Mobile Dev Memo https://mobiledevmemo.com Mobile advertising and freemium strategy. Mobile marketing, analytics, and monetization. Fri, 21 Jun 2019 20:52:45 +0000 en-US hourly 1 58872998 The LTV metric isn’t dead. Here’s why. https://mobiledevmemo.com/ltv-isnt-dead/ https://mobiledevmemo.com/ltv-isnt-dead/#respond Mon, 24 Jun 2019 05:30:35 +0000 https://mobiledevmemo.com/?p=26650 LTV is still a relevant and functional metric for marketers in 2019. This guest post was written by Kate Minogue, who heads the EMEA Marketing Science team for gaming at Facebook. Lifetime Value (LTV) — or more practically, Long-Term Value — should serve as a North Star for advertisers, both in helping them to understand […]

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LTV is still a relevant and functional metric for marketers in 2019.

This guest post was written by Kate Minogue, who heads the EMEA Marketing Science team for gaming at Facebook.

Lifetime Value (LTV) — or more practically, Long-Term Value — should serve as a North Star for advertisers, both in helping them to understand the ultimate quality of their users as well as in giving them the confidence and freedom to acquire them profitably.

In the context of mobile gaming, and particularly IAP-monetized games, we are all too aware of the rarity of a paying user. A report published by mobile attribution company AppsFlyer last year showed that only 3.8% of those that play Mobile Games ever become payers. So, in User Acquisition, teams are challenged with casting the net wide enough to find a large volume of players containing sufficient purchasers to fund acquisition. If we add a further restriction that this group need to pay back fully in the short term, we will unnecessarily limit ourselves to a much rarer event, potentially missing out on a large volume of valuable players.

The recent article, It’s time to retire the LTV metric, discusses the merits of moving away from LTV altogether as it doesn’t gel with the way most companies operate today: the LTV metric we are used to appears outdated. But I believe that the LTV metric is not the problem, but rather the many failings in how companies are using it today.

Let’s start with one very important fact up front — and my favorite quote — to put this in perspective:

We look to LTV models to solve the challenges of acquisition and payback and when they don’t work, we try to make them more and more sophisticated. But they are still models. And still wrong. We need to focus on the final word “useful.”

In working with a range of advertisers on their Marketing Science strategies, we at Facebook have seen that most of the challenges in using pLTV effectively fall either into the fundamental definition of LTV, the model choice / accuracy, or a lack of flexibility of the chosen model.

How can the simple act of defining LTV be a challenge?

When building a model that predicts LTV, the first task for a team to undertake is to define what they are predicting and the appropriate time horizon for that quantity. If this stage of the prediction process isn’t handled with sufficient care and attention, then it’s likely that the rest of the project will not go smoothly. Three factors feed into the definition of LTV that need to be agreed to both by the team building the model (that has access to all the data) and by the teams that plan to use the model:

  1. What is value?
  2. When do we predict?
  3. What period do we predict for? (What is our “Lifetime”?)

The value question is becoming more complex with the rise of games with ad or hybrid monetization models. Some advertisers include an organic coefficient (or k-factor) in their value definition to ensure the full impact of user acquisition can be measured. This will make sense for some games and not others, but either way it is no easy calculation. The goal at this stage is twofold:

  1. Ensuring that all relevant value sources are being accommodated by the LTV model as accurately as possible;
  2. Ensuring that the marketing teams that will refer to this KPI are aware of any assumptions that have been made in defining value so that they are prepared when making trade-offs (e.g. adding a blanket 20% to account for ad revenue contribution).

As for the length of the time horizon component of the model, I always give the very unsatisfactory answer of, “What makes the most sense for your business?”. Take serious caution in any case where an external party (having not seen your data) gives you guidance on the timeline over which you should make a prediction. The key takeaway here is know your own user value curves: a best practice here is to predict as early as possible and continuously update as and when more data becomes available. The first prediction the team uses ought to be as early, and accurate, as necessary to make a good business decision. Lifetime is unlikely to be “forever” (nor would this be useful) — instead we want a duration that makes sense for our customer retention, monetization and, as has been mentioned on MDM many times before, cash flow. The “Advertising Recoup Evolution” approach that was outlined previously by Eric Seufert can be useful if you have a robust understanding of the relationship between early payback and what your company needs to drive profit and growth as long as it is not arbitrary, short sighted, or unnecessarily restrictive.

Model choice is key — so do we need all the bells and whistles of the latest developments in machine learning?

Model choice is key but that does not mean more complicated always equates to better! Different algorithms have different strengths (transparency, stability, handling of extremes) so you want the one that fits your specific use case and priorities.

We see a lot of models that use predominantly macro features such as demographic, device, or acquisition channel — a concern with these is that some of these alone do not generalize as well as in-game behaviors when external factors (such as marketing) shift. Take your acquisition channels as an example: first and foremost these will be dependent on your attribution methodology (often another “wrong” model). Second, this as a model feature can be a very blunt tool if you have a lot of different campaigns on a given channel or you are making a lot of changes. Communication between Product, Marketing, and Data Science is critical to uncovering cyclical relationships between the variables we have chosen versus identifying those that truly represent an indicator of future value.

The reality is that a simple model may be perfect for you if it achieves what it has been built to do. Ideally this would be one model, one source of truth, for the company, but we at Facebook have seen cases where that model needs to differ based on the differing needs of your internal teams. Although your finance team may have different requirements from your monetization or marketing teams if the period of interest, granularity, or accuracy levels required are different, what matters is how confident you are in the accuracy of the model. That means more than checking the aggregated accuracy score (if I sum all my predictions and all of my actuals the difference is X%) at the point of model build and assuming it will take care of itself. It is important to monitor accuracy over time, know this accuracy for different cohorts of users, and know what that means for you when deciding the “winning” strategy and where to invest your money.

So, we built a really accurate model, are we done?

You expect your business, product, and players to change over time and your model needs to do the same. The owner of your LTV model needs to be close enough to these changes to either futureproof the model to handle some of these changes or else to know when a change is significant enough to prompt a refresh or complete rebuild.

The reality is that a simple model may be perfect for you if it achieves what it has been built to do.

Aside from these underlying changes, you also need to be conscious of whether your model acknowledges the non-acquisition marketing activities that your teams pour blood, sweat, tears (and more budget) into. If you are investing in brand loyalty or retention marketing, can your model adapt to the changes you strongly believe this delivers in customer LTV? Or are you still relying on the value your analytics team reported at the time of acquisition.

Okay, we’ve read this far — what is the silver bullet?

