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!

Known knowns, known unknowns, unknown unknowns … and marketing intelligence

How much do you really know about the effectiveness of where, when and how your marketing dollars are being spent?

“…there are known knowns; there are things we know we know. We also know there are known unknowns; that is to say we know there are some things we do not know. But there are also unknown unknowns — the ones we don’t know we don’t know.”

– Donald Rumsfeld

Former U.S. Secretary of Defense Donald Rumsfeld spoke these words at a press briefing in 2002 in response to a question about weapons of mass destruction in Iraq. While he didn’t invent this concept of known knowns, it became his most famous line. The title of his memoir is “Known and Unknown,” and Errol Morris made a documentary film about him titled “Unknown Known.”

The “unknown known” is a fourth concept that Rumsfeld defines as “the things that you think you know that it turns out you did not.” How can these four concepts of known and unknown that Rumsfeld applied to national security intelligence also apply to marketing intelligence?

Let’s take a look.

Known knowns

The first step to marketing intelligence is knowing what you have. On the campaign side, you know your impression, click, and spend data for each campaign, ad set, and ad on each media source. On the attribution side you know how many installs, in-app events, leads, or purchases you’re getting.

While these facts can help you gauge some level of marketing effectiveness, there may be issues getting the data frequently enough to make meaningful decisions. Still, these are the facts you know you know.

Known unknowns

Knowing what you don’t have is important so that you can embark on a journey to find it. You may have an estimation of ROI, but a true picture of ROI is one of the most common known unknowns for marketers. It requires linking campaign data with attribution data in a marketing intelligence platform like Singular.

An example of a known unknown might be understanding ROI by media source for a subscription-based business. Without unified data, it’s difficlut to know ROI over a long lifetime. Finding LTV over both web and mobile is equally challenging. These are known unknowns that can be solved with the right tool.

Unknown unknowns

The unknown unknowns of marketing intelligence are the benefits you could see by understanding ROI at a more granular level. They are the questions you may not be thinking about off the top of your head, but can answer with the right tool.

Consider these questions:

  • How effective is my creative? If you knew when your creative fatigues on a given source, you could achieve greater ROI simply by swapping old ads for new.
  • Are my bids optimal on this media source? Granular ROI insights would allow you to set bids higher or lower depending on the source.
  • Am I setting the optimal budget to maximize delivery? Setting optimal budgets and pacing spend provide additional pockets of ROI.

In addition, creating custom dimensions such as CPA, ROAS, eCPI, ARPU and LTV by combining upper funnel and lower funnel data can yield unknown benefits. If you don’t already have this data, the metrics are unknown, as well as the benefits of knowing those metrics and taking action on them. The most advanced marketers run A/B tests and experiments to uncover more and more revenue-boosting results.

Unknown knowns

An unknown known (data you think you know but actually don’t) sounds an awful lot like fraud. How can you actually know how much you are wasting on fraud? How many of those new clicks, installs, and conversions are actually fake?

Do you have real users with stolen attribution crediting?

Classic metrics may indicate you’re doing well when you really have wasted spending on faked clicks. There’s nothing worse than optimizing based on metrics that are being manipulated. Singular’s fraud detection and prevention features help marketers uncover activity from even the most wily fraudsters. With the help of our fraud engine and in-house fraud expertise, those unknown knowns will start to become known.

Intelligence is exciting

There’s a big difference between working in national security intelligence and marketing intelligence. But successful intelligence workers share a common trait: they are excited about the unknown.

They know that things they thought to be true are often not. They can approach every day without assumptions and make new discoveries. Eventually, the unknowns become known, and there are deeper, more interesting unknowns to unpack. Not only is marketing intelligence an adventure, it’s a lot less risky than searching for weapons of mass destruction.

To learn more about how marketing intelligence can help you venture into the unknown unknown without the tricky language of a career politician, contact us for a demo today.

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.

Oath & Singular: Fireside chat on adtech, martech, fraud, IoT, and the biggest challenges facing marketers

Marketers are facing more challenges now than ever before: the data explosion, the fraud epidemic, cross-channel and cross-platform resolution, and evolving from marketing art to marketing science.

It’s not easy out there.

That’s precisely why Oath, the new AOL and Yahoo!, has put together a new series of fireside chats featuring solutions to some of marketers’ biggest challenges.

