Resource Label: App marketing

Need a retention boost? Singular now supports Google App campaigns for engagement

Today, Google officially announced its latest solution for mobile app performance marketers with the release of App campaigns for engagement. Combined with Singular’s support of this new campaign type, marketers have all the insights they need to maximize revenue and the lifetime value of every single user.

In November of 2017 Google introduced its AI-powered solution for optimizing mobile app campaigns which provides huge improvements to conversion rates. However, the question remained: “now what to do with all those new users?”

ENGAGE!

Google App campaigns for re-engagement runs on the same powerful AI to help marketers re-engage with their customers and encourage them to take specific, in-app actions. The goal of App campaigns for engagement is to improve customer retention and long term revenue by increasing active users, generating sales, and reducing churn.

Have a group of high-value customers that you want to keep happy? Engage them with a customer loyalty offers. What happened to all those users who added something to their cart but never purchased? Target them with a discount to complete their order. What about all the users who you know downloaded your app but never opened? Message them with an incentive to check out “what’s inside”.

Getting started with Google App campaigns for engagement is simple.

Singular makes it easy to set up conversion tracking, create deep-links into the relevant points in your app, and measure the performance of every event from the first time a user engages with your campaign, to the last time they engaged with your app. Get more details from your Singular Help Center.

If you are as excited about Google App campaigns for engagement as we are, reach out to your Google account manager to apply to for the whitelist.

Still, have questions? Reach out to your Singular Customer Success Manager or email us at contact@singular.net for more information.

21st century marketing intelligence webinar: Data, science, and magic in a world of smart devices

Every marketer knows marketing is changing.

You don’t have to be a CMO to see that lack of data is no longer the reason why marketers can’t grow their brands. Marketers are deluged with data, overwhelmed with data, buried in data. The solution lies within … but finding the growth needle in the data haystack is getting more and more challenging.

That growth needle is 21st-century marketing, or what we call marketing intelligence. And that’s exactly what we’re going to talk about in this webinar.

We’ve got the right people to share their insights.

Scott Beechuk
Scott is a partner at Norwest Venture Partners. He’s spent 20 years in the enterprise software industry, including as a senior VP with Salesforce and head of engineering for Desk.com, and VP product management and marketing for Codebees. He’s also co-founded multiple companies and currently serves on the board of Singular, Leanplum, Bluecore, and Socrates AI.

Morgan Norman
Morgan is CMO at Copper, the #1 CRM for Google’s G Suite. He’s previously led marketing at Dialpad, NetSuite, and Zuora, and was a senior director of marketing at Microsoft. Morgan has also founded companies, contributes to Forbes, and is an abstract painter.

We’ll talk about:

  • How CMOs manage the constant onslaught of data
  • How marketers can make smarter decisions to optimize growth
  • The biggest problems with marketing data
  • The emergence of the CGO: Chief Growth Officer
  • Marketing’s emerging leadership role across the entire enterprise
  • How to connect every input (effort) with an output (conversion)

You will not want to miss this webinar.

I’ve personally heard Scott Beechuk talk and there are few who understand the future of marketing and the future of marketing technology like he does. Morgan Norman is a former engineer as well who deeply understand what technology is doing to marketing, and lives it every day.

Details and link

Date: March 21, 2019

Time: 1:30PM Eastern, 10:30AM Pacific

Link: The webinar will be delivered via Zoom

We look forward to hosting you in a few weeks!

Singular & Segment: New partnership integration allowing for frictionless customer onboarding

I’m extremely happy to announce that Singular is now an official integration partner of Segment.

Segment is a customer data platform that many companies use to collect and action their customers’ data. When new relevant data comes from any source that a publisher has connected to Segment, they can now push real-time data streams from Segment to Singular.

That could include information such as customer purchases and revenue, mobile events like push notifications, or custom events that developers define for themselves.

Integration: easier than easy?

The integration also enables Segment customers to immediately adopt Singular attribution with almost zero migration effort.

Using Singular attribution is the best way to measure ROI from the campaign level all the way down to the creative level. It also allows you to benefit from Singular’s fraud protection and audience management solutions to boost and optimize your marketing.

The new integration capability is server-to-server, which means that mobile app developers do not need to add code or Singular’s SDK to their mobile apps. In other words, it can be instantly available.

That’s critically important to many customers because it means no switching costs and no engineering work. Not having to put an extra SDK in your app can also help slim down your install size, shield you from security and privacy concerns, and make your app more stable.

Capabilities: what can you do?

What can Singular customers do with this integration?

In just one example, advertisers can now receive real-time data about purchases that mobile users make via other platforms. That allows Singular to combine this data with details about customer acquisition cost, marketing campaigns, and ad creatives to provide continually updated ROI and customer acquisition cost data for customers, campaigns, and ads.

In much the same way, brands can track results from push notifications such as opens, actions taken after opening, and determine both cost and return of messaging.

And, of course, brands can attribute mobile app installs using Singular’s industry-leading attribution, fraud detection, and audience management tools.

Singular: first mobile attribution company

Singular is one of the first partners for this new integration program, and we couldn’t be happier to offer it to our customers.

For Singular, this is yet another way for us to unify accurate marketing data from an important partner in the mobile ecosystem, which gives marketers more visibility into what they’re doing, and what impact it is driving.

“Our goal at Segment is to allow our customer to quickly and painlessly connect all their data,” says Segment CTO Calvin French-Owen. “Singular is the first mobile attribution company to custom-build their integration using our Developer Center, and we expect great results for Segment customers and Singular customers.”