This should come as no surprise, but an inaccurate LTV model won’t be fixed by one magic variable you haven’t thought of building in. Your best chance of success with pLTV measurement is true collaboration and open communication between all teams. All teams that understand your customer and can have any impact on their journey should play a part in the design and implementation of these models. Disconnected teams, models built in isolation by people that don’t understand the business, or teams that don’t understand the models they’re using will all lead to poor decision making and, if that leads to the demise of LTV, then you have truly thrown out the champagne with the cork.

Kate Minogue heads up the Marketing Science team for Gaming, EMEA at Facebook, where her team works with some of the largest Gaming companies globally to advise on their Marketing effectiveness and data science strategies. Prior to Facebook, Kate worked in Data Science for 6 years focused on Marketing and Customer analytics across Online, Retail, Gaming and Financial Services.

Photo by William Hook on Unsplash

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Mobile advertising is exploding. Who is benefiting? https://mobiledevmemo.com/mobile-advertising-is-exploding-who-is-benefiting/ https://mobiledevmemo.com/mobile-advertising-is-exploding-who-is-benefiting/#respond Mon, 17 Jun 2019 05:00:29 +0000 https://mobiledevmemo.com/?p=26533 Mary Meeker released her latest Internet Trends report last week; as usual, it’s required reading. Meeker directed attention to the state of internet advertising early in the report with these two graphs: For anyone operating in mobile, the dynamics underlying these charts shouldn’t be a surprise. The mobile advertising market has been growing explosively for […]

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Mary Meeker released her latest Internet Trends report last week; as usual, it’s required reading. Meeker directed attention to the state of internet advertising early in the report with these two graphs:

For anyone operating in mobile, the dynamics underlying these charts shouldn’t be a surprise. The mobile advertising market has been growing explosively for the past few years, driven by factors that I outlined in a recent presentation, The State of the App Economy: 2019 and Beyond.

And, of course, that growth in mobile advertising revenues, which was made possible by the almost total penetration of smartphones, shifting behaviors towards video content consumption, increased access to cheap mobile data plans, and accommodations by the mobile platform operators (Apple and Google), means the companies generating those revenues are encouraged to invest money into infrastructure that makes that advertising ever more targeted and performant, benefiting advertisers and publishers alike in what I’ve called the second mobile cycle.

In a recent article, How large is the mobile gaming advertising market?, I made the case for why the mobile gaming advertising market could be worth $100BN per year. But there are numerous other verticals that exist solely because they can build massive audiences through effective mobile advertising; the boom in mobile advertising is really an artifact of the general mobile boom that supports that advertising.

And while advertisers are thrilled to have the opportunity to distribute their products with the kind of precision that modern mobile marketing tools afford, which advertising platforms are the beneficiaries of the growth showcased in the chart above? A third chart from Meeker’s presentation says one thing with the title but depicts another:

It’s true that the entirety of the “other” group of advertising platforms here is growing faster than Facebook and Google, but there’s nuance that is absent from the slide. Firstly, Amazon makes up the vast majority of that revenue:

And Amazon also makes up much of that growth: its “Other” sales category grew from $2.03BN in Q1 2018 to $2.710BN in 2019. That is a substantial amount of money — I believe Amazon is on track to become the third party in a Triumvirate, upsetting the Duopoly — but it’s still peanuts compared to Alphabet’s $30.7BN in Q1 advertising revenues (up 15% year-over-year) and Facebook’s $14.9BN in Q1 advertising revenues (up 26% year-over-year). Pull Amazon out of the “Other” category in the chart above and it’s clear that Amazon is an incipient threat to Facebook and Google but the vast majority of the spoils of the growth of internet advertising are being captured by Google and Facebook.

eMarketer pegs Facebook and Google’s collective share of the 2018 internet advertising market at roughly 57%, but that’s inclusive of desktop, with players like Bing, Yahoo!, and Oath still generating billions per year in advertising revenue. But mobile is the real growth sector, and back in 2016, the IAB — an advertising trade group — released a report in which they claimed that Facebook and Google collectively owned 89% of all growth in advertising revenue, meaning they own mobile. Has that really changed?

It doesn’t seem likely, looking at the numbers from above, and it’s unclear how it could change outside of government intervention or regulation. Facebook and Google have data and infrastructure that simply can’t be replicated by other players: when we say that internet advertising revenues are growing, we really mean that mobile advertising revenues are growing, and by that we really mean that Facebook and Google and Amazon advertising revenues are growing.

Photo by Kevin Grieve on Unsplash

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Would your business survive the elimination of the IDFA? https://mobiledevmemo.com/would-your-business-survive-the-elimination-of-the-idfa/ https://mobiledevmemo.com/would-your-business-survive-the-elimination-of-the-idfa/#respond Mon, 10 Jun 2019 05:30:01 +0000 https://mobiledevmemo.com/?p=26441 Apple made two announcements at its annual developers’ conference, WWDC, last Monday that caused mobile marketers to take pause. These were: Apple updated its iTunes app submission guidelines to declare that children’s apps (defined specifically here as apps submitted to the “Kids” category) can no longer include any app or advertising tracking; Apple revealed a […]

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Apple made two announcements at its annual developers’ conference, WWDC, last Monday that caused mobile marketers to take pause. These were:

  • Apple updated its iTunes app submission guidelines to declare that children’s apps (defined specifically here as apps submitted to the “Kids” category) can no longer include any app or advertising tracking;
  • Apple revealed a new single-sign on (SSO) product called Sign In with Apple. This product will allow users to sign into third-party services via a random email address that Apple associates with the user’s iOS account email address; in this way, the user’s real email address is hidden from third-party services, and communications are relayed between the service and the user via Apple. Apple noted in its submission guidelines update that integration of Apple’s SSO product will be required in all apps that offer third-party sign in.

Many commentators interpreted Apple’s SSO product as an attack on Google and Facebook, which both offer single-sign on products and use user emails as targeting features for advertising campaigns. For example, an app developer can create a lookalike audience on Facebook using its users’ email addresses to target similar users; if the only email addresses it has for users are the obfuscated Apple values, Facebook won’t be able to match email data with its internal user data to identify users.

But I think the impact of this consequence of Apple’s SSO product is exaggerated. Firstly, this will only apply to new users that register with Apple SSO, and Apple hasn’t indicated that it won’t also allow app developers to collect email addresses through other means (eg. asking them to provide a non-Apple SSO email address). Secondly, email isn’t the singular identifier that Facebook and Google really use to anchor data to individual people: those are the platform identifiers, the IDFA on iOS and the Advertising ID on Android.