And we were happy to participate.

Oath Ad Platforms Fireside Chat with John Koetsier, Singular from Oath on Vimeo.

Missy Schnurstein, Oath’s Head of Product Marketing and Demand Strategy, hosted the chat, and we spoke about adtech, martech, IoT, and the changing relationship between brands and customers.

That includes emerging rules and standards around advertising and how marketers might access new prospects in the future via mediated structures — perhaps using blockchain — to communicate to people who have explicitly granted them permission.

Check out the blog post here, and the full video is embedded above, or available on Vimeo.

Mobile attribution webinar: Your Top 27 ‘No BS’ questions answered

We know, it’s sad. You missed our mobile attribution webinar last week. We missed you too!

But we have a solution. Two of them, in fact.

First, if you missed our “No BS Mobile Attribution Webinar” last week, it is still available on-demand. We had fun doing the webinar, and we think you’ll enjoy listening to it as well. But second, if you don’t have 30 minutes to spare, it might be faster to read the answers we provided here.

First, a quick recap: content & speakers

Mobile attribution can be confusing, and it can seem pretty detailed and technical sometimes. That’s why we hosted the attribution webinar with friends from Vungle and Liftoff. And we had three experts, who are also providing the answers you see here …

Barbara Mighdoll
Senior Director of Marketing
Singular

David Bennett
Sales Engineer
Liftoff

Rina Matsumoto
Performance Optimization Lead, US
Vungle

OK. The mobile attribution webinar questions (and answers)

1) What is mobile attribution?

Rina: Mobile attribution is the way mobile marketers understand from which marketing channels their app users are acquired.

It’s incredibly important to know which traffic sources are bringing not only users but high LTV users into your app. This will allow you to invest your marketing budget in the right sources.

2) From Andrew at Flipboard: “Can you please touch on challenges and capabilities for tracking attribution from a mobile app?”

Barbara: Well, this is a fairly broad question that could be taken in so many directions, and since we are just starting the discussion I’ll keep this high-level.

Mobile attribution at the core is the bridging together of advertising and mobile technologies. The challenge to attribution is being able to keep up with this constantly evolving technology, and I’ll also add the constantly evolving ecosystem threats like fraud. However, when done right, the insights from mobile attribution allow marketers to execute and evaluate their mobile marketing campaigns with proper app conversion metrics.

2) What are tracking links? How do they work?

David: There are multiple types of tracking links, impression tracking links and click tracking links. These links are used to gather data around what partners are driving impressions and clicks for you. They also allow us to track what users are downloading your app after seeing an ad.

This helps you assign attribution.

The tracking links also help us route users to the App Store, the Google Play store, or other app marketplaces. In the case of re-engagement or retargeting campaigns they can also be setup to route users directly to your app. In general, they make data collection for digital marketing possible.

As for how they work, they send information to your MMP when impressions are shown or when ads are clicked. The information that they send contains device data as well as a few other key pieces of information. Since they contain device data it allows you to track when users are installing your app because of your advertising efforts and what actions they are taking in your app because of your advertising efforts.

3) What is deep-linking? Why does it matter?

David: Deep-linking is a technology that allows you to link to your app directly from your ads [editor’s note: whether in an app or on the mobile web]. For re-engagement campaigns this means a smoother user experience.

This is important because it allows you to minimize the number of steps that your users have to complete in order to reach the desired event. This usually leads to better performance and increased ROI.

Barbara: Just to add a quick comment here, I think this technology has become a pretty standard part of an attribution stack, and because of that most users now expect when they click on an ad with a particular CTA, the app will open in the correct location.

3) What are postbacks? Should I be getting them?

Rina: Postbacks are the way networks receive in-app data from clients, whether that’s installs or post-install events like in-app purchase or tutorial completion.

These postbacks will be key depending on your network’s buying model or optimization methodology. So it’s important to consult your network partners on what postbacks they’ll be needing.

David: This enables you to share user behavior with your advertising partners.

4) What is a SAN?

David: Self attributing networks such as Facebook, Google, and Twitter inform your attribution partners which installs and actions they drove.

5) What is granularity? Why do marketers need granularity?

Barbara: Granularity describes how deep a marketer is able to analyze their data.