We’re very happy to be the first to offer this new integration method and are looking forward to ensuring our customers have a successful and simple integration.

If you have any questions about this, please feel free to contact your customer success manager.

Or, if you’re not a Singular customer yet, talk to us about getting a demo.

China lifted the gaming ban and developers are flooding back to enter the market. Here’s how you can too

The world’s biggest gaming market banned new games from entering the market starting March of 2018. China stopped approving games amid a regulatory overhaul triggered by growing criticism of games for being violent and allegations that they were causing myopia as well as addiction among young users.

Just recently, however, that changed.

In December, China decided to approve the release of 80 new games after months of no action. Now that the Chinese government has lifted its ban on new gaming releases, gaming app developers are chomping at the bit to launch their new mobile gaming apps on Chinese app stores.

The Chinese mobile opportunity

Vast opportunities are on the horizon as approvals begin to flow again.

China accounts for one out of every four US dollars generated globally from mobile games. The revenue generated by apps in China in 2017 is an estimated $35 billion USD, and app downloads from Chinese Android stores are expected to reach almost $90 billion by the end of this year. Additionally, according to the China Internet Network Information Center (CNNIC), the country’s internet user base now stands at around 772 million, 97% of whom are smartphone users.

This is one massive dragon of an addressable market.

But as a rule, China’s licensing system requires that foreign publishers obtain government approval that often involves a complex process before releasing their games on one of the country’s app stores. This process can take months and has had a significant impact on some (but not all) foreign publishers, deterring some from launching locally. One consequence of the increasingly challenging regulatory environment: at the time of the complete ban, foreign developed games accounted for only 25% of the top 250 mobile game downloads on China’s App Store. That could start to change as approvals are starting to flow again.

With China’s recent reversal in attitude and policy, the tide appears to be turning for non-domestic publishers. 2019 and beyond looks promising for game developers who wish to tap into the Chinese market.

Here are a few important things to know before entering the market.

Complex app store landscape

While the Apple App Store lives very successfully within the great firewall, Google Play is strictly blocked, along with the rest of Google’s services. Instead there are over 400 Chinese alternative app stores where you upload your product for review.

Baidu’s mobile app store

The major ones are often owned by China’s biggest tech companies. For instance, Tencent, Baidu, Huawei, Vivo, China Mobile, and Oppo run major Android app stores. Of course, every store has its own terms and conditions, as well as specific requirements.

In recent years, some of the top handset manufacturers came together to form an alliance to standardize some of the app development and publishing features between app stores.It’s still early to determine how effective this will be.

Different advertising channels

With so many foreign internet services and apps blocked by the great Chinese “firewall” including Google, Facebook, Instagram, one of the key things that advertisers need to be aware of is that the advertising ecosystem in China is extremely different.

Ad channels and ad networks that have worked well for them in other markets may not necessarily render the same results in the Chinese market. In many cases, they may not even exist in China at all.

One way we can help: Singular houses the largest database of global advertising performance data and has successfully helped marketers to identify and work with the most effective ad channels globally, as well as in China.

(If you would like to speak to one of our in-house client success consultants, we would happy to share our list of top performing ad channels in China.)

Culture, language, and UX

WeChat’s opening screen

Chinese culture, language, governance, and mobile user habits are very different from the rest of the world. While the world’s average smartphone user has around 80 apps on their phone, in China users have over 100 … including and especially WeChat, the top social media app with over 200 million daily active users. WeChat has over a million mini-apps … including payments, services, stores, and just about anything else that run within WeChat.

This unique climate means that simple translation won’t suffice, and more complex redesign is often required.

Working with a local developer and translator is highly recommended, and success in China often means re-inventing your game or app for Chinese preferences and habits. Chinese customers typically shun apps which appear translated, so it’s important to make the app look as if it were made in China.

User monetization

In China, most paid and subscription-based apps don’t generate revenue, as free unofficial versions are readily available. This has led to the majority of companies monetizing apps through ads. Interstitial ads are one of the most popular methods of app monetization, with nearly all of the most prominent local apps implementing them to promote in-app purchases and other relevant products.

Banner ads and video ads are also prevalent. One challenge: as a large and populous country, China has many different dialects, which app publishers have to remember as they localize their apps.

APK fraud

Thanks in part to the proliferation of app stores, APK fraud is a challenge. Scammers grab the source code for your app or game, change it slightly and add their own monetization. Then they simply re-upload it to multiple app stores as their own, and benefit from an ad revenue stream, or in-app purchases.

Overall, the Chinese internet and mobile ecosystem is probably the most complex in the world. But since it is also the biggest in terms of consumer app spending, the rewards for getting it right can be massive.

Getting started

Singular has helped top global advertisers to successfully enter the China market.

We welcome you to reach out to speak to one of our in-house experts on the Chinese market and share more in-depth learnings for entering the market successfully.

We asked 1500 marketers how they choose ad networks, and the answer was ‘all of the above’

Is it scale? Quality? Lack of fraud? Personal service, or a great digital experience? Amazing technology? Or perhaps a tight focus on your particular niche?

We recently asked 1,500 marketers a simple question:

How do you choose ad networks? And what are the most important elements of that decision?

According to the responses, it’s pretty much all of the above. If they were absolutely forced to just pick one, completely compelled to isolate one single most important factor — on pain of losing their quarterly bonuses or maybe even the free triple-venti-soy-no-foam-lattes at the office — it’d probably be scale and reach.

