ROI analysis Archives

Why Singular Is The Only MMP Integrated To Twitter’s Ads API

Intelligent data that drives insights for growth requires three key ingredients:

Accuracy
Granularity
Actionability

In order to obtain all three ingredients, you need to ensure the reliability of API integrations with each of your marketing platforms. This is where you find the Singular difference. Singular is the only measurement partner to have two separate API integrations with Twitter, along with over 1,000 additional marketing platforms, providing you the most comprehensive solution for ROI down to the creative level.

This is what we call “dual integration.”

WTH is the Dual Integration approach?

Before you can understand the importance of API integrations (and dual integrations) you first should understand the type of data you need to collect in order to have anything meaningful for your campaign optimization efforts. Simply put, there are two key data sets you need to collect from your marketing platform, whether that is from Twitter, Snapchat, Pinterest, Facebook, Google, Vungle, Unity, Amazon: you name it.

First, you need your campaign analytics data (aka pre-install data) to answer questions like:

“How much did I spend on this campaign?”
“How many impressions did that creative get?”
“How many clicks came from each publisher?”

Second, you need your attribution data (aka post-install data) to answer questions like:

“How many installs did that campaign generate?”
“What was the revenue on this creative asset?”
“How many people went to level two as a result of this keyword?”

Only by combining these two datasets can you really know your ROI by campaign, by creative, by keyword, and by individual ad. This gives you the power to optimize at the most granular as well as aggregate levels, providing your best opportunity to maximize profitability.

Singular’s dual integration visualized

To do this manually, you would need to standardize the hierarchies (some sources offer only campaign and ad level, while others go right down to the keyword) and the taxonomies (names and terms differ) across every source, and then calculate your ROI by each dimension … every single time you need it.

Sounds like a pain in the @$$?

Good thing Singular has already done it for you!

This is the dual integration approach

Singular has spent years building API integrations for both sides of the puzzle across over 1,000 additional marketing platforms, and automatically combines this data to show you ROI at the most granular levels.

Unlike other analytics platforms who are only accountable for your “pre-install data” or other attribution providers who are only accountable for your “post-install data,” Singular is accountable for both. Which is why we are the only Twitter measurement partner to have integrations that collect BOTH datasets, just as we do for hundreds of other marketing platforms: so we can do dual integration for you, out of the box.

Inherent flaws with tracking links

You might be asking: So why can’t I just use tracking links to collect this data? My attribution provider uses tracking links and says they can do campaign ROI.

Great question! While the tracking link is the easiest way to collect the necessary macros for a given network, this method has some inherent flaws.

It is not retroactive
You are only receiving data at the time of the click, therefore if the numbers reconcile after the time of the click, this will not be reflected in your reporting.
Not all networks support passing all macros
For example, you might be able to receive campaign cost and clicks, but you may not get site ID or publisher ID.
No creative assets!
Singular is the only solution on the market to provide you the most complete reporting of your creative asset ROI across the most visual networks. However, creative assets and their performance can only be reported by an API integration.
Data loss and discrepancy is HIGH
In a recent study, we compared a number of customers who were using Singular along with a third-party attribution provider. In observing their “campaign data” collected via our API integration against the same data set collected via the tracking link by the third-party attribution provider, we saw a 31% discrepancy … with the numbers reported from our API integration matching identically to the number on the final bill.

Of course, we too sometimes rely on the tracking link for those marketing platforms that do not offer an API to collect campaign analytics. However, in the rare case that we cannot collect data via an API, we will also rely on alternate integration methods to ensure accuracy of the data.

For example, a daily email report, or a CSV file upload to an S3 bucket.

We understand every marketer is different, and how you look at your data may be completely different from your competitors. We are flexible and here to ensure the data you see in Singular matches your internal systems.

Heck, we even have a bi-directional API to push and pull data to your source of truth.

To learn more about Singular’s “Dual Integration Approach” and the Singular difference, contact us to request a demo today.

Already a Singular customer and looking to take advantage of our dual integration with Twitter? Check out the help center for details on how to configure your Twitter integration.

Jam City optimizes campaign performance and creative strategy with Singular

We got to sit down with Jam City’s VP of User Acquisition Marketing, Brian Sapp, to discuss how his team is using Singular to optimize campaign performance and improve their creative strategy. Watch our discussion on Jam City’s use of campaign analytics below!