But developers are also increasingly harvesting phone numbers from users as unique identifiers, and phone numbers change far less frequently than device identifiers. And since phone numbers are required for two-factor authentication, then users mostly have no choice but to keep them current with their favorite services. Apple’s forced SSO integration won’t prevent app developers from being able to target users with unique data; it may simply change which pieces of unique data are used.

So while I think the degree to which Apple SSO impacts Google and Facebook has been blown out of proportion, I do think it potentially presages a very important change to the mobile ecosystem: the deprecation of the IDFA altogether.

Apple’s change in guidelines related to children’s apps hints at this. If Apple is building a precedent around rejecting advertising tracking in certain categories, it certainly doesn’t seem totally infeasible that it extends that judgment to all categories by deprecating the IDFA, just as it deprecated the UDID back in 2012.

The phasing out of the IDFA would have a tremendous impact on the mobile advertising ecosystem. Even the prospect of the IDFA’s demise should inspire some consideration by marketers around how their business endures in a world without that identifier, since it ties into basically every aspect of mobile marketing: targeting, attribution, re-engagement, etc. Without the IDFA, almost all of the machinery upon which modern mobile marketing relies becomes obsolete and in need of re-tooling. Developers should at the very least understand conceptually what a continuation of business plan looks like in the wake of the IDFA’s deprecation.

Frankly, marketers should have been considering this for a while — at least since the revelation of SKAdNetwork and the introduction of IDFA Zeroing. Apple has been tip-toeing toward an IDFA-less reality for a while, and non-direct response marketing formats have forced marketers to build comprehensive macro models of performance that don’t account for individual attribution, anyway. In a post titled Mobile’s post-attribution era from 2017, I wrote:

App developers are no longer competing for the uninitiated: they’re no longer exclusively trying to reach first adopters in the developed world. Since everyone has a smartphone, everyone is a potential customer, and so marketing tactics have changed: hyper-targeted direct response ads have a place in every company’s strategic arsenal, but since users are everywhere, they need to be reached everywhere. Direct response, alone, simply isn’t broad enough to reach the total addressable market of a company operating in a big, expansive lifestyle space.

If a business is wholly reliant on perfect marketing attribution at the level of the individual user, then that business can’t weather the deprecation of the IDFA (and its viability with the IDFA is likely only illusory). The most agile mobile marketers — the ones who have reached beyond direct response — have already thought through these marketing models and, while they likely wouldn’t welcome the end of the IDFA, can at least withstand it. Every marketer should be thinking about how their job changes if the IDFA goes away.

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Would you rather advertise on TikTok or Netflix? https://mobiledevmemo.com/would-you-rather-advertise-tiktok-or-netflix/ https://mobiledevmemo.com/would-you-rather-advertise-tiktok-or-netflix/#respond Mon, 03 Jun 2019 05:00:59 +0000 https://mobiledevmemo.com/?p=26331 I hosted a panel at the MAU conference in Las Vegas last month titled “Perfect your Growth Mix: Brand vs. Performance Marketing.” At one point in the on-stage exchange, having discussed linear television advertising and streaming, I asked the panel — comprised wholly of app advertisers — a pointed (and unplanned) question: Would you rather […]

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I hosted a panel at the MAU conference in Las Vegas last month titled “Perfect your Growth Mix: Brand vs. Performance Marketing.” At one point in the on-stage exchange, having discussed linear television advertising and streaming, I asked the panel — comprised wholly of app advertisers — a pointed (and unplanned) question: Would you rather advertise on TikTok or Netflix?

The question was frivolous and intentionally contentious: TikTok’s advertising platform is nascent, Netflix (as of now) doesn’t feature any advertisements, and the premise was a false choice (if Netflix did host ads, there’s no reason an advertiser would have to choose between one platform or the other). But given the context of the panel, I thought it would be interesting to see where (and why) people held preferences for one or the other: surely brand marketers would choose Netflix and performance marketers would choose TikTok?

Unsurprisingly, none of the panelists took the bait (almost everyone took the “I’d wish for more wishes” approach and said they’d advertise on both), but even as a fantastical hypothetical, the question draws interesting boundaries between performance and brand advertising on mobile. Which formats and content backdrops are most appealing to each strategic blueprint? And why would one platform be more interesting to a brand marketer than the other?

As background: TikTok is a video sharing mobile app in which users apply music to short video clips (it is the former Musical.ly app, which was acquired by ByteDance and rebranded as TikTok), and Netflix is a video streaming service. 70% of time spent watching Netflix happens via a television (and only 10% happens via a phone), so the products serve different use cases, but a survey conducted by Facebook revealed that 94% of people hold a smartphone in their hand while watching television, so its not absurd to think of Netflix as a potential venue for mobile app install ads.

The real points of differentiation between the services, at least for the purposes of the question, are the target demographics and content formats for each. TikTok serves bite-sized video content and Netflix serves long-form video content. And consequently, the demographic reach of each service is different. While Netflix doesn’t break out viewer statistics (for a comprehensive review of streaming usage statistics, see this recent comScore study), we can infer a number of things from broader streaming demographic research to conclude that Netflix’s viewership is more stratified by age than TikTok’s, which trends extremely young.

Why do these differences matter? A few reasons. The first is that brand advertisers are less likely to want to place their ads next to user-generated content in the first place, and, depending on the brand, they could be extremely loath to juxtapose their brands with user-generated content from very young people. The second reason these differences matter is that the short-form TikTok content is modular and “feed-able,” or able to be placed in a scrolling, infinite feed. This means ad units look almost native and are tightly integrated within content in a way that doesn’t disrupt engagement.

Contrast that with most possible implementations of ads on Netflix, which would be contra-content: video that is designed specifically to disrupt engagement to draw attention to whatever is being advertised. There are potentially creative ways around this (see this article for some ideas on the future of interactive entertainment), but if the “commercial” paradigm (mid-roll ad) or a pre-roll format would prevail on Netflix, it would stand in opposition to the core content that viewers had queued.

That’s a terrible format and placement for mobile app install ads. Users don’t like punctuating their content experience with an app download: it’s why mobile app install ads do well in feeds and before / after short-form content (Facebook feed, Instagram stories) but not in mid-roll placements or even generally on the mobile web within long-form text content, but they do work next to search results (short-form, mid-feed).

Ad context matters, and even though TikTok and Netflix could be replaced in the question I asked with a host of other services each, the underlying fault lines that form between these two video consumption services are interesting for advertisers to think about. What’s the goal of a campaign, and can it be fulfilled with an ad on TikTok? What about with a mid-roll Facebook video ad? A Snap ad? An in-game rewarded video ad? An abundance of ad formats has spoiled mobile advertisers for choice, but those formats tend to be successful in narrow ways. It’s important to consider that before getting excited about new ad inventory.