But it’s a tight competition with the other options.

We only surveyed marketers who actively run ad campaigns. And the results make it clear that ad networks have their work cut out for them, because marketers are not easy customers. Quite simply, when it comes to choosing an ad network, they want it all, and they want it now.

As we all know, when everything’s a priority, nothing is a priority.

Looking at the results, we’d almost be tempted to say that when marketers are asked to choose ad networks, they don’t have a clue what the most important factors are.

But that’s probably unfair.

Individual marketers probably have a pretty good idea what works for them … and how to improve it. However, it is clear that marketers as a group lack consensus on what’s most important in finding new ad partners.

And that might just be the nature of the beast:

It’s not like this is easy.

Of course fraud protection is important. Of course scale matters. Of course a media source’s target tech can be a difference-maker. It never hurts when an ad network has special ability to focus on your specific vertical. And getting the best quality traffic, users, or customers is essential.

So it’s no surprise which ad networks marketers trust most.

We asked the same marketers that question, and the top four were names your grandparents recognize: Google, Facebook, Amazon, and Apple. They’re all massive companies, name brands, and have largely walled garden ad ecosystems, which typically means extremely low fraud.

But your marketing can’t end there.

Why?

We know that most marketers who are successful use many ad networks. In fact, they typically achieve 60% more conversions with 37% less cost. That’s not easy, and it takes work. Profitably scaling media sources is hard.

When everything matters, all your decisions are challenging. Because not all ad networks have huge scale, or super-strong fraud protection, or amazing targeting. But there are typically pockets of profitable growth spread in many different media sources.

Need help? Two ways we can help you choose ad networks:

  1. Get our Scaling Mobile Growth Report to find out why this matters
  2. Check out our Singular ROI Index, coming out soon. It will reveal the highest-ROI ad networks on the planet.

How to become a top 10% marketer: Snap’s Brendan Lyall on scaling mobile growth

How do you become a top 10% marketer?

Simple: you achieve top 10% results.

Of course, that’s where the challenge lies. And doing it is not nearly as easy as saying it. But, as we’ve seen in our Scaling Mobile Growth report, top marketers get more and spend less, helping their companies achieve breakthrough growth.

A great product is a necessity, a great team helps, and a great offer is important, but great marketers know that to maximize their results, they also have have to successfully manipulate four key levers: creative, media sources, bids, and budgets.

Get them right consistently, and you win.

Screw up any one of them, and you risk blowing budget, wasting time, and killing credibility.

Brendan Lyall

Brendan Lyall knows more than most about being a top 10% marketer. He was a growth marketer at RockYou! and Storm8, then built businesses in mobile marketing: Grow Mobile, which was acquired Perion Networks, and Downstream.ai.

He’s currently helping Snap build out its ad solutions for marketers.

I spent some time with him recently to talk about marketing, growth, and moving beyond what is safe and known in order to achieve outsized results.

Essentially: how to become a top 10% marketer.

Koetsier: There’s a comfort level for digital marketers in using ad partners they’ve always worked with before. But what’s the risk in that?

Lyall: I’ve been a marketer and I’ve been on the ad network side too.

It is always risky for marketers to get too complacent or comfortable with their current ad vendors. The ad ecosystem is a constantly changing landscape of ad partners and that also includes ever-changing performance of ad partner inventory quality and install value.

There are also inward changing variables that app marketers need to keep in mind to keep they their UA campaigns running efficiently. As many mobile apps evolve, so do their user bases, features, monetization strategies and a countless number of other variables, and this can directly impact the app’s ad campaign performance.

One common scenario is, your ad partner breakdown at one stage in your mobile app lifecycle might be a great fit and provide excellent yield, making the ad partner choice and reliability appropriate, but as the app transitions to another lifecycle phase this can result in certain ad partners being no longer effective or the best choice for a marketer’s ad partner stack. Active and savvy marketers should always consider and test new partners and track the performance fluctuations in correlation to the changes to the app.

It’s important to never allow ad partners to run unsupervised with no or minimal optimization. A marketer’s comfort can lead to complacency and while we all know building a efficient and ROAS-rich strategy is not accomplished overnight. Always stay on top of your marketing campaigns and constantly validate and iterate your ad partner prioritization.

All of the quality ad partners in the mobile ecosystem have evolving products, ad units, optimization algorithms and publishers, so continue to stay up to date on your ad partner’s product offerings and how they benefit your marketing strategy. Marketers should test new channels continually and optimize their campaigns to ensure that their ad partners remain relevant throughout their app’s lifecycle.

Koetsier: The same applies to old versus new ad formats. What have you seen happen when marketers try new ad formats?

Lyall: This is something that has been a constant struggle for a lot of marketers. New formats are really exciting, but can be challenging if you don’t have the resources. Creative and ad formats are often the one of the most crucial part of a performance marketing campaign and can often get overlooked.

Quantitative optimization often takes the front seat for obvious campaign changes since they can be most closely A/B tested and correlated to certain results. Creative and ad formats are the hardest to optimize effectively and to quantify accurately.

One thing that Snap has been very conscious of is user experience and how different brands mesh with that quality standard. As mobile users we are constantly finding ways to subliminally block out ads, this brings up the importance for both marketers and ad partners to constantly iterate on ad units and formats. Testing new creatives is something we encourage our marketers to do frequently. Snap ads are a full-screen experience and this is a unique and immersive way to interact with an app or a brand, and when people spend time with them, they demonstrate high levels of intent.

