com/dear-apple-these-changes-to-skadnetwork/ https://mobiledevmemo.com/dear-apple-these-changes-to-skadnetwork/#respond Mon, 06 Jul 2020 05:30:00 +0000 https://mobiledevmemo.com/?p=30106

Dear Apple,

The privacy protections you are implementing in iOS 14 are indisputably good for consumers: these changes will give iOS users a level of granular control over their data that amplifies trust. In the short term, these changes present challenges to mobile advertisers, most of which have built infrastructure for which the IDFA is integral to advertising measurement and analytics. Advertisers will feel some pain as they transition away from this infrastructure, but I believe that the long-term consequences to advertising performance of user-level attribution being abolished are exaggerated.

As an analyst of the mobile ecosystem, I find it more beneficial to think about the durable solutions to changes that will persist three to five years from now than devoting energy to thinking about short-term workarounds or loopholes that will only be viable for some number of months. It is for this reason that I believe all advertisers would benefit from planning their infrastructure around SKAdNetwork, which is the long-term solution to advertising measurement on iOS. You have made it clear that fingerprinting will not be allowed if a user has opted out of ad tracking, and other similar, awkward shortcuts are unlikely to be workable for enough time to be interesting.

It is also for this reason that I believe it worthwhile to propose changes to SKAdNetwork that will benefit advertisers while still preserving Apple’s core commitment to user privacy. SKAdNetwork can be an incredible tool for mobile advertising measurement, but in the incarnation described in its technical documentation, it lacks some critical functionality.

A spirited discussion is taking place on the Mobile Dev Memo Slack around how SKAdNetwork will be implemented and how advertisers will be able to transition to this new paradigm. Allowing advertisers’ voices to be heard now will likely alleviate the need to police loopholes and workarounds later. Most advertisers with whom I’ve spoken in the weeks since WWDC20 want SKAdNetwork to succeed as the permanent solution for mobile measurement on iOS; allowing advertisers to become invested in its development is a win for the entire ecosystem. This ecosystem, including Apple, should be aligned around a privacy-centric measurement solution that facilitates advertising growth, and the concomitant increases in developer and platform revenues that result.

That said, I believe there are improvements that can be brought to three functional components of the SKAdNetwork workflow: conversion measurement, campaign tracking, and data retrieval.

SKAdNetwork Conversion Measurement

The current SKAdNetwork conversion measurement logic is overwrought and complex, and the way that conversion identifiers are implemented doesn’t provide enough value to the advertiser. A conversion event should be useful to an ad network in optimizing its targeting within the context of some optimization objective. The current implementation of the conversion identifier only allows for one event — the highest value recorded via the 6-bit field within some cycle of a resettable timer — to be associated with an install attribution, meaning the source network receives no insight into the number or magnitude of the events fired by the user.

A more helpful approach would be to create a second postback type specifically for conversion events. Ad networks use conversion events to target the most engaged users: the magnitude and frequency of events are important considerations of campaign success. Further, the construction of the current conversion event timer logic incentivizes app developers to concentrate their monetization mechanics very early in the user lifecycle so as to produce meaningful monetization signals as quickly as possible. This will result in a degraded user experience: users will feel like their pockets are being picked with aggressive sales tactics, especially with subscription apps, which will need to find ways to incentivize immediate subscriptions so as to feed their ad networks with conversion events in order to capture any monetization data.

Sending conversion events as separate postbacks will allow ad networks to track the magnitude and frequency of these events at the campaign level without incentivizing app developers to front-load the user experience with monetization hooks. And privacy would still be preserved: the ad networks that receive these events back wouldn’t be able to tie them to individual users since they have no insight into the cohort to which the installs belong, and thus they would be prevented from using this information to build device graphs.

SKAdNetwork Campaign Tracking

There are a number of issues with the existing incarnation of SKAdNetwork that prevent campaigns from being optimized effectively. The first and most obvious of these is the arbitrary limit of 100 tracked campaigns. I understand that advertisers can micro-target campaigns in such a way that could lead to near-deterministic attribution, but 100 campaigns is simply too few to allow for adequate segmentation, especially in this new paradigm that reverts campaign management strategy back to circa 2014-2015 best practices. A limit of 1,000 campaign identifiers would achieve the same privacy objective while also giving advertisers enough agency to segment traffic competently.

Second, a campaign identifier isn’t sufficient for optimizing and iterating on ad creative. Advertisers really need an advertisement identifier to be able to build creative production processes that allow them to hone messaging.

SKAdNetwork Data Retrieval and Aggregation

The current workflow, in which a postback is sent to the ad network that generates an install, allows ad networks to optimize campaigns, but it creates some complications by granting first party ownership of attribution data to the ad networks. Users don’t have relationships with ad networks, they have relationships with apps: is it not more seamless and sensible to give advertisers first-party access to install data and let them decide with whom they share it?

Consider making attribution data available to advertisers directly via API. Advertisers could connect directly to the API and ingest all of their attribution data, across all ad networks, in one place, as opposed to the current solution which requires advertisers to ingest data from each of the advertising network that they work with. The current flow of data — from the SKAdNetwork SDK in the app to the ad network — gives ad networks an incredible level of power in determining how that data is subsequently transmitted. Advertisers should have a means of accessing their attribution data without depending on the ad network sending it to them (or any other party).

The current flow of data also burdens app advertisers with a daunting amount of overhead, requiring them to build connections to every one of their ad partners’ APIs in order to ingest siloed advertising data. As anyone who has ever built a cost aggregation system for advertising spend knows, these APIs change and break frequently, causing data availability issues that occur with frustrating regularity.

Conclusion

SKAdNetwork is the future of mobile attribution on iOS, and it will play a critical, focal role in mobile advertising measurement. But an ounce of prevention is worth a pound of cure with SKAdNetwork: by engaging with advertisers and app developers around what they need in an attribution system, Apple can avoid the cat-and-mouse game that will inevitably emerge as ad tech companies create workarounds for user-level attribution that violate the spirit of the changes introduced in iOS 14.

The next few months present a valuable opportunity to re-cast mobile advertising with consumer privacy as a fundamental precept. I think SKAdNetwork can both allow advertisers to perform the measurement necessary to grow their revenue through performance advertising and protect consumer privacy, but it’s currently missing core functionality if it is to replace install attribution on iOS completely.

Photo by Nizzah Khusnunnisa on Unsplash

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Introducing the Modern Mobile Marketing at Scale online course https://mobiledevmemo.com/introducing-the-modern-mobile-marketing-at-scale-online-course/ https://mobiledevmemo.com/introducing-the-modern-mobile-marketing-at-scale-online-course/#respond Wed, 01 Jul 2020 05:29:00 +0000 https://mobiledevmemo.com/?p=29778

Modern Mobile Marketing at Scale was an in-person, 8-hour workshop that I presented in New York, San Francisco, and London last year and had intended on presenting three more times over the course of 2020. I spent nearly 3 months developing the content, which is a comprehensive review of mobile marketing: analytics and reporting, creative strategy, cohort analysis, and specific tactics and strategy for Facebook and Google UAC marketing.

Due to COVID, I have canceled the remaining workshops that were scheduled to take place over the rest of the year and adapted the content into an online course. The online course runs at more than four hours over six modules. The modules are:

  1. Modern mobile ad platforms
  2. Ad creative strategy
  3. Recoup analysis
  4. Analytics and reporting
  5. Facebook
  6. Google UAC

Modern Mobile Marketing at Scale is a great fit for people who want 1) a deep conceptual foundation in mobile marketing as well as 2) a broad primer on tactics that successful companies use to scale their marketing spend profitably to millions of dollars per month.