For example, basic granularity usually includes drilling down to the app & source level, while sophisticated marketers are able to go deeper into the campaign, publisher, keyword and even creative levels. With this level of detail, marketers can decide when they should shift budget. They also can better inform how to spend their time optimizing – and know exactly where to optimize.

Advanced marketers who have been able to achieve scale and see massive growth are the ones who are able to optimize at deep levels of granularity. For example, as part of our Marketing Intelligence Platform we offer creative reporting where we are able to pull in your ad creative so you can easily match your data to your ads.

One of our customers who started utilizing these creative level insights saw ROI increase 40% within 2 months.

Rina: I agree with Barbara. Granularity helps you understand what types of users were acquired and how they were acquired. Are these users from iOS 11? Were they acquired from a specific type of creative?

It’ll also help in investigating any issues with discrepancies and potential campaign or fraud issues, by being able to drill down to specific parameters.

6) Why do marketers need to combine customer-level mobile attribution data and campaign-level marketing data?

Barbara: This is a great question, and one that we address frequently because the complexity of this is often misunderstood.

Before I jump into the why marketers need to combine this data, I first want to touch on why combining it is even a challenge.

Marketing data is only available in aggregate like ad spend, while attribution data is available at the user-level like app installs. By nature, aggregate and user-level data do not fit together – it’s like trying to assemble a puzzle with pieces from different sets.

This means that marketer’s datasets are often left incomplete and inaccurate. Left this way, marketers do not have the ability to dig into granular levels of insights. And this is a core problem Singular solves – we redefined how attribution data matches campaign data with the experience we’ve acquired over 4 years of mapping this ecosystem.

So to answer why marketers need to combine these two datasets, the answer is pretty simple: to unlock ROI at granular levels like the campaign, publisher, keyword and creative-levels.

Rina: User level data are data points like device type, OS version, and country. Campaign level data are data points like publisher and creative information. Only once you marry this data do you have a full understanding of your marketing campaigns.

7) Can I see where ad networks are running my ads? If so, how?

Rina: At Vungle, we try to provide as much transparency to our advertisers as possible. We share publisher site names with all of our clients to give full transparency into their campaigns.

This transparency allows advertisers to better understand their user base and buy more intelligently on our platform.

8) What are the most critical reporting needs in mobile attribution?

Barbara: First of all, discrepancy and transparency are critical. No matter how your attribution provider is getting install and cost data (i.e. via API or tracking links), there are bound to be discrepancies between your provider and your ad networks. Being able to analyze these discrepancies is extremely valuable to avoid making decisions based on incorrect data.

Shameless plug:
One of the advantages of using Singular, is we allow you to compare data sets side by side without having to toggle between dashboards. And using our transparency feature, marketers can select their preferred source for each metric, then easily locate discrepancies in their data, while even setting-up alerts when discrepancies exceed a threshold.

In addition, ROI (return on investment) is the single most important metric for mobile marketers. However, most attribution providers are only able to provide ROI insights at the source level because they are unable to reliably match cost and campaign data with user level data. True ROI data empowers you to optimize your advertising by the quality of users it’s driving, instead of just install and revenue data. It’s also a must-have if you want to scale your programs while maintaining or even improving efficiencies.

David: In my experience at Liftoff, when there are some discrepancies in between different reports the first two places that we would look are fraud and tracking issues. If the discrepancy is due to fraud we revamp what we are doing and work hard to protect our customers.

If the discrepancy is caused by tracking issues we work with our customers and their attribution providers to get tracking functioning as expected.

9) What kinds of ad fraud are most common? How can I avoid them?

Barbara: Today there are two main forms of fraud: fake users and attribution manipulation. Fake users involves bots, malware and install farms to emulate clicks, installs and in-app events, causing advertisers to pay for activity that is not completed by a real user.

Attribution manipulation is an especially dangerous form of fraud since it not only costs marketers their spend but also corrupts performance data, causing marketers to make misguided acquisition decisions. The two most common types are click injection and click spamming.

David: Click fraud is a major form of fraud that we are seeing right now. It can be anything from click farming to click spamming to click injection to ad stacking. These types of fraud are meant to drive a high number of clicks, reduce the CPC of a campaign and possibly steal attribution from users that could convert organically.