It’s no surprise that ad solutions that allow for strong customization for natural and native experiences perform the best. For marketers who do not have the luxury to do high production ad formats, it’s important to iterate and test the formats that are within reasonable scale to your budget and resources.

We often see marketers test Snapchat Ads for the first time get impressive results. With thoughtful optimization and iteration within our Snap Publisher Tool, they have been really pleased with the new segment of users coming from Snapchat through the immersive ad units. User experience is very important to Snapchat which is why the ads team has continually iterated on new ad formats that resonate best with Snapchat’s audience of users while also presenting relevant ads for them to interact with.

Ultimately, new kinds of ad types have created new opportunities for stronger adoption and better performance.

Koetsier: What’s working best on Snap right now? What kinds of campaigns for what kinds of brands?

Lyall: From a performance perspective, we see a lot of scale when it comes to gaming and commerce. We’ve also had very significant scale in dating and travel.

Many of these are very performance-driven campaigns, and we see a lot of advertisers who have very specific downstream metrics and post-install events being able to scale very well with Snap. In commerce, deeplinking has been very successful for specific sales, either to another app or an external website.

Koetsier: What are the common characteristics of the best marketers — top 10% marketers — that you’ve seen?

Lyall: The Snapchat Ads self serve ads manager tool is used by a wide range of marketers and advertisers based on budget size, business category and needs.

To speak more towards the relevancy of this blog post, app advertisers have shown really significant adoption and success in the ads manager’s short history. Our most successful UA managers who generate app installs from Snapchat exemplify a heavy quantitative and non-biased approach towards their ad partners and constantly iterate and test on a wide range of campaign variables.

They also are able to understand that while the quantitative optimization is one piece of the puzzle, the creatives and ad formats are much more difficult to quantify and take a scientific approach to how they evaluate creatives and ad units. Overall, a tireless effort from UA advertisers who are willing to get their hands dirty and optimize campaigns and constantly iterate and test.

Koetsier: Thank you for your time!

. . .

. . .

To learn more about becoming a top 10% marketer, get a copy of our Scaling Mobile Growth report. We analyzed over $10B in ad spend and a trillion ad impressions to learn what the best marketers are doing, and are sharing the insights with you.

What online marketers and ad fraud criminals do and don’t have in common

The recent news about the Department of Justice’s takedown of the code-named 3ve and Methbot ad fraud schemes, including the arrest of three individuals and the indictment of five more, is cause for celebration.

A coordinated effort over several years from the FBI, White Ops, Google and many others shut down a hefty chunk of the $19 billion that Juniper Research estimates will be stolen this year by digital ad fraudsters.

Not only did this operation save advertisers millions in useless spending, the criminal indictment could deter smart, creative individuals from getting into the fraud business in the first place. U.S. law enforcement now has the chops to take down these white collar criminals operating in faraway places like Russia, Bulgaria, and perhaps living it up in Malaysia, where Sergey Osyannikov, one of the defendants in this case, was arrested.

Fraud makes life difficult for everyone.

In a recent survey of 1,100 advertisers by Singular, we asked: “What are the impacts of not having good marketing intelligence about your ad campaigns?”

The #1 answer?

Poor quality traffic, mentioned by 57% of advertisers. The #2 answer was high fraud, mentioned by 50% of advertisers. What we don’t know is how much of poor quality traffic is attributable to fraud, but I’m guessing a good chunk of it is.

Reading the official indictment document (pictured above) as well as the White Ops whitepaper and news coverage offers insight into the practices and mindset of these persistent and creative individuals who managed to collect an estimated $29 million from one scheme and $7 million from another.

As someone new to Singular, which offers built-in fraud protection for marketers, and who’s spent the last 6 years covering HR and recruiting topics for Simply Hired, Glassdoor, and Lever, I couldn’t help but look at the human side of how these people operated, and consider what we can learn from them.

Fraudsters are perhaps the most successful growth managers—that is, until they get caught.

Here’s an assessment of what marketers and fraudsters do and don’t have in common.

Similarity #1: Think broadly and creatively

These criminals took a comprehensive approach to create their fraudulent networks, looking at every parameter of cybersecurity requirements in order to build networks that would go undetected.

The malware they created that was installed on 700,000 computers at any given time opened hidden windows on hidden desktops in order to go undetected by users. Their bots simulated mouse moves across on tens of thousands of spoofed domains and sent fake audiences to real domains. They also make sure that the malware was installed on computers in countries that were in demand. In short, they considered everything.

Marketers today have to think broadly about their campaigns: what money is being spent where, which creatives are working and why, and consider the marketplace dynamics at play. They use their creativity to find new sources, adjust campaigns, and relentlessly pursue growth.

Similarity #2: Collaborate and assign clear roles

The investigation revealed the roles and responsibilities of each of the eight men. There were several programmers, and several who ran the business side and controlled the funds. Whether you’re a legitimate marketer or a fraudster, it takes a village of specialists to scale an operation.

From the press release:

“3ve was remarkably sophisticated,” added Tamer Hassan, CTO of White Ops. “It showed every indication of a well-organized engineering operation with best practices in software development. It exhibited reliability, resilience and scale, rivaling many state-of-the-art software architectures.”