This course can benefit product managers, user acquisition managers who are being promoted to team lead positions, company executives, finance / accounting managers, creative directors, etc. The course provides an overview of the entire mobile marketing function, so students should leave with a very strong understanding of how user acquisition teams operate and systematically grow spend.

The course can be found here.

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Apple killed the IDFA: A comprehensive guide to the future of mobile marketing https://mobiledevmemo.com/mobile-advertising-without-the-idfa-a-comprehensive-overview/ https://mobiledevmemo.com/mobile-advertising-without-the-idfa-a-comprehensive-overview/#respond Mon, 29 Jun 2020 05:00:00 +0000 https://mobiledevmemo.com/?p=30032

Tremors from Apple’s bombshell revelation at WWDC last week that the IDFA will effectively be killed continue to reverberate. iOS 14 will be released in September, and if past iOS releases are any indication, more than half of all iOS devices will run iOS 14 by October. WWDC 2020 featured Apple’s Thanos moment: with a snap of its fingers, Apple obliterated a large proportion of the mobile ad tech ecosystem.

A brief overview of the IDFA-related changes that will be introduced in iOS 14:

  • Device IDFAs will be made available to specific apps on the basis of user opt-in via a pop-up at app open. LAT as a setting will continue to exist at the device level, meaning no apps will have access to the device IDFA if LAT is turned on;
  • Apple will use the SKAdNetwork API to receive meta data from ad clicks and to send postbacks from the app client to advertising networks that drive app installs. As of iOS 14, new parameters will be added to SKAdNetwork that provide information around the source publisher, and a conversion event;
  • The postbacks to ad networks can include ad campaign IDs, but only 100 values (labels 1-100) per ad network are available to be mapped;
  • The postbacks to ad networks can also include up to 64 conversion event IDs via a 6-bit conversion value;
  • The postbacks to ad networks will also include a “Redownloaded” flag that indicates whether the app is being downloaded by a user for the first time or not;
  • Postbacks will be sent based on a sequence of timers (the logic is somewhat complicated — more information on this below) and will include just one conversion event per attributed install. This means that advertisers won’t get absolute counts of events but rather counts of highest value events that users complete within the conversion measurement period;
  • IDFV, or ID For Vendors, will continue to be made available for publishers per device for all of their apps. What this means is that a publisher will have a unique device identifier available to it across its apps on any given single device. That is, if a user has installed three of my apps on her iPhone, I will have access to a unique IDFV for that user that is the same as accessed from all three of my apps.

In Apocalypse Soon, which I published in February, I outlined a hypothetical chain of events that began with Apple deprecating the IDFA at WWDC 2020. The IDFA has been living on borrowed time since the introduction of Limit Ad Tracking; its elimination was wholly foreseeable. I posted my thoughts on how the death of the IDFA will impact the mobile advertising ecosystem in this Twitter thread, which still represents my latest understanding. Below is more detail on the topics covered in the thread, as well as a few additional topics.

Opt-in rates for IDFA access

My belief is that user opt in rates for IDFA access will fall between 10-20%. Opt in rates will dictate the impact of these privacy changes on all aspects of the mobile advertising ecosystem, but with opt-in rates at the 10-20% level, the IDFA is effectively dead.

In an informal poll conducted in the Mobile Dev Memo Slack team, the majority of respondents indicated that they believe opt in rates for ad tracking will fall between 0-20% (see below). The language presented within the opt-in popup is intimidating: App X would like permission to track you across apps and websites owned by other companies (note that a second string of text underneath this one can be customized by the developer). This verbiage seems specifically designed by Apple to deter users from accepting tracking.

Attribution

I believe that deterministic, user-level app attribution will cease to exist once SKAdNetwork adoption reaches critical mass — likely by the end of the year or throughout Q1 2021.

I’ve heard the argument that some advertisers might continue to use MMPs to attribute the small (10-20%) subset of users that opt into ad tracking, because that sample of users could be used to extrapolate composition ratios to the broader set of acquired installs. For instance, if 30% of the opt-in users were acquired via Facebook ads, then that ratio might be applied to the cohorts of opt-out users whose provenance is unknowable — this knowledge could help advertisers budget ad spend, and would justify MMP tracking for the opt-in users.

But there is sampling bias inherent in this: the group of users that opt into tracking are unlikely to behave like those that don’t. We see this now with LAT users: LAT users for many advertisers tend to monetize better than non-LAT users, with the hypothesis being that they are more technically savvy (because they managed to find the LAT setting).

And even if the opt-in subset of users was useful for the purpose of extrapolating ratios, this small volume of attributions would not support the MMP industry at the scale that it currently exists: either dramatic consolidation would take place or a few of the major firms would simply fail as MMP budgets evaporate to 10-20% of their current level.

Another factor that will determine the fate of user-level ad attribution is Google’s adoption of opt-in tracking for Android. If Google doesn’t create an opt-in tracking mechanism for Android, then install attribution could continue to exist to serve the Android market. But this seems unlikely: Google and Apple operate more or less in lockstep with respect to privacy. Google introduced its LAT equivalent (Ads personalization) one year after Apple introduced LAT. Furthermore, Google has had attribution services in its crosshairs since at least 2017, when it introduced install attribution to Firebase.

It seems possible that MMPs adapt to this change by serving as auditors: receiving install receipts from ad networks, aggregating them, and reconciling campaign and event identifiers against naming conventions and revenue values, and using cost data to present probabilistic ROAS reporting.

This functionality would be useful and is almost certainly something that few advertisers want to build themselves, but it’s also mostly administrative: an accounting service of sorts that doesn’t unlock much value. Put another way: this isn’t a service that advertisers would be willing to pay tens of thousands of dollars per month for, and it wouldn’t support the current size and scope of the mobile ad attribution market. MMPs will need to pivot into lines of business that provide commercial insight if they want to continue to charge MMP prices.

Re-targeting and Custom Audiences / Lookalike Audiences

Re-targeting on mobile is currently done via advertising identifiers: an advertiser provides Facebook or a re-targeting DSP with a list of advertising identifiers that it wants to target, and those channels serve ads to those devices when a matching impression or bid request surfaces.

Without an advertising identifier to use in serving a specific device, it seems unlikely that re-targeting DSPs will be able to function as they currently do: they simply won’t be able to accurately identify a given device. Facebook will be able to facilitate re-targeting, since it can use any number of user-specific (versus device-specific) identifiers to target individuals: email addresses, phone numbers, etc. Back in 2014, I posited that Facebook acquired WhatsApp specifically to amass a large bank of phone numbers, which are perfect identifiers because they rarely change and are tied exclusively to a device (versus an email address, which is tied to an account that can be accessed from any number of devices).

This reality is likely to impact product design: developers will seek to either incentivize or simply require users to register accounts using their email addresses or phone numbers so that those identifiers can be stored and used for re-targeting and lookalike list construction. The ad platforms that don’t already offer lookalike and custom audience construction directly from lists (notably, Google UAC) will also likely roll these products out to compete with Facebook.

Of course, all of this assumes that Apple and Google are amenable to email being used as a proxy for the advertising identifier when users have explicitly opted out of ad tracking. A user might be horrified to know that they restricted an app’s access to their advertising identifier — which can be reset — only to have their email address, which is attached to many other aspects of their life, used for the same purpose. Note that Apple introduced its privacy-centric Sign in with Apple authentication service at last year’s WWDC; it could potentially enforce use of it as an alternative for user registration in apps to prevent exactly this scenario.