Video

Transcription

Introduction

My name is Brian Sapp. I’m the VP of UA (User Acquisition) Marketing at Jam City. Jam City is one of the leading mobile casual gaming developers in the West. We have a fairly large portfolio of games. Some of our tentpole games are Cookie Jam and Panda Pop. But recently we launched Harry Potter Hogwarts Mystery, [and] that game has been a big success.

Connecting fragmented marketing data

Singular right now we use to ingest data from all of our ad networks and as you can imagine, when we spend with over 40 networks, that’s a lot of data. The manpower it would take to go into each network and pull in that API, do the work, or pull it in manually, would be extremely time-consuming.

Singular solves that for us, solves it in a much faster time, and more efficiently than we could do it ourselves. And then, having that data in the dashboard, especially for someone like me who’s spending across 8-10 titles, we have a massive portfolio, the dashboard really gives us the ability to easily pivot that data whether it be by spend, by channel, by paid installs, by tracker installs, impressions, it’s very, very useful, as well as creative. Having all of the data, especially creative data, plus images, plus the data behind the creative, in one dashboard is extremely valuable for us.

Aligning with Creative Product Marketing

So we actually have a team called Creative Product Marketing that focused on our creative roadmap/creative strategy and they’re using Singular to look at our performance by game, by channel, and right now it’s the fastest, easiest way we have to do that across all the different data sources.

Next-level Performance Analytics

Singular collects a lot of the ad network data for us and we’re using that to look at CPI by campaign, CPI by geo, paid performance, scale, spend, organics versus paid installs. And so we’re also ingesting data from our attribution partner which allows us to kind of marry the two, and so we get very granular on Singular’s reporting for whatever questions we have.

I use it for executive reports, building massive pivot charts, visuals that I want to see across the portfolio. The combination of our internal tools plus Singular really gives me everything I need.

Ready to take your growth marketing to the next level? Let’s connect!

Nexon transforms marketing data into granular performance insights with Singular

We got the chance to catch up with Nexon‘s Director of Acquisition, Warren Woodward, and discuss how his team uses Singular to analyze and optimize the performance of their sophisticated user acquisition program. One topic we covered was how challenging it can be to analyze the performance of ad creatives, especially when advertising multiple titles across many ad partners. Thankfully, Nexon overcame this challenge. 😊Check out our discussion below!

Video

Transcription

Introduction

My name is Warren Woodward, and I’m with Nexon. I’m the Director of User Acquisition. Nexon is an international gaming company. We have games for PC and mobile across the world. We have a couple of major IPs, such as Maple Story, and the biggest video game series in China, Dungeon and Fighter.

How Singular fits in Nexon’s growth stack

At Nexon, we leverage Singular in a few different ways. We use it as our source of truth. We also use it as a way to join data from multiple sources for day-to-day campaign optimization. Singular for us is a great way to look at all that data in one place. And for me as Director to look in on everything that everyone is doing and see it all in an apples-to-apples comparison to understand how to best move budget around.

Reaching growth goals

Singular helps us reach our growth goals ultimately because it saves us time in any number of ways. With the number of titles that we’re buying for, we don’t have the time to go into every separate source, every separate title, and look at it all à la carte. So the ways that we can slice and dice, and stack rank what’s important to us to see what’s giving us the highest ROI… everything across the game, where are we spending the most, what’s bleeding, let’s us judge how we can use a finite number of hours in the day to get the most impact to our campaigns.

Singular let me see it all in one place, slice and dice it. If I just want to see all of the performance for any given country or OS, or maybe just dive really deep to a particular ad network. It’s nice to have one dashboard that can quickly make all these ad-hoc reports. And also to do a creative analysis and collaborate with the creative team.

Perfecting storytelling

So one of the ways that we use this on a daily basis is to get our creative team talking about performance, the same way that we do; to be looking at a creative level across the dozens of creatives who might have any given time for a game. Understand the trends that are working. It helps us identify what we want to make next, what’s really working, and we start seeing a better return on investment for the creatives themselves when we can see how they’ve been performing in the ecosystem.

Ready to take your growth marketing to the next level? Let’s connect!

N3TWORK optimizes marketing spend and aligns internal teams with Singular [Video]

We recently had the privilege of sitting down with Nebojsa Radovic, Director of Performance Marketing at N3TWORK, to talk about how his team is leveraging Singular’s Marketing Intelligence Platform to optimize marketing spend and align their internal teams with a single source of truth for marketing performance. Check out our discussion below!

Transcription

Introduction

Hi, my name is Nebojsa Radovic, but most people in the industry know me as Nebo. I’m a Director of Performance Marketing at a company called N3TWORK, which is the developer behind Legendary Game of Heroes.