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Introducing QuantMar, a knowledge-sharing website for performance marketers https://mobiledevmemo.com/introducing-quantmar-knowledge-sharing-platform-performance-marketing/ https://mobiledevmemo.com/introducing-quantmar-knowledge-sharing-platform-performance-marketing/#respond Mon, 03 Jun 2019 00:55:03 +0000 https://mobiledevmemo.com/?p=26337 Today I am pleased to announce the launch of QuantMar, a knowledge-sharing and Q&A website for performance marketers. The premise of QuantMar is simple: users can sign up and ask and answer questions related to performance marketing. I like to think of QuantMar as a StackExchange for performance marketers: it’s a place where best practices […]

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Today I am pleased to announce the launch of QuantMar, a knowledge-sharing and Q&A website for performance marketers. The premise of QuantMar is simple: users can sign up and ask and answer questions related to performance marketing. I like to think of QuantMar as a StackExchange for performance marketers: it’s a place where best practices and wisdom around user acquisition and quantitative marketing (thus QuantMar) can be shared, indexed, and referenced.

I built QuantMar because I believe that performance marketing, as a discipline and career path, is too opaque. Universities don’t do an adequate job of preparing students for careers in performance marketing, most institutional knowledge about performance marketing is unique to specific products and verticals, and the vast majority of free, educational material related to performance marketing is low-value, biased content marketing written by vendors.

The innovations that have benefited digital marketing and mobile monetization over the past few years have allowed new commercial paradigms (like Direct To Consumer) to bloom into billion dollar industries. Much of this is driven by hyper-targeted performance marketing — my aspiration is for QuantMar to become the hub at which the best marketers learn, share, and are vetted and recruited by the best companies.

The sad reality is that the lack of acknowledged, industry-wide best practices for performance marketing benefits the multitude of middle-men that exist in the advertising landscape: when there’s not clear consensus on eg. how to measure retention, or how to calculate LTV, or how to track cross-channel install attribution on mobile, or how CPI traffic is delivered, then rent-seeking behavior steps in to profit from that ignorance. From my perspective, facilitating the sharing of this basic insight helps all advertisers by killing the market for bad behavior.

Additionally, the opacity of this field makes it difficult for companies to find and recruit top talent. When a company is making its first user acquisition hire, it can be difficult for them to evaluate the candidates they meet since so few standards exist to assess experience as valuable or not. QuantMar provides a venue for the best performance marketers to showcase their knowledge and abilities (with more features aimed at recruitment / expertise recognition on the 2019 product roadmap), making them easy for hiring managers to discover and pursue.

And finally, I believe the future of education for applied fields like performance marketing, which combines a broad set of academic disciplines like statistics, economics, marketing, and computer science into a practical framework, is collaborative knowledge-sharing — a real-time, dynamic Socratic method. Performance marketing moves too quickly and is too multi-disciplinary to be capably taught in a classroom. I believe QuantMar can become the MBA program for performance marketers.

A vast number of public and private Slack groups have been organized to facilitate knowledge sharing amongst marketers, but Slack is not a good tool for indexing information for later reference. The Mobile Dev Memo Slack team, for example, has had more than 2,000 people register as members since launch, and I see the same questions and conversations being raised consistently. Marketing Slack groups are great places to trade time-sensitive, market-related intelligence (eg. “Has anyone else seen FB CPMs increase this month?”) and gossip, but they’re not very good places to permanently store wisdom and establish common best practices.

QuantMar has been live in a limited beta for the past two weeks, and already an encouraging volume of high-quality content has been written for the platform. I’d specifically like to thank the founding members of the site for helping me to QA features, think about product improvements, and contribute content: Olivier Lemarie, Thomas Petit (@thomasbcn), Shamanth Rao (of the How Things Grow podcast), and Avinash S.

Here are some topics that I suggest reading to get started:

Happy reading!

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Does CPI matter in a ROAS-centric strategy? https://mobiledevmemo.com/does-cpi-matter-roas-facebook-vo-aeo/ https://mobiledevmemo.com/does-cpi-matter-roas-facebook-vo-aeo/#respond Mon, 27 May 2019 05:00:41 +0000 https://mobiledevmemo.com/?p=26239 With Google’s UAC and Facebook’s AEO and VO bid strategies becoming so prominent in mobile marketing strategy, the relevance of the CPI metric has diminished. Last week I wrote about how I believe the LTV metric should be retired by mobile advertisers: the metric is anachronistic and doesn’t reflect the modern reality of mobile advertising, […]

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With Google’s UAC and Facebook’s AEO and VO bid strategies becoming so prominent in mobile marketing strategy, the relevance of the CPI metric has diminished.

Last week I wrote about how I believe the LTV metric should be retired by mobile advertisers: the metric is anachronistic and doesn’t reflect the modern reality of mobile advertising, which increasingly is optimized for ROAS by black-box algorithms as on Facebook and Google. One common refrain I’ve heard in this context is that, when campaigns are operated against ROAS targets, CPA metrics like CPI don’t matter: the underlying cost of an event or install is irrelevant if a ROAS goal is hit.

This line of thought makes sense, to a point. One could extrapolate this logic to an extreme to showcase why marketers, to some degree, must care about CPI even if ROAS targets are being hit. Which of these cohorts would a company prefer?:

  1. 1,000 users in its app that were acquired at an average CPI of $1 (campaign cost: $1,000) and will completely recoup that cost in seven days;
  2. 1 user in its app that was acquired at an average CPI of $1,000 (campaign cost: $1,000) and will completely recoup that cost in seven days?

It’s fairly obvious that the company would prefer 1,000 users. But why?

For one, virality effects accrue at the absolute size of the DAU-base, so even if the k-factor is the same for both of these groups of users — at, say, 2 — then the group of 1,000 users generates an additional 2,000 users whereas the lone user in the second group recruits exactly two new users. One could quibble about the value of these virally-acquired users (is the $1,000-spending user recruiting other $1,000-spending users?), but if organics tend to look the same, then the organic value of the 1,000-user cohort is far higher than the value of the one-user cohort.

Additionally, one simply can’t know anything about a cohort of one: the law of large numbers helps marketing teams derive insights around user behavior and to build predictive models of churn and monetization, but those insights need to be applied to groups of users. A retention profile or recoup curve can’t be applied to a cohort of one: that user is an unpredictable liability. Diversifying the user base with a large number of users is a protection against knowable average behavior (like the retention profile) but especially against unknowable changes to behavior (eg. the market changes and the retention curve shifts downward).