Another example of fraud would be install-fraud through something like install farming or click spamming to steal install credit. These types of fraud are done to drive a higher number of installs to reduce the CPIs of a campaign. In order to combat both click-fraud and install-fraud Liftoff recommends focusing campaigns on actions that users perform through CPA goals or KPIs or through setting ROAS goals or KPIs.

Other ways that we help our customers avoid fraud are blacklisting suspicious traffic, blacklisting traffic from suspicious sources, we even go so far as to reject anonymous traffic, or traffic that doesn’t have advertising IDs or IP addresses associated with the devices.

10) How can I avoid ad fraud?

Rina: Attribution partners and ad networks will have their own technology to prevent and detect fraud.

Something that you can do as an advertiser is take a look at ROAS data, which can be useful to spot install fraud or fake users. However, click fraud or attribution manipulation will typically snipe organic users that usually have high LTV.

At Vungle, we recommend marketers take a closer look at their CTR/CVR and click to install time distributions to find any anomalies. Any abnormally high CTR or low CVR can signal that the clicks aren’t real. A click-to-install time distribution that is skewed beyond the one hour mark is also an indicator that most users didn’t download after a real click that redirected them to the store.

11) Should I pay extra for fraud protection?

Barbara: The biggest mistake marketers can make is to think that fraud is a “nice to have” feature, or that they can “block fraud manually”. Even traffic that looks great i.e. good retention, high ROI can actually be fraud due to attribution manipulation. That’s why we at Singular offer fraud prevention for free.

Also be careful of the actual type of fraud prevention your provider has. With fraud costs so high and growing every year, you need to ensure that your attribution platform not only detects fraud but proactively prevents fraud in real-time.

And by this I mean some attribution providers do not offer actual prevention, but only detection. That means they offer “alerts.” where you then have to manually look at the data and fix it in retrospect. Be on the lookout for prevention types including IP blacklists, geographic outliers, hyper engagement, install validation, and time to install analysis – and the more included the better.

12) How can I ensure brand safety in my mobile advertising?

David: We have customers that worry about brand safety and focus on targeting specific verticals and avoiding others. This is done by setting up either blacklisting or whitelisting for specific types of apps. An example of this would be to blacklist violent apps.

13) Getting app installs is great, but it’s just the first step. What are the most important post-install events to measure?

David: App marketers need to determine which post-install events are the best indicators for future conversions and revenue. Once these events have been determined, these become the events that should be tracked and used to set goals for your campaigns.

These events might be adding an item to your cart or reaching level ten in a game. The idea is that these events indicate a high LTV.

Rina: Understanding short-term metrics as a proxy to determine long-term LTV is the key for performance marketing.

Often times ROAS in the short term is strong indicator of high LTV.

If users often monetize later in their user lifetime, looking at other benchmarks like level completions or retention could be the solution for campaign optimizations.

14) Data is critical to mobile marketing success. Why do I need API access to my attribution partner’s datastream? What kinds of data should I have access to?

Barbara: One of the critical elements to pay attention to if you are in the search for a new attribution provider is data accessibility. After all, your data is only valuable if it’s readily available and in a usable format. This is especially important for marketing organizations with centralized internal reporting.

Regarding what kinds of data you should have access to, there are two types:

  • Aggregate
    This includes LTV, retention, or other in-app KPIs grouped by any number of segments (app, media source, campaign, ad ID, etc).
  • User-level/device-level
    Why do you want this? Just one example: you may need to join that device-level data with offline or proprietary data and perform internal analysis on that combined dataset.

15) Do I have to use one attribution solution across all my apps?

David: The short answer is no … but the long answer is a lot more complicated but really comes down to how many tools you want to worry about integrating and how many tools you want your employees to have to learn.

The more attribution solutions you use across your portfolio the more complexity you add to your portfolio.

Barbara: Yes, complexity is the issue. Do you want to have multiple dashboards? Different workflows?

16) Measuring installs is great, but we do have attrition. How important is uninstall measurement?

Barbara: Uninstall measurement is a useful metric when it comes to understanding your users.

Uninstall data by itself is interesting, but its best used in conjunction with other lower-funnel events to understand the behavior of your users and of your marketing activities.