How Singular fits in N3TWORK’s growth stack

We’re currently using Singular pretty much as our marketing stack. We’re using [Singular] as both an attribution and cost aggregation partner. We’re getting spend data from our ad partners and at the same time, we’re getting user-level data that tells us where the installs are coming from.

The importance of granular performance insights

Singular was very important for us simply because it unlocked certain opportunities that we were not having before. Think about optimizing at the publisher and creative level, combining those two, and trying to spend more dollars in the places where it makes sense. Without that data, it’s pretty much impossible to extrapolate manually and figure out what’s working and what’s not.

Let’s say if you’re trying to scale your spending from $100,000 a month to $10 million a month. You really need very granular, accurate data that comes in on time… When you’re spending a few thousand dollars a day, maybe you don’t need granular data. But if you’re trying to scale the spend across different channels and different geos you really need accurate and granular data to be able to do that. So thank you Singular!

Aligning internal teams with a single source of truth

Singular helped us a lot with aligning the Finance team with the User Acquisition team. And within the User Acquisition team, we have a media buying team, data analytics team and marketing creative [team]. All four teams, Finance and the three sub-teams of the marketing team are looking closely into the Singular data and trying to understand how to do their jobs better.

In particular, Finance is estimating what the spend is at the end of the month or week, whatever is that time period we’re looking at, and they’re estimating what the payback windows are going to be and the financial health of the company at a very high level. At the same time on the User Acquisition side, we’re just trying to make better buy decisions by using Singular data. This is crucial to do this job successfully.

Ready to take your growth marketing to the next level? Let’s connect!

Singular ROI Index 2019: The unmissable advertising ROI webinar

Singular’s ROI Index is the largest study that ranks top ad networks globally based on their ability to deliver ROI for advertisers. We’ve already published the Index and made it available to the world, giving you the ability to find the best advertising ROI available.

But now it’s time to dig deeper.

This webinar goes beyond the Index to talk about not only where individual media sources rank, but also what some of the key differentiators are.

Meet the experts

To do that, we’re going to bring in the experts: Susan Kuo, Brian Sapp, and Christen Luciano. (Yours truly, John Koetsier, VP of Insights at Singular, will moderate.)

Susan and Christen have deep insight into how various ad partners performed in the Index. Brian has an even deeper insight into what mobile marketers look for, and what they need in terms of advertising ROI from ad networks.

Susan Kuo
COO, Head of Business Development
Susan has an extensive background in mobile ad tech, analytics, and gaming. Prior to Singular, Susan held senior leadership roles at Onavo and InMobi. Susan is an active member of the mobile community and serves on the advisory board for several mobile-focused start-ups.

Brian Sapp
VP, User Acquisition Marketing, Jam City
A mobile veteran with previous roles at Tapjoy and Web Games, Brian manages user acquisition for Jam City, which currently has six of the top 100 highest-grossing games across the App Store and Google Play.

Christen Luciano
Director of Partner Development
Christen oversees Singular’s relationships with key partners. Prior to Singular, she was a product marketing manager with Kenshoo and held multiple additional marketing roles. Her focus is collaborating with top marketing platforms to help advertisers grow reach and maximize performance.

We’ll review the 2019 Singular ROI Index, but also talk about fraud, things marketers need to know about their ad campaigns, some of the biggest surprises, and the role SANs (self-attributing networks like Facebook and Google) should play in marketers’ ad campaigns alongside some of the mid-tier players.

Advertising ROI is critical, of course, but it doesn’t happen in a vacuum.

So we’ll also talk about how to find niches of profitable growth, new innovative players, and what to look out for.

One of the things that the 2019 Singular ROI Index makes very clear is that Snap and Twitter have made significant moves recently in terms of the value they offer to advertisers. We’ve seen that in their recent quarterly reports: Snap grew quarterly revenue almost $100 million year over year, and Twitter had record quarterly earnings.

We’ll talk about what we’re seeing in the platforms that is driving increased advertiser adoption, and we’ll talk about everything else the Index reveals about advertising ROI.

Sign up now for the ROI Index webinar.

Introducing global-first Cross-Device, Cross-Platform ROI analytics

How do you grow ROI while maintaining CPA and scale?

This is a question marketers face every day. And answering this question has become more complex as they advertise on more platforms across more devices than ever before. When conversions happen, it’s a struggle to connect the dots and understand what caused them.