Managed DSPs

In discussing mobile DSPs in the new, IDFA-less environment, a distinction must be drawn between in-housed or self-serve DSPs, which facilitate advertiser-led programmatic media buying, and managed DSP services, which are utilized by advertisers essentially as ad networks.

As discussed in the recent MDM Podcast, How a bid becomes a DAU, the core value proposition of managed DSPs are their device graphs, or their lists of device advertising identifiers paired with knowledge around which of those devices have monetized, and in what apps. Once these device graphs become irrelevant, managed DSPs will lose competitive advantage over other programmatic solutions, even in-house DSPs. My former colleague Nebojsa Radovic summarized this dynamic in a Twitter thread:

It’s unclear what path forward exists for managed DSPs without advertising identifiers. Managed DSPs can of course optimize advertising campaigns at the publisher and placement level, but that level of granularity tends to not be efficient for advertising optimization: programmatic supply suffers from an adverse selection bias as most programmatic inventory is backfill. And if advertisers can in-house programmatic spend and be on equal footing with managed DSPs, why would they pay hefty fees to managed services?

updateConversionValue(_:)

The Conversion flag in an SKAdNetwork postback will allow the advertising network to receive notification that some conversion event has taken place from within traffic acquired by a campaign. This flag will allow ad networks to count events at the level of the campaign cohort, eg. Campaign X generated 15 counts of Event Y. But this is worth clarifying further.

Advertisers will be able to map up to 64 in-app events to identifiers (via the 6-bit conversion ID parameter in the SKAdNetwork postback) that will be sent to SKAdNetwork via the updateConversionValue method when executed. Whenever an install is attributed by SKAdNetwork, a rolling 24-hour postback timer begins counting down to 0, and each time a mapped event is fired, the timer resets. Once the timer reaches 0, the postback is fired to the ad network that supplied the install.

A mapped event identifier will only be recorded for the postback if either no conversion event has yet to be recorded for the attributed user or if the identifier is larger than that which is previously recorded for the attributed user. According to the SKAdNetwork documentation, it seems that the postback timer can be reset up to 64 times, so long as increasing event identifiers are being recorded. Once the initial timer completes, Apple begins another timer on a random length of time between 0 and 24 hours to obfuscate the source of the event, and the postback is fired when that second timer completes.

This postback logic will dramatically alter app monetization design: advertisers will aspire to strike a meaningful balance between minimum elapsed time between install and postback receipt and maximum monetization and / or engagement signal contained within the postback (ie. higher values of the conversion event carrying important information about the value of the user).

Developers will strive to instrument their apps such that the most valuable users fire as many of the 64 events as possible within the first few sessions of app use to facilitate the postback being received in a timely manner. This new dynamic will accelerate the existing trend of integration between app monetization and user acquisition: the user acquisition team of the very near future will play a central role in designing the content path that users take before a conversion event is fired via SKAdNetwork.

Event-optimized campaigns

Since networks receive conversions, they can optimize campaigns against them, albeit not at the granularity level of individual user identifiers, and not with exact event counts: if a network receives a conversion identifier, it will only know that the event mapped to that identifier executed at least once, but it won’t know whether that event executed more than once or if any other events with lower identifier values were executed.

Facebook, for instance, could use these conversion receipts to optimize targeting with broad demographic features as well as to build correlations between targets and app types. This won’t be as efficient as its current method, which uses the features of individual users — especially monetization history — to hone targeting for the App Event Optimization (AEO) campaign strategy, but it will still allow for event-optimized targeting.

The Value Optimized (VO) campaign strategy is harder to execute without advertising identifiers because it relies on the magnitude of monetization expected from an individual user — if the advertiser is unable to post all revenue events back to Facebook with user identifiers attached, and Facebook can only receive the one event signal per user per campaign (absent any revenue data), then Facebook has no way of gauging magnitude of monetization or engagement. All of this similarly applies to tCPA / tROAS campaigns on Google’s UAC.

I believe that the shift to SKAdNetwork-tracked conversions might actually benefit ad networks that compete with the Self Attributing Networks (SANs). Currently, many advertisers withhold conversion events from non-SAN ad networks in an attempt to hide the identities of their most valuable users from them: if an ad network knows that User X monetized in App A, the network might specifically target User X for App B‘s campaigns in order to improve their conversion efficiency. Given that revenue context is excluded from the SKAdNetwork postbacks, there’s no reason for any advertiser to withhold that information from ad networks, and they are put on equal footing with Facebook and Google.

In-app ad monetization

In-app monetization is governed by the same forces that change the economics of DSP advertising. Header bidding especially will be fundamentally impacted by a lack of advertising identifiers: header bidding provides for a programmatic unified auction that allows advertisers to bid on inventory at the ad level (versus ad waterfalls, which bid on CPM values that are set intermittently based on historical averages).

It seems likely that the eradication of advertising identifiers depresses CPMs across the board because performance will only be measurable at the campaign level. It is very difficult to make the economics of programmatic buying work if individual users can’t be targeted based on proprietary information. If bid logic reverts back to historical campaign performance (via the conversions logged with SKAdNetwork postbacks), then header bidding — which, again, provides the ability to bid at the level of an individual impression — won’t really confer any advantage over CPM-based waterfall ad mediation.

Cross promotion

One interesting tidbit found in the updated SKAdNetwork documentation is the fact that the Identifier For Vendors (IDFV) will remain accessible regardless of whether a user has opted into ad tracking. The IDFV is a unique device identifier available to app developers across its own apps on a given user’s device. For example, a User X might have three of Developer A‘s apps installed on her phone: App 1, App 2, and App 3. In this case, Developer X would have access to a unique IDFV for User X within its own apps on User X‘s phone: it could use this to store engagement and monetization data for User X across all three of its apps.

The IDFV cannot be used for acquisition attribution, but it can be used to great effect for cross promotion, which might provide a competitive advantage to developers that oversee portfolios of apps that feature large MAU. And the IDFV might become an important vector of M&A: a developer might not be able to transparently acquire high-value users, but it can acquire developers with large but declining user bases and cross promote those users across its existing portfolio on the basis of monetization history. I discussed the power of an intelligent cross promotion mechanism for developers with large portfolios in my 2016 GDC presentation about my efforts in building an internal ad network at Rovio.

What’s the net impact?

More important than how the privacy changes to iOS announced at WWDC 2020 impact the mobile ad tech ecosystem is how they impact consumers. My personal opinion here is that an app-level tracking opt in mechanism provides users with an even more granular level of control over their personal data, and in that respect, this is a welcome and positive change to the ecosystem.

I think the net impact of these privacy changes to advertisers is neutral to slightly negative. Advertisers won’t have attribution transparency at the user level, but as I argued in the beginning of this piece and in Media mix models are the future of mobile advertising, that transparency in the current paradigm is, to a large degree, a facade: last-click attribution provides advertisers with the veneer of control and measurability, but the reality is that the pool of users swirling throughout the overlapping fields of vision of the major ad networks has rendered value attribution impossible. And Google and Facebook had an unfair advantage in poaching the last click for most ad interactions, anyway.

Many sophisticated advertisers have been moving towards top-down, macro-level incrementality models over the past 12-24 months. The loss of device-level identifiers will hasten that transition out of necessity, but that doesn’t also mean it’s not the most prudent approach to advertising. Fast forwarding two or three years, I don’t think the mobile advertising ecosystem — from an advertiser’s perspective — looks meaningfully different now than it would have if Apple had announced business as usual for the IDFA at WWDC 2020. This trajectory had already been established.