One might look at the example above and call it fatuous, but a $1,000 CPI is not completely unrealistic: CPIs can easily creep into the mid-$100s with VO campaigns on Facebook for niche audiences. And the higher those CPI numbers climb, the more confidence an advertiser needs to have in the immutable shape of its ROAS curve: will ROAS progression really remain unchanged as CPIs increase? At CPIs in the hundreds of dollars, the cost of historical ROAS curves changing for new cohorts is substantial.

And what’s more, one has to recognize that the competition for (cost of) users that monetize to a high degree is disproportionate to their monetization: the competition for those users scales non-linearly as the best-monetizing apps fight to recruit them. So a high-value user might install the app and monetize along the schedule of the company’s observed ROAS curve, but the survival function for those users is different: they are more highly-sought than other users and thus they are seeing more relevant, precisely-targeted ads for other apps than other users are. In other words, the $1,000-spending user from the above example is under greater threat for being poached by a competing app than any of the $1-spending users are.

At the margins, a marketing team should be more focused on ROAS targets than CPI. But common sense needs to prevail in running ROAS-centric campaigns as cost creeps up: will new users really monetize in the same way that older ones have as campaigns target more and more precisely on the basis of expected revenue contribution, as the VO bid strategy does? It makes sense to always be conscious of CPI, even if the CPI number isn’t central to the campaign goal.

Photo by NeONBRAND on Unsplash

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It’s time to retire the LTV metric https://mobiledevmemo.com/retire-the-lifetime-customer-value-metric/ https://mobiledevmemo.com/retire-the-lifetime-customer-value-metric/#respond Mon, 20 May 2019 05:30:24 +0000 https://mobiledevmemo.com/?p=26186 The LTV metric is only partially useful to modern mobile marketers. For many mobile advertisers, the LTV / CAC ratio sits at the very heart of commercial operations — LTV / CAC is the fundamental measurement of product viability, encapsulating both marketing efficiency and product monetization. But the LTV metric is an anachronism on mobile. […]

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The LTV metric is only partially useful to modern mobile marketers.

For many mobile advertisers, the LTV / CAC ratio sits at the very heart of commercial operations — LTV / CAC is the fundamental measurement of product viability, encapsulating both marketing efficiency and product monetization.

But the LTV metric is an anachronism on mobile. For one, no one thinks about monetization on the basis of some abstract “lifetime”: in this second act of the mobile economy, users are sticky, products are enduring, and monetization streams are complex. The “LTV paradigm” really represents the first act of the mobile economy, when products could launch and gain traction with relatively modest budgets and users casually, frictionlessly flitted between them as fungible commodities.

This isn’t what the world looks like anymore: there are fewer winners in the second act of the mobile economy, but they’re gigantic, and they enjoy some level of customer lock-in — either through network moats, brand loyalty, product differentiation based on real technological innovation, or a combination of all three. On this basis, there is no “user lifetime,” at least not in a way that can practically be applied to marketing operations. If a user can remain with an app for three or five or seven years, what’s the point of calculating some hypothetical “value” of that lifetime? An estimated LTV over the course of years would be unreliable, fragile, susceptible to massive swings as the product changed, and most importantly, too expensive to buy traffic against.

Rather, marketers in this second act should approach growth from the opposite perspective: what is the optimal way to structure cash flows to support the growth of the business? In “How does LTV / CAC fit into a growth strategy?,” I wrote:

Really, cash flow management requires comprehension of the way in which cohort daily revenues compound to make daily P&L positive and how much cash the company has in the bank to fund marketing and all other operations. These are truly strategic points for a company to dissect, and that process can’t start without knowledge of what relevant LTV / CAC ratios are.

What this means is that a payback window — that is, the period over which the money outlaid on marketing is paid back by a cohort — is really the critical component of marketing strategy, not LTV. If an advertiser is cash constrained, a Day 365 LTV is irrelevant. Advertisers are better served focusing on month-to-month cash generation than the hypothetical LTVs of users acquired today.

Yet advertisers torture themselves over LTV models that are highly fragile and almost impossible to validate (how do you back-test an LTV model when the product is updated every two weeks?). LTV modeling is actually fairly straightforward (although some marketing analysts overcomplicate it), but it’s futile to try to project an LTV curve out years into the future, especially for an immature product.

That approach simply doesn’t work. I showed in this presentation that even trying to calculate Day 4 ARPU at a 95% confidence level with 500 new users per day is fraught; projecting LTV out to Day 30 or 90 or 180 requires much, much more data than most advertisers realize.

I believe the better approach to thinking about LTV in the abstract, terminal sense is to buy traffic at a 100% ROAS target against an early-stage benchmark until enough data is accumulated to establish new, later-stage benchmarks. An example is outlined below: the marketing team sets a ROAS goal of 100% at the Day 15 benchmark and accumulates enough data to understand how the ARPU curve evolves to that point. Using that curve, the team estimates the bid level for 100% recoup at the Day 30 benchmark and buys against that (reducing the Day 15% ROAS to 80%). The team then accumulates data until they can estimate the bid for 100% ROAS at Day 60, and so on.

This is methodical, and it’s slow: if the underlying Day 60 ROAS bid for a campaign is high, an advertiser is leaving money on the table by bidding less than that just to accumulate data. But slow isn’t necessarily a bad thing when millions of dollars in ad spend are at risk. And coupling this approach with a mechanic like the Blended Same-Money Return metric allows advertisers to build robust, defensible growth strategies that don’t rely on ever-more cash being pushed into an unpredictable system against unrealistically long payback windows. A slow, winning strategy beats a losing strategy over a long enough timeframe.

What’s more, this strategy is consistent with the new, algorithmic campaign management reality that advertisers live in now. Google and Facebook et al will manage to ROAS targets for advertisers; it is wholly unnecessary to spend time and energy trying to estimate LTV across a long timeline when the largest networks are doing the heavy lifting.

Photo by Josh Rose on Unsplash

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What marketers should know about Facebook’s upcoming pivot https://mobiledevmemo.com/can-cryptocurrency-protect-facebook-being-broken-up-2/ https://mobiledevmemo.com/can-cryptocurrency-protect-facebook-being-broken-up-2/#respond Mon, 13 May 2019 22:36:51 +0000 https://mobiledevmemo.com/?p=26151 It's clear that Facebook is desperately working to stave off the kind of repressive regulation that would almost certainly not be constructed with a full appreciation of the marketing ecosystem, and the company should continue to very visibly make progress there. One development that has proceeded without much fanfare is Facebook's cryptocurrency project, which I believe might not be designed with payments at its core but rather as a sort of "cash back" or rewards program for users.