Aside from the insights, uninstall data can be provided to partners to be used in campaigns for retargeting audiences.

17) Can I use attribution to know how much ad revenue I’m generating from each mobile app user? Or from each network?

Rina: Analytics providers are starting to develop features to ingest ad revenue data to be able to track true LTV of acquired users. As ad revenue on the user level data becomes more readily available, I expect this feature will be widely used by developers.

Barbara: The short answer is yes. It’s a developing technology that we have some customers using right now. The best thing I can say is … talk to us!

https://pixabay.com/en/mobile-phone-smartphone-hand-1419275/

Next steps: mobile attribution master class

Quick-witted readers may be wondering: How did 27 questions turn into 17? The answer: via the magic of multiple queries within each one.

But you may still have unanswered questions.

The solution: get a copy of our No BS Guide To Mobile Attribution. Alternatively, get a full demo of Singular’s mobile attribution capabilities. 

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.

The ‘No BS’ mobile attribution webinar: 27 questions answered (plus yours!)

You’ve heard about mobile attribution. You’ve wondered about mobile attribution. Maybe you even use mobile attribution. But you still have questions.

Like: Why?

Or: Who needs that?

And: Aren’t all mobile attribution solutions basically the same?

We get it. It can be confusing, and it can seem pretty detailed and technical sometimes. That’s why we’re hosting a webinar (with friends from Vungle and Liftoff) on November 6. And we’d like you to attend.

Why? Keep reading.

 

What you won’t get from this webinar

https://pixabay.com/en/communication-head-balloons-man-1991848/What we won’t do is feature talking heads making long droning speeches. We don’t like those kinds of webinars either.

We also won’t do a fancy sales pitch on Why Singular Rocks or How Singular Is The Total Best, Dude. That’s not our style, and we suspect that it’s not really yours, either.

What you will get from this webinar

Quick, to the point answers on key questions about mobile attribution.

Which questions? Keep scrolling …

(And yes, you can add one of your own. Or even two. Start by signing up here.)

Who you’ll get answers from

We’ve selected an attribution expert from Singular, an advertising expert from Vungle, and a mobile app install expert from Liftoff to provide all the answers.

They are:

Barbara Mighdoll
Senior Director of Marketing
Singular

David Bennett
Sales Engineer
Liftoff

Rina Matsumoto
Performance Optimization Lead, US
Vungle

And I’ll be moderating (John Koetsier, VP Insights, Singular.)

And finally, the mobile attribution questions

We have a lot of questions that we’ve seen people ask. Here’s a quick overview:

  1. What is mobile attribution?
  2. What are tracking links? How do they work?
  3. What are postbacks? Should I be getting them?
  4. What is granularity? Why do marketers need granularity?
  5. What is a SAN? Are SANs really self-reporting? What does that mean?
  6. Why do marketers need to combine customer-level attribution data and campaign-level marketing data?
  7. What are the most critical reporting needs in mobile attribution?
  8. What is server-side measurement? When does it make sense?
  9. What kinds of ad fraud are most common? How can I avoid them?
  10. Can I see where ad networks are running my ads? If so, how?
  11. How can I ensure brand safety?
  12. Should I pay extra for fraud protection? What about viewability tracking?
  13. Why do I need API access to my attribution partner’s datastream?
  14. What kinds of API access should I have?
  15. Do I need access to raw log files? Why?
  16. What are the most important post-install events to measure?
  17. Do I have to use one attribution solution across all my apps?

That is actually 27 separate questions, even though we’ve organized them into 17. But it’s pretty likely that there are some that we haven’t seen. Or thought of. And one of them might be yours.

Please ask it here. We’d love nothing more than to add it to the list.

Sign up for the webinar

Join us on November 6 by clicking here to sign up.

7 critical criteria to include in your mobile attribution RFP

According to eMarketer, mobile advertising in the United States is expected to reach over $70 billion this year and account for a whopping 75% of all digital ad spend.

As mobile advertising budgets continue to climb, so does the demand for tools and services to measure and optimize these investments. This surge in demand has led to a proliferation of mobile attribution companies and the commoditization of attribution technologies.

As a result, many mobile attribution providers rely heavily on inflated claims and marketing jargon as a means to differentiate themselves in an increasingly crowded market. Even if you’re an attribution expert, researching mobile measurement providers can be a confusing and frustrating experience for marketers.