Back when Singular was founded in 2014, we focused on solving this challenge first for the complex, highly fragmented, mobile ecosystem: providing a single solution that automatically collects and combines spend data and conversion data to expose mobile marketing performance, including ROI, at unrivaled levels of granularity.

That is powerful. And we quickly became the de facto solution for unifying campaign analytics and mobile attribution to expose ROI.

But in 2019, the game is different

Top brands advertise over a wide range of platforms to users on multiple devices. A customer may see an advertisement for a product on her desktop, and later buy that product on her mobile app. With today’s analytics, it’s hard to connect the two experiences and measure the customer journey accurately.

For mobile-first brands, this often leads to two separate teams, one web, one mobile app, using different tools, and even different metrics, to measure the customer journey. For web-first brands, it results in limited investment in mobile apps, preventing them from diversifying their marketing efforts to bring in incremental users, leaving untapped growth potential on the line.

Moreover, inaccurate measurement leads to misguided decision-making. Matter of fact, poor data quality costs brands an average of $15 million annually, according to Gartner. Making an investment and creative decisions with inaccurate and incomplete datasets is just plain costly.

In true Singular spirit, we sought to solve this new challenge for our customers so they can drive growth more effectively and efficiently in this multichannel world. And I’m happy to say that we have leveraged our vast experience in attribution and marketing analytics to do just that.

Cross-device, cross-platform attribution

Today, Singular is announcing the first-ever cross-platform and cross-device ROI analytics solution for growth marketers.

With the release of Cross-Device Attribution, Singular’s Marketing Intelligence Platform connects marketing spend data to conversion results across devices and platforms. First, we ingest granular spend and marketing data from thousands of sources. Then we connect it with attribution data from our easy-to-implement in-app and web SDKs as well as direct integrations with customer data platforms, analytics solutions, and internal BI systems, bringing the full customer journey into a single view. Finally, we match the two datasets.

The result is the most accurate cohort ROI and CPA metrics available to marketers, at the deepest levels of granularity including campaign, publisher and even creative.

That’s ground-breaking. It’s revolutionary.

But bringing cross-device and cross-platform ROI into Singular and measuring it accurately, at granular levels, is only the beginning to driving impactful growth.

Granular data for growth

Marketers can now access granular ROI cohort reporting that is more accurate than ever, as you can get clear, combined revenue for users across all devices. This is critical to achieving profitable growth and only possible with Singular – a complete platform that innovates beyond a single attribution solution.

Moreover, marketers can also utilize the wide set of capabilities that Singular’s Marketing Intelligence Platform offers to make smarter decisions and optimize their growth efforts with additional cross-device visibility; plus, they have more visibility into essential context such as the exact creative customers engaged with and the audience segments they belong to.

For example, you may find that a web channel’s impact is much higher than expected for specific types of customers. And now you can analyze the impact of the same creative across mobile and web.

In fact, we won’t be surprised if marketers start shifting investments with this new level of clarity. We are excited to see how growth strategists are going to rise above the crowd using this new solution to become part of the future wave of sophisticated marketers. Gone are the days of attribution feature wars – Marketing Intelligence has arrived.

Launching Cross-Device Attribution is just another step towards achieving our goal: to be every marketer’s indispensable tool in driving growth. We keep working not only to ensure that you can innovate your growth processes and have access to the highest data accuracy but also to ensure that we bring you the right insights at the right time to help you make timely strategic and operational decisions.

Are you ready to take part in the future of growth?

Find out what Singular can do for you

The world’s best mobile ad networks: Announcing the 2019 Singular ROI Index

3 martech tools mobile marketers absolutely need to achieve outsized results

The very best mobile marketers get more while spending less than average marketers. We’ve seen it in the data.

But questions remain.

How do they achieve outsized results? Are they just smarter? Do they pick better ad networks? Did they choose the right agency that just happened to massively over-deliver?

None of the above. Instead, what our research shows is that super-successful marketers who outperform their competitors have a number of unfair advantages. To put it simply, they use the right tools.

For one thing, marketers generally recognize that working with more ad partners increases your chances of success. Research indicates that, Singular’s data proves it, and marketers instinctually recognize it.

So why aren’t marketers doing it? Perhaps the most important reason: they lack the right tools to manage multiple ad networks at scale.

Here are the three tools they need:

Essential martech tools: measurement

Without the right tools to measure, manage, and optimize your marketing spend, marketers have to deal with too much incompatible data, too many reports, too many dashboards, and too many incomplete perspectives on their overall picture.

Marketers need a way to see the big picture: all their data normalized, standardized, and visible in one place.