Interestingly, the collaboration tools they used were pretty similar to the ones used by marketers: spreadsheets in the cloud. (Fortunately, they will never have the benefit of a marketing intelligence platform like Singular that serves as a single source of truth around business results.)

Similarity #3: When you have a good thing, keep it going

These schemes ran for years, detected only by the investigators.

It was their Candy Crush Saga, a top-grossing app of all time that they kept optimizing—until their time was up. While it’s unfortunate that so many advertiser dollars were spent on fraudulent traffic, the law enforcement long game ensured the networks would be shut down for good and at least some of the fraudsters could be caught.

Twenty organizations, including Google, Microsoft, Amazon and Adobe donated resources to take down the scheme. Consider the ad dollars spent as donations to fighting crime.

Similarity #4: Retaliation will get you fired

ZDNet coverage of the Zhukov arrest says that “Zhukov exposed his operation during a fit of rage after a deal with a customer went wrong, and he turned up all his servers against that customer’s video inventory, generating millions of views, and indirectly catching the eye of advertising networks.”

It can be difficult to hold down a job if you have an anger management problem. But instead of just moving on to the next gig, Zhukov faces a maximum penalty of 20 years in prison.

Difference #1: You can be proud of your profession

These men have friends, families, partners, and spouses—all to whom they have to lie about what they do for a living. While it may be difficult to explain your occupation to those who don’t work in the industry, it’s far less pressure than having to blatantly lie.

Not only that, as a legitimate marketer you have a wealth of resources and tools such as Singular to support you, and you don’t have to manage your business in cloud-based spreadsheets.

Difference #2: You can spend your bonuses guilt-free

The 3ve defendants were indicted on two counts of money laundering, one for each scheme. It takes a lot effort to conceal large sums of money across nations.

While you probably don’t get to reap millions for your the work you do, at least you can spend your bonus guilt-free on whatever you want, whether it’s an exotic trip or home renovation.

Difference #3: Your work creates happy users, not ad fraud victims

At the end of the day, it’s nice to know that your work to acquire more customers results in moments of joy, satisfaction, or productivity as they consume your company’s product.

The 3ve defendants left a trail of victims: thousands who work in the online industry, and millions whose computers were affected. As a marketer, it’s gratifying to read this list of victims shown in the indictment:

At Singular, we’re proud to say that by offering ad fraud prevention, we’re doing our part to help fight crime.

If fewer advertisers spend on fraudulent sites, the less motivated individuals like these men will be to waste their talents working in fraud. After all, they just might end up in jail with Aleksandr Zhukov, Yevgeniy Timchenko, and Sergey Ovsyannikov.

Request a demo today to learn how our fraud prevention suite improves ROI by reducing spending on fraud.

Scaling mobile growth: How smart marketers pay 37% less and get 60% more

The cold hard reality of mobile marketing is that the rich get richer and the smart get smarter. That sounds unfair, but there is a sunny side up: nearly every mobile marketer has a shot at success.

But achieve breakout mobile growth isn’t easy.

Among other things, it requires prioritizing what already know you should be doing, but aren’t.

Cold hard data on mobile growth: what we’re seeing

Over the past year our customers used Singular to optimize more than $10 billion in annual ad spend. That includes over a trillion ad impressions, billions of conversion events, and hundreds of millions of app installs.

And it shows us that some marketers are vastly outperforming others.

The average mobile marketer achieving average mobile growth uses just a few ad partners: typically ones whose names your parents would know. That’s not a bad thing: those massive media sources are used by billions of people every single day. They have unparalleled audience and reach, which every mobile marketer will likely need.

But it’s definitely suboptimal to only dance with the big boys and girls of advertising. When you look at the average cost to onboard new mobile users, for example, mobile marketers using five or fewer ad partners pay $3.58. Marketers using six or more average just $2.24.

That’s a big difference. And it means that for the same $100,000 ad spend, top mobile marketers achieve 44,643 app installs … while others get only 27,933. Over a year’s worth of marketing, that’s well over half a million potential new users in your app versus just 335,000.

That’s massive competitive advantage. And at an example $10 LTV, it’s over $2 million in extra revenue that top mobile growth experts bring in.

Which, of course, is additional fuel for even more growth.

Most marketers know what they need to do

The results above are based on hard data … actual data on spend and performance and conversions. But hard data like this doesn’t tell us something very important: why marketers are doing what they’re doing.

Or, of course, why they’re not.

So we surveyed over 900 marketers who run ad campaigns, and what we found is that marketers who fail to create mobile growth don’t fail because they they don’t know what to do — at least at the macro level. Instead, they fail because they don’t know how to accomplish what they need to do effectively, at scale, while avoiding fraud.

Most marketers — 60% of them — understand that in order to access significant growth, they need to add media sources, or ad partners. The problem is that scaling is tough. In particular, scaling beyond known safe channels is dangerous.

Marketers know and trust just a few name-brand media sources. Going beyond those entails serious risk: from complexity, fraud, management, knowledge/skills gap, and more.

As a result, most marketers simply try harder with partners they already know. Even though they believe that the best way to grow is by adding more ad networks, they turn to optimizing with existing, known networks instead of experimentation with new, unproven options.

Optimization is not bad. In fact, it’s a critical part of success.

But when marketers are optimizing on only a very limited subset of possible partners, they’re reducing their chances of bigger-picture success. Getting small incremental wins is great, but opening up entirely new veins of fast growth is better.