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Facebook unveiled a bold new redesign of the main Facebook app (the “Big Blue app”) at its F8 developers’ conference two weeks ago that elevates Groups and Events into central focus and mostly relegates News Feed updates. This redesign is a component of a larger strategy that Mark Zuckerberg outlined in March: Facebook is to no longer serve as a public square and will rather better facilitate private communications. The subtext here is that, with 1.5BN Daily Active Users, Facebook has already connected the world — now it must safeguard the fidelity of those connections and prevent them from being violated by sinister forces.

Everyone who is paying attention knows exactly what those sinister forces are: unscrupulous marketing analytics companies, state actors, fringe social movements and conspiracy theorists, for-profit Sowers of Political Discord, etc. These entities exist and should be dealt with, but the hysteria from the media that surrounds the discovery of each one is, ironically, designed to do the same thing that critics malign Facebook for: stealing attention with clickbait.

And with each new revelation of a privacy breach or an abuse by a malicious actor, an OpEd is penned that explores, from the most abstract altitude, Facebook’s impact on “small-d democracy” and its utility to society. Most commentary of Facebook as a social force is puerile sermonizing that wouldn’t be out of place at a dorm room bong session. And, surprise, very few people want to take the difficult position: Facebook is the most powerful advertising platform that has ever existed, and it has unlocked an untold amount of value for advertisers.

The most recent and highest-profile variant of this OpEd is the one written by Chris Hughes, a Facebook co-founder, in the New York Times last week. In the piece, Hughes argues that Facebook has become a monopoly and should be broken up by the government. Hughes proposes that the company has too wide a reach, Zuckerberg has too much concentrated power, and its colossal size has allowed it to stifle innovation, which, he asserts, should be another standard for classifying a monopoly (versus merely manipulating consumer prices).

The article is accompanied by a — frankly, puzzling — infographic:


There are two glaring issues with this infographic. The first is that the userbase across each of Facebook’s portfolio apps isn’t exclusive to those apps: this infographic appears to claim that 2.3BN users log into Facebook each month and 1.6BN different users log into WhatsApp each month, but that simply isn’t true, as there is a tremendous amount of overlap across these services. Facebook’s overall MAU size is not the sum of the MAUs of each of these apps. If I log into the Facebook app and then into WhatsApp and subsequently into Instagram, I’m just one person to Facebook, not three.

The second issue with this infographic is conceptually similar: a Facebook user doesn’t sign away their right to browse Reddit or to watch videos on YouTube or to record karaoke videos on TikTok when they sign up to the service — there’s overlap across services, too. These products are all freemium services with zero switching costs that can co-exist: it’s bizarre to look at the size of Facebook’s portfolio in terms of user base and say it is crowding out other participants when those other participants are perfectly capable of also capturing the attention of Facebook users.

It’s also impossible to make the argument that Facebook has crowded out new entrants to the market. Take YOLO, the anonymous question-asking app that rocketed to the #1 Downloaded chart position last week and was built on top of Snapchat’s Snap Kit platform. Facebook acquired an app with a similar launch trajectory, tbh, in 2017 and shuttered it a year later. tbh — or Instagram, or WhatsApp — didn’t have to sell at all and certainly not to Facebook: it did so because Facebook’s patronage offered it the best opportunity for success. That’s true because Facebook has managed to institutionalize the practice of not only capturing growth but of also capturing attention and value via its expansive, sophisticated advertising platform. That’s not true of some other potential acquirers and it’s why Facebook wins those deals.

There is real substance to that last point: if Facebook is broken apart or otherwise heavy-handedly regulated, much of the that value evaporates. Your Casper mattress, your Criquet shirt, your Atoms shoes become more expensive or disappear altogether because advertising as a whole has gotten much less efficient. OpEds about Facebook’s role in society don’t discuss this; they don’t recognize the value that Facebook has delivered to consumers and to advertisers by building the world’s most efficient advertising platform. This is the massive elephant in the room that frustrates marketers when they read these OpEds.

Especially when those OpEds claim that Facebook is no longer innovative, which is either disingenuous or out of touch. Facebook generates almost all of its revenue from advertising; advertising features play a central role in the user experience and therefore are user features. And Facebook has certainly brought to market profound advertising innovations in recent years: Lookalike Audiences (which is probably the most impactful innovation in digital advertising since the tracking pixel, although it technically falls outside of the five-year time window), the App Event Optimization bid strategy, the Value Optimization bid strategy, dynamic Campaign Budget Optimization, automatic captioning of video ads, pre- and mid-roll ads for videos, the dynamic creative generator, Instagram’s Checkout feature, and automated ads (which was launched just last week).

Without AEO and VO bid strategies, I’m convinced that the entire D2C category would not exist, and the dynamic creative generator allowed mobile commerce to flourish. But even beyond my subjective opinion on this topic, it’d simply be impossible for Facebook to grow ARPU in Western markets while user growth stalled unless — as it has — it was consistently delivering value to advertisers through ad product innovation.


This dimension is wholly absent from the discourse around Facebook and democracy, and it’s exasperating both because it should be the very heart of the argument (rather than the ridiculous and facile notion that Facebook “sells data”) and because actual marketers have more or less been excluded from the debate. Everyone wins when Facebook introduces new advertising features: users (who get better-targeted ads), advertisers (who get to sell products more efficiently), Facebook, and competitors, who get to borrow those ideas, like Snap did when it recently announced the Snapchat Audience Network.

It’s clear that Facebook is desperately working to stave off repressive regulation or something even more drastic, like the dismantling of the company. And Facebook should make overtures to governments, accept fines like the one it will imminently pay to the FTC with humility, and generally attempt to dull the knives that have cynically been unsheathed for it by politicians looking to score populist points. One development that has proceeded without much fanfare is Facebook’s cryptocurrency project, which I believe might not be designed with payments at its core but rather as a sort of “cash back” or rewards program for users.

Imagine users being rewarded for allowing advertisers to target them: it’d enable users to monetize their data while also ensuring that ads stay relevant. Facebook has been incredibly guarded in its discussion of this cryptocurrency project, and it may not end up being in service of advertising at all. But such a mechanism would certainly change the dynamics of Facebook advertising and incentivize users to make their data available for targeting — and potentially placate calls for extreme regulation or the company’s break-up in the process.

In any case, marketers should closely follow Facebook’s pivot from the digital public square to a secure messaging platform. As this transformation happens, much of the infrastructure that marketers rely on will likely change, with custom audiences and lookalike audiences appearing especially vulnerable to new constraints. And maybe that’s a good thing: perhaps if most users knew how custom audiences worked, they’d feel invaded. But the discussion as it exists now doesn’t examine that; it only promotes soaring language and sweeping philosophical pontification. And marketers need to prepare for the world that such language seeks to deliver.