Essential martech tools: optimization

Once marketers’ data is assembled and accessible, it becomes a gold mine of valuable insights that the right platform can reveal. That means marketers don’t have to guess where they’re getting more value.

They know.

In addition, growth marketers don’t have to wonder how different creatives are performing: they know. They can compare ad units and creative across all campaigns and all platforms, understanding which images, text, and playables resonate with which audiences across all their ad partners.

Essential martech tools: management

When they add new networks, marketers also open themselves up to increased risk. They need a way to assess the relative quality of traffic, clicks, conversions, and installs from each ad network, and ensure they’re not paying for non-converting users.

In short, marketers need a way to maximize ROI and control fraud.

None of this is easy

Digital marketers generally know two or three “safe” sources of traffic, clicks, app installs, and conversions. The big two, Google and Facebook, are usually in that picture. After that, Amazon is getting some play — although mostly in consumer goods — and Apple Search Ads is growing as well.

But beyond these names many mobile marketers simply aren’t sure where they should go, which networks are trustworthy, and who they should try.

“Scaling mobile partners is hard,” says Barbara Mighdoll, Senior Director of Marketing for Singular. “It requires more effort, and without the right tools, you take more risks on fraud and traffic quality.”

Scaling is challenging, but without scaling, marketers are left in the same boat as all the others: mediocre results at high cost. And without the right tools, it’s almost impossible to scale ad partners safely.

The solution? Get the right tool.

For more information and details on how the best mobile marketers are achieving outsized results, download Scaling Mobile Growth: How smart marketers pay 37% less and get 60% more today.

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.

Mobile ad fraud: 6 ways fraudsters win via dirty tricks, nasty scams, illegal tech, and cutting-edge camouflage

Ad fraud is a game where losing can look like winning, our Singular Fraud Index says. That’s why you need the latest intel — and the best fraud protection suite in the attribution industry — to protect you.

And understanding the enemy is the first step in winning the fraud war.

Or at least … not losing it.

At our recent UNIFY conference, IronSource’s Vice President for Growth Yevgeny Peres unpacked the science and data behind how fraudsters win. This was new intel to some of the world’s top digital marketers (not an easy task) and showed attendees how fraud was happening live in their campaigns right from the most innocuous, trustworthy, and high-quality apps.

Now we’re sharing the insights with you.

How fraudsters win: Outsourcing fake clicks to real people

“Assuming you have a phone and you’ve engaged with ads and you have some apps installed, fraudsters have access to your phone: your device ID,” says Peres. “And that device ID … once a fraudster has it, it’s not that complicated to start using it to manipulate attribution.”

Click spamming in operation. Code translated by Singular CEO Gadi Eliashiv.

Here’s how it works.

Peres demonstrated with a mobile app on a phone that he connected to desktop technology to read and display all internet traffic. The app, a household name and top-60 grossing app, is perfectly legitimate and aboveboard. It would look like a quality publisher and a quality traffic source to any advertiser.

But it happens to show banner ads.

And fraudsters have managed to get their banner ads displayed on the app.

One of them is running code in Javascript behind the image. That code contains a long list of click URLs and opens multiple iFrames: mini virtual web browser windows. The URLs are tracking links, potentially from multiple tracking and attribution vendors, but they’re wrapped links that obscure exactly what they are and where they’re going.

The result: many advertisers, including multiple UNIFY attendees, see potential customer activity on mobile web that turns out to be completely fake.

“This was in-app banner traffic that’s going to be reported by tracking companies as if it were mobile web,” says Peres. “[These were] various websites that were not open on the phone … you would assume you’re buying from these guys when actually it was driven from the app.”

In one fell swoop you have multiple forms of fraud:

Ad stacking: multiple ads stacked where one appears
Click spamming: 50 clicks fired for one banner view
Domain spoofing: clicks are reported as coming from sites that no-one ever visited
Fingerprint manipulation: device fingerprints are faked to look like real devices

“This looks like great quality … but there’s zero intent,” says Peres.

How the fraudsters win: SDK spoofing

“The first thing to understand about SDK spoofing is that it’s much bigger than you think,” says Peres.

SDK spoofing requires some serious technical chops. If fraudsters have access to real device IDs, they could simply engage in click spamming. But why wait for people to install an app or convert in a campaign randomly or organically?

In SDK spoofing, fraudsters employ code in one app to send fake install and conversion signals on behalf of another app: an advertiser’s app.

Fraudsters can vastly multiply their ill-gotten earnings by faking conversion events.