Most of this problem is simply due to lack of needed tools for scaling growth safely and profitably via marketing intelligence. Essentially, the price growth marketers pay for the lack of marketing intelligence is sub-optimal growth.

How to unlock breakthrough mobile growth

We’ve talked to the top echelon of mobile marketers who are achieving outsized growth. And we know how they’re doing it.

Download the full free report Scaling Mobile Growth report to get the answers, including:

  • 4 critical levers that top growth marketers optimize
  • 3 key ways top marketers achieve smart insights on growth opportunities
  • 3 toolsets top growth marketers use to run their campaigns
  • 7 ad partners who are delivering outsized returns

Testing and trying more marketing options improves results. It lowers costs, and it increases conversions. It’s what all marketers instinctively know, but it’s also hard.

And it does come with more risk.

With the right tool, however, marketers can understand what’s happening. Measure it. Analyze it for results. And use strategies and insights that allow them to beat the market … achieving significantly great results for less cost.

That translates directly to competitive benefit. And, ultimately, to faster growth.

Get the full report on how to get started.

Using attribution data to calculate mobile ads LTV

Eric Benjamin Seufert is the owner of Mobile Dev Memo, a popular mobile advertising trade blog. He also runs Platform and Publishing efforts at N3TWORK, a mobile gaming company based in San Francisco, and published Freemium Economics, a book about the freemium business model. You can follow Eric on Twitter.

Note: if you’re looking for ad monetization with perhaps less effort than Eric’s method below, talk to your Singular customer service representative (and stay tuned for additional announcements).

Various macro market forces have aligned over the past two years to create the commercial opportunity for app developers to generate significant revenue from in-app advertising. New genres like hypercasual games and even legacy gaming genres and non-gaming genres have created large businesses out of serving rich media video and playable ads to their users by building deep, sophisticated monetization loops that enrich the user experience and produce far less usability friction than some in-app purchases.

But unfortunately, while talented, analytical product designers are able to increase ad revenues with in-game data by deconstructing player behavior and optimizing the placement of ads, user acquisition managers have less data at their disposal in optimizing the acquisition funnel for this type of monetization. Building an acquisition pipeline around in-app ads monetization is challenging because many of the inputs needed to create an LTV model for in-app ads are unavailable or obfuscated. This is evidenced in the fact that a Google search for “mobile app LTV model” yields hundreds of results across a broad range of statistical rigor, but a search for “mobile app ads LTV model” yields almost nothing helpful.

Why is mobile ads LTV so difficult to calculate?

For one, the immediate revenue impact of an ad click within an app isn’t knowable on the part of the developer and is largely outside of their control. Developers get eCPM data from their ad network partners on a monthly basis when they are paid by them, but they can’t really know what any given click is worth because of the way eCPMs are derived (ad networks usually get paid for app installs, not for impressions, so eCPM is a synthetic metric).

Secondly, app developers can’t track ad clicks within their apps, only impressions. So while a developer might understand which users see the most ads in their app and can aggregate that data into average ad views per day (potentially split by source), since most ad revenue is driven by the subsequent installs that happen after a user clicks on an ad, ad view counts alone don’t help to contribute to an understanding of ads LTV.

Thirdly, for most developers, to borrow conceptually from IAP monetization, there are multiple “stores” from which ad viewing (and hopefully, clicking) users can “purchase” from: each of the networks that an app developer is running ads from, versus the single App Store or Google Play Store from which the developer gathers information. So not only is it more onerous to consolidate revenue data for ads, it also further muddies the monetization waters because even if CPMs for various networks can be cast forward to impute revenue, there’s no certainty around what the impression makeup will look like in an app in a given country on a go-forward basis (in other words: just because Network X served 50% of my ads in the US this month, I have no idea if it will serve 50% of my ads in the US next month).

For digging into problems that contain multiple unknown, variable inputs, I often start from the standpoint of: If I knew everything, how would I solve this? For building an ads LTV model, a very broad, conceptual calculation might look like:

What this means is: for a given user who was acquired via Channel A, is using Platform B, and lives in Geography C, the lifetime ad revenue they are expected to generate is the sum of the Monthly Ad Views we estimate for users of that profile (eg. Channel A, Platform B, Geography C) times the monthly blended CPM of ad impressions served to users of that profile.

In this equation, using user attribution data of the form that Singular provides alongside internal behavioral data, we can come up with Lifetime Ad Views broken down by acquisition channel, platform, and geography pretty easily: this is more or less a simple dimensionalized cumulative ad views curve over time that’d be derived in the same way as a cumulative IAP revenue curve.

But the Blended CPM component of this equation is very messy. This is because:

  • Ad networks don’t communicate CPMs by user, only at the geo level; [Editorial note: there is some significant change happening here; we will keep you posted on new developments.]
  • Most developers run many networks in their mediation mix, and that mix changes month-over-month;
  • Impression, click, and video completion counts can be calculated at the user level via mediation services like Tapdaq and ironSource, but as of now those counts don’t come with revenue data.

Note that in the medium-term future, many of the above issues with data availability and transparency will be ameliorated by in-app header bidding (for a good read on that topic, see this article by Dom Bracher of Tapdaq). In the meantime, there are some steps we can take to back into reasonable estimates of blended CPMs for the level of granularity that our attribution data gives us and which is valuable for the purposes of user acquisition (read: provides an LTV that can be bid against on user acquisition channels).