Photo by Adi Ulici on Unsplash

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Optimizing campaign spend with the Blended Same-Month Return metric https://mobiledevmemo.com/optimizing-campaign-spend-with-the-blended-same-month-return-metric/ https://mobiledevmemo.com/optimizing-campaign-spend-with-the-blended-same-month-return-metric/#respond Mon, 06 May 2019 16:12:23 +0000 https://mobiledevmemo.com/?p=25997 Systematic growth through programmatic mobile marketing analysis. One of the difficult aspects of managing large marketing budgets is reconciling the urge to measure everything on the basis of a cohort’s lifetime with the very practical need of the business to manage cash flow and plan around monthly expenses. Companies can go bankrupt buying advertising on […]

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Systematic growth through programmatic mobile marketing analysis.

One of the difficult aspects of managing large marketing budgets is reconciling the urge to measure everything on the basis of a cohort’s lifetime with the very practical need of the business to manage cash flow and plan around monthly expenses. Companies can go bankrupt buying advertising on an LTV-profitable basis; it’s important for advertising managers to be able to predict how much money any given cohort will recoup against a timeline of regular, predictable monthly expenses like payroll, rent, and even advertising media itself.

One interesting way to think about this is: if I start buying traffic on the first of the month and run those campaigns for some set amount of days, when will that spend be recovered (recouped)? The shorter that number is, all other things held equal, the higher the “velocity” of revenue for the product is and the faster profit accumulates into growth. If media spend could be recouped same-month — that is, I spend $100 in May and generate $100+ in revenue on that spend in May — then it’s likely that expenses could be managed in such a way that advertising spend never reduces the company’s cash balance.

How would one go about measuring that same-month return? We can start with a theoretical, linear cumulative ARPU curve:

This curve shows that any given cohort produces $0.05 in revenue each day, and by the end of Day 7, $0.35 has been generated.

If we were to start buying traffic against this curve, and buy it for seven days, then we’d theoretically see an ARPDAU schedule that looks like this:

The Timeline x Cohort matrix here just shows that we have seven cohorts that passed through the Day 1 value of the cumulative ARPU curve above and produced $0.05 for a total of $0.35 (7x$0.05 = $.35), we have six cohorts that passed through the Day 2 value and produced $0.05 for a total of $0.30, we have five cohorts that passed through the Day 3 value and produced $0.05 for a total of $0.25, etc. If we sum those Day X cumulative ARPU totals up, we get $1.40, which means that $1.40 is the expected aggregate revenue generated from seven of these hypothetical users introduced to the product over seven days (ie. if we acquired one user per day for seven days and they all exhibited this cumulative ARPU curve).

If we don’t want to use a spreadsheet to calculate this, we can do it two other ways. The first is to use linear algebra to calculate the value of this ARPU matrix with a dot product, where the number of cohorts that have reached each LTV day is multiplied by the ARPU value at that day. Programmatically, that looks like this:

#assumptions
timeline = 7
CPI = 3
DNU = 100

#linear cumulative ARPU equation: y = .05x

ARPDAU = [ ( .05 * x ) - ( .05 * ( x - 1 ) ) if x > 1 
    else ( .05 * ( x ) ) for x in np.arange( 1, ( timeline + 1 ) ) ]
blended_ARPU = np.dot( list( range( timeline, 0, -1 ) ), ARPDAU )
print( blended_ARPU )
#this is the revenue generated by all cohorts over the timeline (=7 days in this example)

This produces the following output:


The other approach is to use a mathematical sum on the LTV equation over the timeline. We can do this with Wolfram Alpha to get the same result:

(If you are wondering why a sum is used here instead of an integral, this article provides a useful explanation.)

Once we have this per-user revenue number, we can calculate the total return from those cohorts within that timeline by multiplying the weighted average DNU over the period by the user-level revenue and dividing that number by the total cost (which is CPI times the timeline times weighted average DNU).

We can also do this programmatically:

total_recoup = ( blended_ARPU * DNU )
total_cost = CPI * DNU * timeline
total_recoup = total_recoup / total_cost
print( str( round( total_recoup * 100, 2 ) ) + '%' )

In both cases, we get 6.67% total blended return from these seven cohorts in the same seven days: we generated $7,000 in total revenue from the 35,000 acquired users, and we spent $105,000 on user acquisition.

We can now look at a more realistic example with a log-shaped LTV curve:

In this case, using the same DNU and CPI assumptions, we generated $4.40 in per-user revenue over those seven days, which produces 21% return over the period:

It’s easy to see why this analysis is inconvenient to manage in a spreadsheet: in order to calculate these values for the month, we’d need a 30×30 matrix of cells that wouldn’t fit in one screen. So we can implement this programmatically by changing the timeline:

timeline = 30

#logarithmic cumulative ARPU equation: y = 0.25*ln(x)+0.02
ARPDAU = [ ( .5 * np.log( x ) + .02) - ( .5 * np.log( x - 1 ) + .02 ) if x > 1 
    else ( .5 * np.log( x ) + .02 ) for x in np.arange( 1, ( timeline + 1 ) ) ]
blended_ARPU = np.dot( list( range( timeline, 0, -1 ) ), ARPDAU )
print( blended_ARPU )
plt.plot( np.arange( 1, ( timeline + 1 ) ), [ ( .5 * np.log( x ) + .02) - ( .5 * np.log( x - 1 ) + .02 ) if x > 1 
    else ( .5 * np.log( x ) + .02 ) for x in np.arange( 1, ( timeline + 1 ) ) ] )
# ^ ARPDAU
plt.plot( np.arange( 1, ( timeline + 1 ) ), [ ( .5 * np.log( x ) + .02 ) for x in np.arange( 1, ( timeline + 1 ) ) ] )
# ^ Cumulative ARPDAU
plt.show()

#again, the revenue generated by all cohorts over the timeline

Here we have calculated a blended per-user revenue value of $38, which is a 42% same-month return:

If we want to know how many days it will take to recoup the money spent on those 30 cohorts, we can write a simple function that just iterates through the LTV curves of those cohorts and calculates how much cumulative revenue has been accrued from them and stops when the total cost is equal to the total revenue:

def get_total_return_date( timeline, CPI, DNU, equation ):
    #equation contains a and c for the formula y = a*ln(x)+c
    
    ARPDAU = [ equation[ 'a' ] * np.log( x ) + equation[ 'c' ] for x in np.arange( 1, ( timeline + 1 ) ) ]
    blended_ARPU = sum( ARPDAU )

    total_recoup = ( blended_ARPU * DNU )
    total_cost = CPI * DNU * timeline

    total_recoup = total_recoup / total_cost

    i = 1
    while total_recoup < 1:
        ARPDAU = [ equation[ 'a' ] * np.log( x ) + equation[ 'c' ] for x in np.arange( 1, ( timeline + 1 + i ) ) ]
        blended_ARPU = sum( ARPDAU )

        total_recoup = ( blended_ARPU * DNU )
        total_cost = CPI * DNU * timeline

        total_recoup = total_recoup / total_cost
        i += 1
        if i > 100:
            return False
    return i + timeline

In this case, our 30 cohorts recoup fully by day 59, which means that we’ll get paid in month three for the money we spent in month one.