“If I know what the tracking company’s SDK reports on app open, I might as well intercept that, replace the device ID, play around with the other parameters, and send it again,” says Peres. “A couple minutes later, I can orchestrate a beautiful KPI curve … I can [even] inflate organics to make sure this channel [looks like it] has an organic uplift.”

How the fraudsters win: Click spamming

The good guys in adtech have access to hundreds of millions if not billions of device identifiers. The bad news: so do the bad guys.

That’s a problem.

“All we need to do is gain access to a campaign and start running a script and fire a click every morning, randomly,” says Peres, mimicking a fraudster’s thought process. “[You’re] hoping that one of these guys will generate a conversion … that’s probably a $50K income a day, just doing that.”

On an ad exchange, once you gain access to a device ID you can do whatever you want with it, technically speaking.

“Once you have access to it, anyone can report a click,” Peres says. “It’s how the design of our stats-serving ecosystem is … that’s the bad news.”

How the fraudsters win: No incrementality analysis

Fraudulent activity isn’t just something on top of your standard organic marketing results or even just your paid marketing campaigns.

Some fraudulent channels eat organics.

Some fraudulent channels eat other paid channels.

How ad fraud can eat both organic and paid channels.

“It’s very important to understand the difference between channels that are incremental to you and channels that are not,” says Peres. “This is the biggest challenge for a marketer.”

Marketers may perceive fraud as a 20-30% problem, but much of it is not incremental. It’s cannibalistic. That means that marketers absolutely must test each channel for incrementality, ensuring that each channel really does independently drive business results.

How the fraudsters win: Fraud looks so juicy good

Some fraud has excellent camouflage. Here’s one example: check out the average revenue per user (ARPU) for these two campaigns.

Some fraudulent networks report views as clicks.

Campaign 1 and 2 have identical cost per install (CPI) and near-identical impressions, plus near-identical real clicks. But campaign two is a video ad that is either auto-redirecting to the App Store or Google Play after every view.

“When you look at the funnel, the CTR is almost 100%,” Peres says. “This is by the design of their product where they report a click for every completed view … so once the video is over, they have to report a click because they redirect the user to the App Store.”

The ARPU looks great — better than a clean campaign — so it’s very tempting for marketers to keep spending there. Especially if they’re not closely checking the other parameters such as the impossibly-high click-through rate.

This is an example of something that completely breaks the mobile advertising model, says Peres.

“These channels … if they’re manipulating attribution, their media costs are very low,” he says. “Other DSPs are competing with these guys. You have a 1% CTR rate for playing a clean game; these guys on a single impression generate 50 clicks. That’s 5000X stronger. That’s something you cannot outbid no matter which data scientist you hire.”

How the fraudsters win: Marketers don’t monitor key indicators

There are many key indicators that marketers who care about limiting fraud need to pay attention to, says Peres. Here are some of them (watch the full video for the complete list).

Fraud is revealed when you look at the right data.

Good ad fraud prevention enables you to see:

Channel metrics versus attribution metrics (look for discrepancies)
Percentage of clicks without a device/advertising ID (Android should be about 1%; iOS should be about 20%)
Percentage of view-through attribution (VTA) versus click-through attribution (CTA) conversions
Number of clicks per device ID (high is suspicious, shockingly)
Number of views per device ID (again, high is suspicious)
Percentage of clicks without a prior view … in some cases, 65% or more of clicks happen without a view: this is suspicious
Very low eCPM
Short, very regular, very long, or otherwise improbable or unnatural click to install times
Attribution analytics versus iTune Connect and Google Developers Console numbers
Incrementality

That’s not a small number to keep track of, but savvy marketers who don’t want to get burned by fraud will need to stay on top of these key indicators.

Summing up: One thing you must do

Fraudsters are smart, they’re technical, and they’re always working hard to separate you from your hard-earned ad dollars.

They also hide in plain sight, as sub-publishers and lower-tier ad networks or sources of supply.

How fraudsters hide in the adtech ecosystem.

You need a partner who stays on top of ad fraud for you.

“My single advice is … make sure you work with a tracking company that invests a lot on research,” Peres says. “Singular obviously invests a lot on research and has a lot of knowledge there … they update their SDK a lot, the security of their SDK. Make sure you have the latest version of the SDK and keep updating … it’s a must, every time it comes out.”

Our investment in mobile ad fraud prevention protects you from donating to organized crime … and shooting your paid promotion campaigns in the foot.

What to do now: Get the hard numbers on fraud … and the top fraud-free networks.