But until that manifests, user acquisition managers are left with some gaps in the data they can use to construct ads LTV estimates. The first glaring gap is the network composition of the impression pool: assuming a diverse mediation pool, there’s no way to know which networks will be filling what percentage of overall impressions in the next month. And the second is the CPMs that will be achieved across those networks on a forward-looking basis, since that’s almost entirely dependent on whether users install apps from the ads they view.

The only way to get around these two gaps is to lean on historical data as a hint at what the future will look like (which violates a key rule of value investing but is nonetheless helpful in forming a view of what’s to come). In this case, we want to look at past CPM performance and past network impression composition for guidance on what to expect on any given future month.

Estimating mobile ads LTV in Python

To showcase how to do that, we can build a simple script in python, starting with the generation of some random sample data. This data considers an app that is only serving ads to users from Facebook, Unity, and Vungle in the US, Canada, and UK:

[code]
import pandas as pd
import matplotlib
import numpy as np
from itertools import product
import random

geos = [ 'US', 'CA', 'UK' ]
platforms = [ 'iOS', 'Android' ]
networks = [ 'Facebook', 'Unity', 'Applovin' ]

def create_historical_ad_network_data( geos, networks ):
 history = pd.DataFrame(list(product(geos, platforms, networks)),
 columns=[ 'geo', 'platform', 'network' ])

 for i in range( 1, 4 ):
 history[ 'cpm-' + str( i ) ] = np.random.randint ( 1, 10, size=len( history ) )
 history[ 'imp-' + str( i ) ] = np.random.randint( 100, 1000, size=len( history ) )
 history[ 'imp-share-' + str( i ) ] = history[ 'imp-' + str( i ) ] / history[ 'imp-' + str( i ) ].sum()

 return history

history = create_historical_data(geos, networks)
print(history)
[/code]

Running this code generates a Pandas DataFrame that looks something like this (your numbers will vary as they’re randomly generated):

[code / table]
geo platform network cpm-1 imp-1 imp-share-1 cpm-2 imp-2 \
0 US iOS Facebook 2 729 0.070374 9 549 
1 US iOS Unity 7 914 0.088232 3 203 
2 US iOS Applovin 7 826 0.079737 4 100 
3 US Android Facebook 2 271 0.026161 2 128 
4 US Android Unity 5 121 0.011681 9 240 
5 US Android Applovin 6 922 0.089005 9 784 
6 CA iOS Facebook 2 831 0.080220 9 889 
7 CA iOS Unity 8 483 0.046626 5 876 
8 CA iOS Applovin 7 236 0.022782 9 642 
9 CA Android Facebook 8 486 0.046916 4 523 
10 CA Android Unity 1 371 0.035814 5 639 
11 CA Android Applovin 8 588 0.056762 7 339 
12 UK iOS Facebook 2 850 0.082054 8 680 
13 UK iOS Unity 7 409 0.039483 3 310 
14 UK iOS Applovin 1 291 0.028092 5 471 
15 UK Android Facebook 7 370 0.035718 6 381 
16 UK Android Unity 3 707 0.068250 6 117 
17 UK Android Applovin 3 954 0.092094 3 581

imp-share-2 cpm-3 imp-3 imp-share-3 
0 0.064955 8 980 0.104433 
1 0.024018 4 417 0.044437 
2 0.011832 3 157 0.016731 
3 0.015144 7 686 0.073103 
4 0.028396 3 550 0.058610 
5 0.092759 8 103 0.010976 
6 0.105182 1 539 0.057438 
7 0.103644 6 679 0.072357 
8 0.075958 5 883 0.094096 
9 0.061879 1 212 0.022592 
10 0.075603 8 775 0.082587 
11 0.040109 6 378 0.040281 
12 0.080454 6 622 0.066283 
13 0.036678 8 402 0.042839 
14 0.055726 7 182 0.019395 
15 0.045078 2 623 0.066390 
16 0.013843 2 842 0.089727 
17 0.068741 1 354 0.037724
[/code]

One thing to consider at this point is that we have to assume, on a month-to-month basis, that any user in any given country will be exposed to the same network composition as any other user on the same platform (that is, the ratio of Applovin ads being served to users in the US on iOS is the same for all users of an app in a given month). This almost certainly isn’t strictly true, as, for any given impression, the type of device a user is on (eg. iPhone XS Max vs. iPhone 6) and other user-specific information will influence which network fills an impression. But in general, this assumption is probably safe enough to employ in the model.

Another thing to point out is that retention is captured in the Monthly Ad Views estimate that is tied to source channel. One common confusion in building an Ads LTV model is that there are ad networks involved in both sides of the funnel: the network a user is acquired from and the network a user monetizes with via ads served in the app. In the construction of our model, we capture “user quality” in the Monthly Ad Views component from Part A, which encompasses retention in the same way that a traditional IAP-based LTV curve does. So there’s no reason to include “user quality” in the Part B of the equation, since it’s already used to inform Part A.

Given this, the next step in approximating Part B is to get a historical share of each network, aggregated at the level of the Geo and Platform. Once we have this, we can generate a blended CPM value at the level of Geo and Platform to multiply against the formulation in Part A (again, since we assume all users see the same network blend of ads, we don’t have to further aggregate the network share by the user’s source network).