The more interesting question to answer is: how can I ensure that my Blended Same-Month Return is 1? That is, how can I manage my spend in such a way that I’m only spending in-month what I’m also recouping in-month? If that can be accomplished, then payment terms and timelines can roughly be adjusted to ensure that I’m carefully managing my cash balance.

In order to do this, we can alter the very simple function above to start at 30 (or whatever the number of days in the current month is) and work backwards until some number of cohorts fully recoups within that same month. The new function is:

def get_cohort_return_timeline( CPI, DNU, equation ):
    #equation contains a and c for the formula y = a*ln(x)+c
    
    #month length is the timeline that we calculate blended return across
    month_length = 30
    timeline = month_length
    
    ARPDAU = [ equation[ 'a' ] * np.log( x ) + equation[ 'c' ] for x in np.arange( 1, ( month_length + 1 ) ) ]
    blended_ARPU = sum( ARPDAU )

    total_recoup = ( blended_ARPU * DNU )
    total_cost = CPI * DNU * timeline

    total_recoup = total_recoup / total_cost
    
    if total_recoup >= 1:
        return True

    i = 1
    while total_recoup < 1:
        ARPDAU = [ equation[ 'a' ] * np.log( x ) + equation[ 'c' ] for x in np.arange( 1, ( month_length + 1 ) ) ]
        blended_ARPU = sum( ARPDAU )

        total_recoup = ( blended_ARPU * DNU )
        total_cost = CPI * DNU * ( timeline - i )

        total_recoup = total_recoup / total_cost
        i += 1
        if i > month_length:
            return False
    return ( timeline - i ), total_recoup

What this function does is start by calculating the Blended Same-Month Return (BSMR) for a full set of 30 cohorts in the month. If that BSMR metric is >= 1, it returns True, meaning that the cohorts fully recoup within the month; if the BSMR is less than 1, it iterates backwards, trying 29 cohorts, 28 cohorts, 27 cohorts etc. until it finds some number of cohorts that can be purchased in the month that will fully recoup in that same month.

For the values in the example above, the number of cohorts that can be acquired and fully recoup same-month is 11:

Thinking about acquisition costs and recoup from the perspective of calendar months (or quarters, or years, etc.) as opposed to individual cohort timelines (eg. the cohort we acquired yesterday will pay for itself in 120 days) is helpful in planning and accounting for media spend. The code above is crude, but a more robust approach can be integrated into an advertising workflow to assist the marketing team and the broader organization in making better decisions around media buying.

The code used in this article can be found on GitHub. The Google Doc used in the article can be found here.

Photo by Scott Blake on Unsplash

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The uncomfortable tension between brand and performance marketing on mobile https://mobiledevmemo.com/the-uncomfortable-tension-between-brand-and-performance-marketing-on-mobile/ https://mobiledevmemo.com/the-uncomfortable-tension-between-brand-and-performance-marketing-on-mobile/#respond Mon, 29 Apr 2019 14:00:37 +0000 https://mobiledevmemo.com/?p=25883 The second mobile cycle instantiated an extraordinary new set of commercial opportunities: apps in entirely new categories and featuring new content interaction models are now generating billions of dollars per year. The mobile Top Grossing chart has thawed and diversified far beyond gaming, and the games that do reside there have adapted to the new […]

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The second mobile cycle instantiated an extraordinary new set of commercial opportunities: apps in entirely new categories and featuring new content interaction models are now generating billions of dollars per year.

The mobile Top Grossing chart has thawed and diversified far beyond gaming, and the games that do reside there have adapted to the new commercial milieu in which they operate as either eSports or with recurring subscription packages. The Top 10 grossing chart is now comprised of apps that support the Three Ss: streaming, subscriptions, and social in-app advertising. The monetization strategies and tactics that support these interaction models are the result of a number of tectonic shifts that have taken place in mobile over the past few years.

This new opportunity space, to many, feels like a beginning: the start of a new commercial era for mobile. A blank playbook can be terrifying, and as the second mobile cycle has attracted marketing talent from outside of mobile, some companies are looking to traditional media strategies for app growth. For many companies, these marketing tactics have taken the form of pure-play brand development as a go-to-market and growth strategy. This is a reversion.

If the first mobile cycle is ignored, and the idiosyncrasies of mobile are ignored, and the nature of freemium is ignored, then it’s natural to look to pre-mobile marketing strategies for guidance on achieving commercial traction. But the growth lessons that were learned during the first mobile cycle are relevant — eminently so, painfully so — in this second mobile cycle. The marketers that are ignoring the lessons of the first mobile cycle are doing nothing but paying tuition with their marketing budgets.

Mobile apps are probably the most measurable, instrument-able, connected consumer products that humans have ever interacted with. This measurability and the immediate feedback cycle between the user and the developer (advertiser) means that direct response advertising is the shortest and most efficient path to the largest number of users.

A simple diagram for thinking about the progression from reach to monetization on mobile is:

This pyramid is perennial and hasn’t changed with the second mobile wave. The bottom of the pyramid here is important: it is growth. And while brand equity plays an important role for some apps in capturing value and even in capturing growth, brand marketing almost certainly isn’t and shouldn’t be the exclusive strategy used for capturing growth. Brand marketing can contribute across the entire value generation spectrum (all three triangles), but it cannot service the foundation — capturing growth — on its own. Direct response performance marketing is fundamental to capturing growth on mobile.

As I have written before, the opposite of performance marketing is not brand marketing: it is non-performance marketing. Brand marketing can be woven into a performance marketing strategy in a way that preserves the measurability and immediacy of mobile: in fact, the best marketing teams use brand building to make their direct response campaigns more efficient. But as uncomfortable as it might be for brand marketers to embrace, direct response advertising is best suited to do the heavy lifting in capturing growth on mobile.

Photo by Benjamin Voros on Unsplash

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