Obviously the impact of tracking opt-in on much of the mobile ad tech terrain will be more severe, and the outlook for companies residing in those outposts is not as rosy.

Caveat lector

Apple’s privacy announcement from WWDC 2020 is only one week old, and the documentation they have released about the updates coming to SKAdNetwork are, in many places, incomplete or vague. The above represents my best guess at what will happen to the mobile advertising ecosystem, which is complex and multi-faceted, over the next 6-24 months. My opinion has been informed by exhaustive study of Apple’s documentation and through talking to a large number of people across the ecosystem, but my perspective is limited by the amount of information available. I believe that I approach this topic objectively and without any inherent bias, but readers should judge that for themselves.

Thank you

I have gained very valuable perspective around the changes coming to iOS 14 by speaking with people over the past few months whose opinions and insight I value greatly. Thank you to: Gadi Eliashiv, Nebojsa Radovic, Thomas Petit, David Barnard, David Philippson, Dick Filippini, Maor Sadra, and John Koetsier.

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Apocalypse Now: per WWDC, the IDFA is dead https://mobiledevmemo.com/apocalypse-now-per-wwdc-the-idfa-is-dead/ https://mobiledevmemo.com/apocalypse-now-per-wwdc-the-idfa-is-dead/#respond Mon, 22 Jun 2020 22:07:00 +0000 https://mobiledevmemo.com/?p=29977

At Apple’s WWDC event yesterday, a number of new privacy features were unveiled that will be rolled out to iOS 14 later this year. Among those — and most relevant to direct response marketers — is the AppTrackingTransparency framework, which requires an app to explicitly request a user’s permission before accessing their device’s IDFA, or unique mobile advertising identifier. If the user declines to grant an app access to the device IDFA, the user will appear to that app as if they have Limit Ad Tracking turned on:

Additionally, iOS 14 will introduce a “privacy dashboard” of sorts that will allow users to understand the information that various apps are tracking for them. Presumably, users will be able to revoke access to this data from the privacy dashboard, even if those apps had been previously given access to it:

And finally, Apple has introduced changes to SKAdNetwork, Apple’s app install attribution API that I wrote about back in 2018, that will make it possible to determine from which app a specific install campaign delivered a user.

These changes represent a seismic shift in the mobile advertising ecosystem. Mobile advertising, and specifically app install advertising, will fundamentally change with iOS 14.

In Apocalypse Soon, I predicted that Apple would announce at WWDC 2020 that LAT would be turned on for all users by default, and that a Certified Mobile Advertising Partners program would emerge that allowed advertising platforms to participate in a direct attribution system via SKAdNetwork. From the article:

At this point, advertisers begin to get a clearer picture of the future of mobile measurement and the severity of the changes being instituted. If attribution is handled directly from iTunes and doesn’t include user device identifiers, then the impending new reality of advertising dictates:

– ROAS and CPE campaigns will only be possible via the SANs that are able to do any form of fingerprinting via their proprietary SDK data and the revenue data they collect;

– Cost Per Install values for campaigns from the networks in Apple’s Ad Partners program are calculable, but building effective ROAS models for these campaigns will be difficult, if not impossible. Without being able to attribute revenue to campaigns (because all IDFAs are zeroed, and SKAdNetwork transmits no identifiable user information), the traffic sources of monetizing users are unknowable;

– Most of the infrastructure currently supporting mobile advertising will soon become obsolete.

In Why is Apple Building an Ad Network?, I posited that Apple’s motivation in making the changes I predicted in Apocalypse Soon could be to capture some of the predicted $240BN market for mobile advertising in 2020 by giving itself a privileged position relative to other ad platforms if device-level tracking was no longer possible:

When it launched, the general sentiment from advertisers toward ASA was that it would be a minor component of overall traffic and, in some ways, serve as a tax on App Store discovery (advertisers use ASA to bid against their own keywords so that competitors can’t poach organic installs). I rarely see the advertisers I work with spending more than 5-8% of their overall budget on ASA given its limited reach, but that is changing with these new placements. If Apple grows ASA into a bonafide ad network, and especially if Apple does deprecate its advertising identifier, Apple could propel ASA into the top echelon of mobile app install advertising platforms.

And in Media mix models are the future of mobile advertising, I explained why deterministic app install attribution as a paradigm needs to be replaced by probabilistic models that don’t require direct, per-install measurement:

The notion that measurability is exclusively a function of attributable clicks has been evaporating for years, hastening for the aforementioned reasons, and yet user acquisition teams have persisted. The demise of advertising IDs won’t precipitate the demise of user acquisition on mobile, since the deprecation of advertising IDs will simply take a trend that already exists — of decreased reliance on click attribution in a shift toward more holistic, macro-level measurement — to its logical conclusion.

These changes are happening now. It seems unrealistic to think that more than 10% or so of users would opt into being tracked via IDFA, even if (as per Apple’s documentation) advertisers have the ability to craft the message that is exposed in the opt-in dialogue box.

With this change, Apple effectively kills the IDFA and pushes app attribution to SKAdNetwork. Advertisers must reckon with the consequences of those changes for their business.

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Three arguments against Apple anti-trust accusations https://mobiledevmemo.com/three-arguments-against-apple-anti-trust-accusations/ https://mobiledevmemo.com/three-arguments-against-apple-anti-trust-accusations/#respond Mon, 22 Jun 2020 13:03:50 +0000 https://mobiledevmemo.com/?p=29958

Apple’s App Store approval policies were placed under a public microscope last week when the executive team at Basecamp, the web application development company behind the eponymously-named team productivity suite, revealed that Apple had rejected updates to their recently-launched consumer email product called Hey.

In a series of tweets, the CTO of Basecamp laid out the issue:

  • Users must create a Hey account before they are able to meaningfully use the Hey app: upon opening the app, a user is presented with only a login form, meaning that no functionality is available to users* unless they have already registered an account on the Hey.com website;
  • Hey does not offer in-app purchases (IAPs). A Hey account can only be paid for on the Hey.com website;
  • Other email clients are available on the App Store that likewise require external accounts and don’t offer IAPs;

The reaction to the tweet storm was mostly fierce criticism of Apple, with objections falling across three general themes:

  1. Apple should enforce its App Store terms consistently and not selectively sanction specific apps;
  2. The 30% platform fee that Apple charges is too high and should be reduced;
  3. Apple should not be in a position to block any app from being used on an iOS device.

Outspoken criticism of Apple’s App Store policies tends to prevail in these types of debates from a very vocal minority of developers that feel aggrieved. The problem with claiming that Apple enforces App Store rules inconsistently is that it’s difficult to know how Apple classifies certain apps that appear to flaunt those rules, and what logic it uses in doing so. For instance, Apple exempts “Reader” apps from the rule that apps must feature functionality upon initial open without a pre-existing paid account, but it also classifies Netflix and Spotify as “Reader” apps, which isn’t necessarily the core use case one might associate with those products.

Nevertheless, Apple does seem to have a thoughtful — albeit opaque — rule system that is uniformly applied, as explained in this article that dissects a recent interview with Apple Marketing VP Phil Schiller.

As regards the second and third themes that dominate criticism of Apple, the 30% platform fee being “too high” is completely subjective: obviously there is no “right” or “appropriate” fee that a platform can charge its developers. And the idea that the App Store should not be a closed ecosystem is simply misguided; Apple’s control over the App Store ecosystem (exercised through app review and its ability to reject apps) is unambiguously good for consumers. While the app review process can certainly be frustrating, ultimately consumers care most about safety and quality, which the process facilitates.