In the below code, the trailing three-month impressions are calculated as a share of the total at the level of Geo and Platform. Then, each network’s CPM is averaged over the trailing three months and the sumproduct is returned:

[code]
history[ 'trailing-3-month-imp' ] = history[ 'imp-1' ] + history[ 'imp-2' ] + history[ 'imp-3' ]

history[ 'trailing-3-month-imp-share' ] = history[ 'trailing-3-month-imp' ] / history.groupby( [ 'geo', 'platform' ] )[ 'trailing-3-month-imp' ].transform( sum )

history[ 'trailing-3-month-cpm' ] = history[ [ 'cpm-1', 'cpm-2', 'cpm-3' ] ].mean( axis=1 )

blended_cpms = ( history[ [ 'trailing-3-month-imp-share', 'trailing-3-month-cpm' ] ].prod( axis=1 )
 .groupby( [ history[ 'geo' ], history[ 'platform' ] ] ).sum( ).reset_index( )
)

blended_cpms.rename( columns = { blended_cpms.columns[ len( blended_cpms.columns ) - 1 ]: 'CPM' }, inplace = True )

print( blended_cpms )
[/code]

Running this snippet of code should output a DataFrame that looks something like this (again, the numbers will be different):

[code]
geo platform CPM
0 CA Android 5.406508
1 CA iOS 4.883667
2 UK Android 4.590680
3 UK iOS 5.265561
4 US Android 4.289083
5 US iOS 4.103224
[/code]

So now what do we have? We have a matrix of blended CPMs broken out at the level of Geo and Platform (eg. the CPM that Unity Ads provides for US, iOS users) — this is Part B from the equation above. The Part A from that equation — which is the average number of ad views in a given month that we expect from users that match various profile characteristics pertaining to their source channel, geography, and platform — would have been taken from internal attribution data mixed with internal app data, but we can generate some random data to match what it might look like with this function:

[code]
def create_historical_one_month_ad_views( geos, networks ):
 ad_views = pd.DataFrame( list( product( geos, platforms, networks ) ), 
 columns=[ 'geo', 'platform', 'source_channel' ] )
 ad_views[ 'ad_views' ] = np.random.randint( 50, 500, size=len( ad_views ) )
 
 return ad_views

month_1_ad_views = create_historical_one_month_ad_views( geos, networks )
print( month_1_ad_views )
[/code]

Running the above snippet should output something like the following:

[code]
geo platform source_channel ad_views
0 US iOS Facebook 73
1 US iOS Unity 463
2 US iOS Applovin 52
3 US Android Facebook 60
4 US Android Unity 442
5 US Android Applovin 349
6 CA iOS Facebook 279
7 CA iOS Unity 478
8 CA iOS Applovin 77
9 CA Android Facebook 479
10 CA Android Unity 120
11 CA Android Applovin 417
12 UK iOS Facebook 243
13 UK iOS Unity 306
14 UK iOS Applovin 52
15 UK Android Facebook 243
16 UK Android Unity 106
17 UK Android Applovin 195
[/code]

We can now match the performance data from our user base (gleaned using attribution data) with our projected CPM data to get an estimate of ad revenue for the given month with this code:

[code]
combined = pd.merge( month_1_ad_views, blended_cpms, on=[ 'geo', 'platform' ] )
combined[ 'month_1_ARPU' ] = combined[ 'CPM' ] * ( combined[ 'ad_views' ] / 1000 )

print( combined )
[/code]

Running the above snippet should output something like the following:

[code]
geo platform source_channel ad_views CPM month_1_ARPU
0 US iOS Facebook 73 5.832458 0.425769
1 US iOS Unity 463 5.832458 2.700428
2 US iOS Applovin 52 5.832458 0.303288
3 US Android Facebook 60 5.327445 0.319647
4 US Android Unity 442 5.327445 2.354731
5 US Android Applovin 349 5.327445 1.859278
6 CA iOS Facebook 279 6.547197 1.826668
7 CA iOS Unity 478 6.547197 3.129560
8 CA iOS Applovin 77 6.547197 0.504134
9 CA Android Facebook 479 4.108413 1.967930
10 CA Android Unity 120 4.108413 0.493010
11 CA Android Applovin 417 4.108413 1.713208
12 UK iOS Facebook 243 4.626163 1.124158
13 UK iOS Unity 306 4.626163 1.415606
14 UK iOS Applovin 52 4.626163 0.240560
15 UK Android Facebook 243 5.584462 1.357024
16 UK Android Unity 106 5.584462 0.591953
17 UK Android Applovin 195 5.584462 1.088970
[/code]

That last column — month_1_ARPU — is the amount of ad revenue you might expect from users in their first month, matched to their source channel, their geography, and their platform. In other words, it is their 30-day LTV.

Putting it all together

Hopefully this article has showcased the fact that, while it’s messy and somewhat convoluted, there does exist a reasonable approach to estimating ads LTV using attribution and ads performance data. Taking this approach further, one might string together more months of ad view performance data to extend the limit of the Ads LTV estimate (to month two, three, four, etc.) and then use historical CPM fluctuations to get a more realistic estimate of where CPMs will be on any given point in the future (for example, using a historical blended average doesn’t make sense in the run-up to Christmas, when CPMs spike).

The opportunities and possibilities for making money via rich ads at this point of the mobile cycle are exciting, but they don’t come without new challenges. In general, with the way the mobile advertising ecosystem is progressing towards algorithm-driven and programmatic campaign management, user acquisition teams need to empower themselves with analytical creativity to find novel ways to scale their apps profitably.

. . .

. . .

Next: Get the full No-BS Guide to Mobile Attribution, for free, today.