One galling accusation that surfaces incessantly in these developer complaints is that of Apple as a monopoly engaging in anti-competitive behavior that should be remedied by anti-trust law. I believe the term monopoly is being abused when applied to Apple, as I discuss in this podcast with developer advocate David Barnard and in this article. In this post, I intend to refute the three most common arguments made when accusing Apple of having monopoly power: I will do my best to present Steel Man interpretations of these points — or generous, good-faith representations that capture their true intent (a Steel Man argument is the opposite of a Straw Man argument).

Apple’s 30% platform fee is directly born by and is thus harmful to consumers.

It’s important in this discussion to distinguish between an economic monopoly, which is the sole provider of a good, and monopoly power, which is the ability of a firm to set prices so as to capture outsized, supracompetitive profits on the basis of its control of the market in a way that harms the consumer. Nobel Prize-winning economist Joseph Stigler describes the economic argument against monopolies as:

Rather, the purely “economic” case against monopoly is that it reduces aggregate economic welfare (as opposed to simply making some people worse off and others better off by an equal amount). When the monopolist raises prices above the competitive level in order to reap his monopoly profits, customers buy less of the product, less is produced, and society as a whole is worse off. In short, monopoly reduces society’s income.

Note that there is a distinction between the economic and legal definitions of the term monopoly: the legal definition, in the US, is enshrined in the collective anti-trust legislation of the Sherman Act, the Clayton Act, and the Federal Trade Commission Act. The US anti-trust law does not treat monopolies as de facto illegal, but rather classifies their behaviors as illegal when those behaviors are anti-competitive measures taken by a firm or when those behaviors willfully result in anti-competitive market conditions.

Focusing specifically on consumer harm through pricing: can Apple “charge prices above what they would be with competition” with the App Store, as per the definition from above? If a competing app store existed on iOS, would app prices be different than what they are now?

I argue that they would not be: app publishers sell digital goods with no marginal cost of production or distribution. If an app developer sells its app for some price, or prices its app at $0 and sells digital goods within the app for some set of prices, those prices are determined by consumer demand — the developer is incentivized to maximize revenue by setting prices at whatever level produces the most absolute revenue.

The 30% platform fee that the developer pays to Apple doesn’t impact the underlying prices attached to apps and digital goods. The consumer doesn’t feel the platform fee because the market prices of apps and digital goods would not correct by 30% if the App Store platform fee was abolished — rather, developers would simply retain that revenue, because market forces still control price setting.

This would be different if the platform fee was a fixed price — say, $0.99 per sale, regardless of the cost of the app or in-app digital good. If this were the case, then developers would have to bake this price into their products just to clear that absolute cost hurdle and make money on a sale, and thus the platform fee would be passed on to consumers. But because the platform fee is a percentage of revenue, developers are incentivized to set prices at whatever level maximizes total revenue, which would still be the case if the platform fee was done away with.

Apple is a monopoly because the App Store is the only means by which a developer can distribute apps to iOS users.

There are a number of forces operating in parallel in the case of the App Store that muddy the picture from an anti-trust perspective related to existing case law:

  1. Apple operates a digital storefront, or marketplace, that interfaces with both consumers (iPhone and iPad users that purchase developer content from the App Store) and merchants (developers that sell content directly to consumers via the App Store);
  2. Each party in the three-party system has a direct relationship with the other two parties. Merchants sell their apps and digital goods directly to users; if this weren’t true, then developers wouldn’t engage in direct-response marketing to users, and developers wouldn’t directly handle eg. customer service requests;
  3. iPhone and iPad users are consumers of Apple’s hardware and software products, and Apple publishes apps directly to its own App Store (and also pre-loads some of its apps on its own devices);
  4. The vast majority of apps in the App Store are available for free and feature digital goods that can be purchased (in-app purchases, or IAPs).

As I described in Is Apple a monopoly because of its 30% App Store fee?, the US Supreme Court case of Illinois Brick prohibits anti-trust claims by indirect purchasers (this defense was challenged in a suit against Apple and the Supreme Court determined that an anti-trust case could be leveled, and that case is ongoing). Putting aside the legal precedent, I believe that it’s important with this particular accusation to consider what exactly an “app” is.

If the accusation was rephrased as, “Apple is a monopoly because the App Store is the only means by which a developer can distribute content to iOS users,” then it’d be dismissed as patently false: iOS users can freely consume content via the web or bluetooth or wifi in a way that is not at all gated by the App Store. An “app” within the context of iOS is just content that is saved in the .ipa file format for upload to the App Store.

Modern tools like Unity3D, a cross-platform game development engine, and React Native, a cross-platform app development framework, allow developers to build software products once and publish them to many platforms, including, in some cases, the web. If a developer builds a software product, publishes it both to the mobile web and the App Store by exporting separate files, is the App Store really the only means by which that developer can reach consumers? Applying the “app” label here only to products that are published via the App Store makes the initial statement tautologically true but not meaningful.

This website is not an app, yet 30% of the people that read this website do so on their smartphones. If I were to re-build this site in React, export it as an .ipa file, and publish that file to the App Store, has access suddenly been monopolized by Apple? What about a game developer that publishes to both Google Play and the App Store simultaneously from the same core Unity3D file: is access to the game now monopolized by Apple?

And, again, the welfare of the consumer has to be considered (if not prioritized) when applying the anti-trust logic. Epic Games famously sidestepped the Google Play store with Fortnite, opting to distribute the game on Android via a stand-alone download, and that launch was plagued with fraud and scams. Epic ultimately opted to publish Fortnite to the Google Play store.

Apple engages in rent seeking by charging a 30% platform fee.

Economic rent in the context of neoclassical economics refers to returns taken on some resource above opportunity cost that derive from ownership and not competitive advantage. Economic rent is usually the result of scarcity and an imbalance between supply and demand: the test for whether profit is the result of competitive advantage or economic rent is whether the owner of the resource could make just as much money renting out the asset as it does using it to produce some service or good. Rent seeking is any behavior that seeks to optimize for economic rent.

Apple provides tremendous value to developers via the App Store: payments processing, editorial curation and discovery, fraud prevention and general security, etc. But perhaps the most valuable benefit that the App Store provides to developers is a frictionless, straightforward path for users to engage with apps. The combination of these things — the tools that app developers have at their disposal, plus access to a very large user base — is self-reinforcing and creates value. More users download apps than otherwise would because that process is simple and straightforward . And more developers publish apps than otherwise would because that process is simple and straightforward. Apple’s unique expertise in designing simple, easy-to-use and consumer friendly experiences has been a significant factor behind the success of the mobile app paradigm. The App Store is absolutely economically constructive

For the App Store’s 30% platform to qualify as rent seeking, it would have to be true that Apple could simply rent out the App Store as a business and make just as much money. That is, another company pays some yearly fee to Apple in exchange for operating the app store and earning the platform fee as income, and Apple is indifferent to either case. This is clearly farcical.

Apple is one of the most consumer-obsessed companies in the world, with a fanatical devotion to consumer happiness, ease of use, and aesthetics. The App Store is valuable because Apple runs it: Apple’s unique domain expertise with respect to elegant simplicity allowed the iOS ecosystem to flourish. Whether the 30% platform fee is too high is a separate topic, and perhaps the size of the fee needs revisiting. But the notion that the 30% platform fee represents non-productive economic rent simply doesn’t hold up to intellectual scrutiny.

Conclusion

I have only focused on the three specific allegations I see made most commonly against Apple as an anti-trust violator; others exist.

I believe Apple is soon to face a reckoning: does it accept that the scale and success achieved by the App Store requires a more evolved perspective than the company possessed when it was launched in 2008? I do believe that the opacity with which Apple approaches terms of service enforcement decisions needs to be addressed: Apple should be more transparent with developers around the circumstances under which certain behaviors, especially those related to out-of-app pricing, are acceptable.

But I do not believe that Apple has violated anti-trust law; I do not believe that Apple’s policies harm consumers; and I do not believe that charging a platform fee is morally or ethically indefensible.

*Update Monday, June 22nd: after this post was published, Basecamp revealed that it had implemented a free tier in Hey and subsequently its app update had been approved by Apple.

Photo by BP Miller on Unsplash

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Media mix models are the future of mobile advertising https://mobiledevmemo.com/media-mix-models-are-the-future-of-mobile-advertising/ https://mobiledevmemo.com/media-mix-models-are-the-future-of-mobile-advertising/#respond Mon, 15 Jun 2020 05:30:00 +0000 https://mobiledevmemo.com/?p=29889

A recent theme that has seemed to reach critical mass within the milieu of the mobile ecosystem is that performance advertising on mobile is on an extinction course, with the meteor impact event being Apple’s presumed inevitable deprecation of its proprietary advertising identifier.

It’s certainly true that the deprecation of mobile advertising IDs would precipitate fundamental change within the mobile ecosystem — and do I believe that deprecation will absolutely happen, although COVID may have granted mobile advertising IDs a stay of execution. But mobile performance marketing will not disappear with the eradication of advertising IDs — its shape will merely change, but not as substantially as some might believe.

The truth is that mobile advertising identifiers are really just a crutch that many performance marketing teams lean on to feel confident in their work. Limit Ad Tracking, which sets a device’s advertising ID to a sequence of zeroes and was introduced by Apple in 2016, already renders roughly one third of iOS users totally anonymous: un-targetable and un-trackable (Google introduced a similar mechanic in the same year).

And perhaps even more confounding but less frequently addressed is the fact that every advertising platform effectively lords over the same device graph — the only difference between advertising platforms is their bank of historical monetization data per device. Advertisers think they are diversifying their traffic intake by operating campaigns across multiple channels, but in reality they’re just reaching the same users in the same apps via different inventory. Running multiple campaigns across different networks is akin to setting many fishing poles at various points on a river: if you don’t catch a fish at one point, you may still catch it downstream.

The mechanic that allows advertisers to believe they’re navigating this system efficiently is last-click attribution: the fishing pole that yielded the fish was clearly better suited to catching that particular class of fish! But in reality, Facebook and Google are more or less randomly rewarded with the vast majority of fish upstream.

Last-click attribution provides advertisers with a veneer of control, like a security blanket: budget is spent and attributed and everyone feels confident that they are systematically driving profitable revenue through performance user acquisition. But this measurability is really an illusion: some percentage of spend obviously produces incremental revenue, but the way that users swirl around within the lines of sight of various ad platforms means that, once an advertiser extends their spend beyond a single channels, they are guaranteed to be losing money to redundant, superfluous spend.

So the deprecation of mobile advertising IDs will really just accelerate a trend that has existed for the past few years: despite clear attribution and unit economic metrics, advertisers can’t trust that their spend on any given channel is totally incremental, and thus their measurement is really only valid at the broadest level of granularity. This macro-level framework is how many traditional companies evaluate their spend across advertising media, like television and radio, that is difficult to attribute.

The mobile ecosystem — with Limit Ad Tracking; the consolidation of inventory across just a handful of large advertising platforms; the wholesale shift to event-driven, algorithmically optimized campaigns by Facebook and Google; and the maturation of mobile and attendant decrease in smartphone sales — has been drifting toward such an environment for many years. Back in 2017, in Mobile’s post-attribution era, I wrote:

And this is where the concept of attribution on mobile begins to whither as marketers diversify away from direct response. TV, influencer, out of home, et al can all be integrated into performance marketing machinery with an eye toward profit at the unit level. But this is difficult; it requires building a top-down model of a company’s marketing schema that incorporates 1) uncertainty and 2) statistical robustness. And these channels aren’t attributable: the outcome of these campaigns, while capable of being evaluated broadly, can’t be specifically measured at the level of the individual user.

The notion that measurability is exclusively a function of attributable clicks has been evaporating for years, hastening for the aforementioned reasons, and yet user acquisition teams have persisted. The demise of advertising IDs won’t precipitate the demise of user acquisition on mobile, since the deprecation of advertising IDs will simply take a trend that already exists — of decreased reliance on click attribution in a shift toward more holistic, macro-level measurement — to its logical conclusion.

The structure and size of user acquisition teams has fundamentally changed as this trend has played out (see: The future of mobile growth teams, Are mobile marketing teams shrinking?), but user acquisition as a function is no less important than it was in previous stages of the history of the app economy — in fact, mobile ad spend is on a tremendous growth trajectory that isn’t poised to abate any time soon. User acquisition is therefore as critical to success on mobile as it has ever been — the question then is what form that function takes as measurement shifts.

Media Mix Modeling

Media Mix Modeling is an advertising measurement methodology that attempts to quantify the incremental business impact of spend on any given channel within the context of a multi-channel advertising environment. Media mix modeling is often referred to as a “top down” approach to measurement (versus “bottoms up,” click-based attribution) because it evaluates advertising performance through the correlation between broad inputs: media spend per channel and some conversion metric (usually sales). A media mix model often also takes time-based, economic, and exogenous factors into account, such as competitor spend around a new launch or seasonality.

Media mix models have been in vogue with large CPG advertisers for a few years: this Harvard Business Review piece from 2013 describes the media-mix-model-centric measurement system as “Analytics 2.0.”

This paper, titled Challenges And Opportunities In Media Mix Modeling and written by two data scientists at Google, does a very good job of explaining the underlying statistical methods used in media mix models, along with some of the challenges in constructing them.

One such challenge for mobile marketers operating across direct response channels is the inherent selection bias of utilizing ad spend data from targeted campaigns: the underlying demand is not captured in the model but influences both ad spend and conversions (this is known as a confounding variable). This issue is perhaps more acute with channels like Facebook and Google UAC, where targeting is largely controlled by the platform and optimized automatically over time. A detailed review of the mechanics of media mix models is beyond the scope of this article, but this white paper (with code) and this master’s thesis are very clear and helpful resources for learning about media mix modeling. This much more technical paper from Google introduces Bayesian techniques for capturing lagged response and diminishing returns in a media mix model.

A media mix model solution is writ large more relevant for advertisers that use a broad mix of offline and online marketing channels, such as a combination of direct mail, television, radio, paid search, etc. Most app advertisers and, more broadly, mobile advertisers allocate the majority of their budget to direct response channels, which have been seen as explicitly, precisely attributable. It’s clear that this is no longer the case: not only because of the impending removal of mobile advertising IDs, but for the reasons outlined earlier in the article. Direct response advertising is very much approaching a measurability environment like those of television, radio, out of home, etc.: bottoms-up measurement is an anachronistic performance assessment methodology that can’t effectively guide advertising incrementality-minded spend decisions in the contemporary environment. The veneer of credibility of bottoms-up, attribution-centric measurement has already cracked; it will completely disintegrate when the mobile advertising IDs vanish.

A quick note here about ad fraud. Ad fraud on mobile is a mostly exaggerated problem, the impact of which is overstated by mobile attribution companies to allow them to diversify away from their core business, which was always on a collision path with commodification. For the vast majority of mobile advertisers, which allocate their budgets primarily to the Duopoly and a handful of large, credible advertising platforms, ad fraud amounts to a trivial amount of wasted spend — certainly not enough to justify the use of expensive fraud analytics tools.

Most cases of ad fraud are really just cases of lack of incrementality: an advertiser is spending money on multiple channels to chase one core group of people, but because of last-click attribution and the disproportionate purview that self-attributing networks have, the advertiser can’t reconcile spend with acquisitions or make the case for incremental revenue through its ad spend. Marketing managers frame this incrementality problem as a fraud problem because it allows them to blame their lack of effectiveness on a nefarious bogeyman. It is telling that the most high-profile case of mobile ad fraud is Uber’s, which is more of an example of fraud powered by social engineering and manipulation than it is an example of systemic fraud powered by malicious technology.

The robots are coming

Marketing automation is often cited as one of the primary reasons that mobile marketing teams are shrinking: algorithmically-optimized ad platforms like Facebook’s and Google’s UAC can be scaled without much manual effort, and the large, matrixed marketing organizations of the 2014-2016 era are simply outdated. This was apparent in many of the high-profile COVID-related layoffs that took place in April and May at consumer technology companies: marketing teams were disproportionately targeted with the layoffs at Airbnb, Uber, and Lyft.

It is true that marketing automation is displacing media buyers: much of the work of campaign management has been ingested by the ad platforms, and the media buying and analysis work that remains beyond that is being addressed by tools from ad tech companies.

But it’s a mistake to confuse media buying with marketing. Modern marketing is incredibly technical and data-driven: all of the infrastructure and analytical tools that support media spend are only becoming more core to the broader marketing function for large advertisers. Yes, marketing teams are shedding media buyers as platforms subsume campaign management responsibility, but merely automating away the work of optimizing campaigns doesn’t alleviate the need for incrementality measurement.

In fact, incrementality measurement arguably becomes more important as automation proliferates because automated systems make it easier to onboard and operate new channels and media sources. So as media buying teams shrink, more and more resources are being allocated to the tools and infrastructure that power and measure the automated marketing systems. So long as digital products exist, companies will want to grow them. User acquisition as a function is not going to “die”; the composition of user acquisition teams is changing as the need for media buyers diminishes and the need for analysts, engineers, and data scientists increases.

Media mix modeling sits at the intersection of these trends: click-based mobile advertising attribution is already insufficient for measurement, and it very well may be rendered completely obsolete with the deprecation of mobile advertising identifiers. Media mix modeling allows advertisers to map spend to revenue on the basis of incremental value and diminishing returns: it sidesteps completely the complications caused by last-click attribution, limit ad tracking, overlap of audience, etc.

The mobile marketing team of the future exists to support a media mix model, which sits at the center of a Level 4-automated platform that directs spend across channels via predicted incremental contribution. This user acquisition team, staffed primarily by engineers and data scientists, looks very different from a contemporary team, but the functional focus and output is the same.

Photo by Franck V. on Unsplash

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How to launch a Top 100 Grossing app https://mobiledevmemo.com/how-to-launch-a-top-100-grossing-app/ https://mobiledevmemo.com/how-to-launch-a-top-100-grossing-app/#respond Mon, 08 Jun 2020 05:30:00 +0000 https://mobiledevmemo.com/?p=29842

Quibi, the streaming video entertainment app helmed by Jeffrey Katzenberg and Meg Whitman, will likely serve as a case study for some time in how not to launch a mobile product. Despite having raised $1.8BN in funding, as well as securing $150MM in advertising commitments ahead of launch (some of which are now being renegotiated), Quibi’s debut was muted: the app only saw around 1.7MM installs over the course of its launch week, with Jeffrey Katzenberg claiming that the app had roughly 1.3MM DAU in early May.

The mobile app economy, once considered something of a commercial Wild West where good ideas coupled with capable execution could be turned into big businesses by scrappy entrepreneurs, is a mature and hyper-competitive marketplace. User base growth for successful mobile apps is almost exclusively driven by a flywheel of performance marketing and deep monetization expertise: resources matter, and if Quibi, a company with abundant resources to invest into growth, couldn’t manage to launch its app into a sustainable Top 100 Grossing rank, it’s only natural to question how any company could.

And indeed, it is rare that an app is able to launch into a Top 100 Grossing position — I calculated the average age of apps that spent any time within the Top 100 Grossing position over the past 90 days as 1,452 days for iOS and 1,648 days for Android. Most Top Grossing apps are years old: incumbents dominate the Top Grossing charts and have an advantage with respect to the flywheel described above.

Yet new apps do launch into the Top Grossing charts. In the past 90 days, eight apps have launched that have spent any time in a Top 100 Grossing ranking in the US across both iOS and Android (only the ‘Overall’ Grossing chart is used here, not category charts like Games or Health and Fitness):

Some interesting things to note about these apps:

  1. These eight apps are really just five: most of the apps that launch into a Top 100 Grossing position on one platform also reach a Top 100 Grossing position on the other;
  2. G-TV is a Chinese-language social media app that was released on April 15th and rocketed up the US Top Grossing charts immediately after release and almost as quickly disappeared. Saraca Media Group, the company that publishes G-TV, is owned by a Chinese billionaire;
  3. Aside from G-TV, all of the Top 100 Grossing launches are games;
  4. Of the games that launched to Top 100 Grossing positions, Glu publishes two: Disney’s Sorcerer’s Arena and MLB Tap Sports Baseball;
  5. All of the developers behind the games that launched into Top 100 Grossing positions are public companies, with Netmarble headquartered in South Korea, SQUARE ENIX headquartered in Japan, and Glu headquartered in the United States. All of these companies operate substantial mobile portfolios — none of these apps is a first foray into mobile for these companies.

The fact that just four companies were able to launch into Top 100 Grossing positions over the past 90 days is striking: the upper echelon of the app economy is incredibly difficult to penetrate.

Of course, setting a standard of Top 100 Grossing is arbitrary: the market is so large that most developers would be happy with an app that reaches a Top 500 Grossing position (in the US alone, a Top 500 Grossing app might generate between $10-30k per day). But a Top 100 Grossing position is a commonly held aspiration for mobile app developers: it’s a significant and widely respected milestone.

So how does a company launch an app into a Top 100 Grossing position? The contrast between the companies that have most recently done it and companies, like Quibi, that tried and failed, seems to highlight a need for deep mobile expertise: not just in advertising but in monetization, audience development, pricing, product marketing, etc.

As of this writing, Glu’s market cap is $1.3BN — Quibi raised $1.8BN. Money is a necessary but insufficient pre-requisite for success on mobile: the companies that are consistently able to launch titles into Top 100 Grossing rankings have more than just money, they have deep domain expertise around mobile publishing and products that meet a large and monetizable use case. This sort of internalized expertise around the specifics of mobile publishing is important; the differences between mobile and all other consumer technology form factors are meaningful and substantive.

Photo by David Carboni on Unsplash

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Monetization data is critical in a mobile game soft launch https://mobiledevmemo.com/monetization-data-is-critical-in-a-mobile-game-soft-launch/ https://mobiledevmemo.com/monetization-data-is-critical-in-a-mobile-game-soft-launch/#respond Wed, 03 Jun 2020 05:30:00 +0000 https://mobiledevmemo.com/?p=29799

Conventional wisdom has long held that monetization data should play little if any role in the evaluation of a mobile game’s soft launch, with retention data providing the primary insight into whether a game is viable. The crux of the argument supporting retention’s primacy is that retained users can ultimately be exposed to any monetization mechanic the developer can dream up — retention is at its heart a measure of delight, and it should be held above other metrics as a guiding light with respect to player intent.

This line of reasoning was certainly popular in 2014, when I presented an overview of the approach that my team took in launching the swipe-3 puzzle game, Jelly Splash, at GDC. But I believe that the market — both for mobile games and for mobile advertising — has changed sufficiently since that time to upend this logic.

Now, I believe that any singular metric on its own, be it marketing-centric (eg. CPI, CTR, IPM, etc.) or game-centric (eg. ARPDAU, DX retention, etc.), is too narrowly scoped to be useful in evaluating a soft launch. Monetization is now a critical assessment feature of a soft launch evaluation, because monetization signaling has become such a core component of product growth, given that:

  1. Facebook and Google (and Snapchat, TikTok, and soon, Pinterest) are now dominated by algorithmic, event- and value-optimized campaign spend;
  2. Most successful games are disproportionately dependent on Facebook and Google for user base growth.

The retention-first soft launch measurement strategy was conceived of in a period in which just one campaign strategy existed for scaling an app via paid advertising: Mobile App Install (MAI), which optimizes merely for the lowest cost of an install given some targeting scope. But very few successful mobile games (outside of the hypercasual genre) exclusively utilize MAI campaigns for growth. Most successful games rely on some combination of Facebook’s AEO and VO campaigns and Google’s in-app event and tROAS UAC campaigns for the majority of their new users, with the distribution of spend between these campaign strategies being dependent on the type of game: for example, games with very long-tail LTV distributions tend to be able to scale VO campaigns more easily than those with LTV distributions tightly concentrated around the mean value.

When advertising campaigns are only optimized for install price, then the LTV / CPI ratio is transparent and directly connected to marketing metrics: the team can impute some level of ARPDAU to the unfinished prototype, use retention as a measure of overall quality, and make some assumption-based adjustments to CPI to determine if a game can be scaled in Tier 1 geographies based on soft launch performance.

But if a developer accepts that, if its game is successful, it will be spending a majority of its ad spend on event-driven Facebook and Google UAC campaigns, then it follows that it can’t know whether the game is successful until it has validated that those events provide for scale on those channels. And ultimately, the event that delivers the most insight — and the one most commonly used for scaled event-optimized campaigns — is the purchase.

The only two questions that an advertiser should endeavor to answer in a soft launch are:

  1. Scale: To what size of a user base can the game be scaled?
  2. Advertising payback: Over what period is ROAS (return on ad spend) realized, and is this acceptable to the company given cash flow demands?

In this new algorithmic, event-oriented campaign optimization environment, those questions are answered via ROAS based on event signals that must fundamentally be tied to monetization, and thus monetization is a critical feature of a modern soft launch.

Without monetization data, the advertiser is unable to know to what scale its game can possibly reach: if only MAI campaigns are being used in soft launch, the traffic composition of the soft launch isn’t representative of what the developer will see in a globally-live setting. And if monetization signals aren’t being sent for AEO, VO, or Google UAC campaigns, then the developer doesn’t know whether or how its campaigns will scale in global launch. And, of course, a payback window is impossible to assess absent monetization data. For these reasons, monetization is now a critical factor of a mobile game soft launch.

Visualizations taken from the recently-released Modern Mobile Marketing at Scale online course

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Is AR a failed consumer category or a successful mobile gaming genre? https://mobiledevmemo.com/is-ar-a-failed-consumer-category-or-a-successful-mobile-gaming-genre/ https://mobiledevmemo.com/is-ar-a-failed-consumer-category-or-a-successful-mobile-gaming-genre/#respond Mon, 01 Jun 2020 05:30:00 +0000 https://mobiledevmemo.com/?p=29770

Augmented Reality (often grouped with Virtual Reality, as AR/VR) has been championed as the ascendant consumer tech category since Niantic’s Pokemon Go was released in 2016. Just this January, Tim Cook celebrated augmented reality as “the next big thing,” citing its broad use cases in everyday life, such as in getting specific instructions from an AR app when changing the oil in a car. Cook’s declaration was widely interpreted as a dig at Facebook, which has invested massively into Virtual Reality with its Oculus product, but regardless, Apple has devoted a tremendous amount of resources into developer resources for building AR experiences, such as its ARKit API launched with iOS 11.

AR’s applications have been theorized and implemented for almost every form of content imaginable, from home decor to theme parks to guided tours to clothing and on. But despite the myriad ways in which AR can be applied to almost any experience, only really one has evolved into a meaningful business: games, and specifically, Pokemon Go.

Pokemon Go is a wildly successful mobile AR game, having generated roughly $3BN in lifetime revenues as of the end of last year. But Pokemon Go stands alone as a successful, mass-market, AR-centric consumer product, which invites the question: has AR been a disappointment as a consumer category, or has it been a success as a genre within mobile gaming?

Answering this requires first establishing a definition for “consumer category.” To my mind, a consumer category is a wholly unique format to which mass-market consumer products conform — instigated by some new technological innovation, hardware form factor, or business model — which spawns new consumer behaviors and which by definition features sub-categories. Mobile gaming itself is a consumer category, instigated by the proliferation of smartphones and the introduction (in the West) of the free-to-play business model. Online dating, messaging, ridesharing, real-time video chat, streaming video / music etc. are other examples of consumer categories.

New consumer categories tend to create hundreds of billions of dollars of value; the formation of categories is what venture capitalists base investment theses around, since most consumer tech is winner-takes-all, so the companies that “win” a new category often become extraordinarily valuable. Sub-categories tend to not be as valuable, since, by definition, they have smaller scope than whole categories.

If this seems like a distinction without a difference, consider the fact that AR has mostly underperformed as a category with just one significant product to speak of, whereas it’s by any measure a success as a genre (sub-category) within mobile gaming, still with just that one significant, mass-market product (Niantic’s Harry Potter AR game hasn’t done nearly as well as Pokemon Go).

Some might argue that AR is still in its infancy and that gaming tends to lead consumer tech innovation (a notion to which I wholeheartedly subscribe), but Pokemon Go become a massive cultural phenomenon from nearly its first day of launch in 2016 and there isn’t a single other AR game — much less app — that has approached its scale since. Compare this with the Battle Royale genre within mobile gaming, which conceived three billion-dollar franchises (Knives Out, PUBG, and Fortnite, although to be fair, these games aren’t mobile-only), all of which were released in 2017.

It’s interesting to consider AR through the lens of the category / sub-category division and within the context of the timeline of Pokemon Go’s success. Could AR, at some point, become a consumer category? Of course. But the fact that it hasn’t is not a terrible outcome: much in the way that Playrix owns the Puzzle & Decorate genre with Homescapes and Gardenscapes, having become a multi-billion dollar company on the success of those games, Niantic is still the undisputed leader of the AR gaming category with a valuation of many billions of dollars.

But it seems difficult to argue, nearly four years after Pokemon Go’s rise to cultural prominence, that AR is on the precipice of breaking out into a mass-market consumer category. Without any real traction outside of Pokemon Go, and with the amount of resources that have been applied by not only developers but of the mobile platform owners, which have aggressively invested into AR’s adoption, the path to category creation is unclear; that moment may have passed. Mobile gaming is a substantial enough consumer category — at $85BN in revenues in 2019, excluding China — to support many genres that themselves are worth billions of dollars.

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