To help you feel more confident re-evaluating your current provider or choosing a new one, we’ve written an attribution RFP guide to help you wade through the jargon and find the right provider.

Download our No Bullsh!t Guide to Mobile Attribution now.

Here’s a snapshot of of the seven key criteria we cover in the guide to help you build the best mobile attribution RFP.

1. Mobile Measurement Partner (MMP)

A mobile attribution provider will only be effective if it’s integrated with the media sources you buy from, period. This is particularly crucial if you spend (and you most likely are) on any of the self-attributing networks (such as Facebook, Google, Snapchat, Twitter, and Apple).

2. Data combining

There are two main types of integrations marketers need to be aware of in order to understand the complexity of mapping their data. The first is an attribution integration (which delivers user-behavior data) and the second is an ad network integration (which delivers marketing data). To truly understand ROI across your mobile campaigns, creatives and publishers, you need to collect and combine both sets of data. However, the ability to do this is entirely dependent on the provider’s technological capabilities, and almost all providers (except Singular) cannot do both today. We’ll dig deeper into each data integration and demonstrate why it’s necessary for both to be connected.

3. Granularity

Ensuring accuracy and completeness when combining and connecting your data is the greatest challenge to granularity. No two sources are the same and even when it is accurate, complete, and attributed, you still need to ensure your marketing campaign data matches your user-level data to unlock ROI at the campaign, publisher, keyword, and creative-levels.

4. Reporting

Another area where attribution providers are heavily differentiated is reporting. While many providers will provide some way of accessing raw data, for the system to be truly impactful, the reporting interface must be designed in a way for insights to be derived quickly and efficiently. We’ll review a few report types that have proven to be indispensable to mobile marketers.

5. Fraud prevention

No matter where you advertise or how much you spend, your mobile campaigns will likely be impacted by fraud. Even if a provider says they offer “fraud protection,” make sure you read the fine print. Actual prevention, custom rules, reporting and insights, cost savings and alignment of incentives are a few specifics to pay attention to.

6. Data retention & accessibility

This is an area where this is a huge difference between providers, where some offer decent APIs, and some offer rather useless means to export your data, that will make the entire exercise extremely painful. We’ll dive into the four different options available for downloading and sharing more robust data with your internal teams and systems in this guide.

7. Pricing

While pricing may be one of the most obvious points of consideration when evaluating attribution providers, it’s far more useful to weigh value over price. Hidden costs and feature charges can add up quickly. So, before you are lured by a low-cost solution, make sure you know what you’re paying for.

We’ll help you understand the different pricing models in the attribution market today.

So what are you waiting for? Cut the bullsh!t and get the complete facts on mobile attribution providers now.

Marketers are thinking about mobile attribution completely wrong

Why Singular is creating the new marketing data standard

Since launching Singular 4 years ago, we’ve worked with some of the largest mobile apps, along with an expansive set of mobile marketing solutions to become the de-facto Marketing Data Platform. Singular provides a single source of truth for marketing campaign performance by merging three core datasets that historically existed in silos:

  1. Media sources:
    Ad spend, campaign information, creative performance, targeting options
  2. Attribution:
    Clicks, attributed app installs, tracking links, postbacks
  3. BI:
    Customer profiles, post-install events, predictive LTV, cross-platform revenue

We developed a technology unlike any in existence — instead of building another stand alone marketing solution (advertising channel, email service, re-engagement tool, etc.) — Singular built a platform that could connect data across all the different siloed solutions in the marketing stack, standardize it, and make it actionable for mobile marketers. Using Singular, the leading marketers easily get the analytics and insights they need to maximize ROI across their mobile campaigns.

While we initially set out to collect and connect every piece of marketing and attribution data in a single platform, customers were still challenged with combining these datasets together. We recognized that there are two very different types of data that marketers needed: certain data is only available in aggregate (i.e. ad spend), while other datasets are aggregated from user level data (i.e. app install attribution). Combining these is like trying to assemble a puzzle with pieces from different sets. It simply doesn’t work… the pieces are not built to fit together.

Today we want to talk about a mistreated piece of the marketing stack: Mobile Attribution.

We’ve witnessed an odd approach to Mobile Attribution throughout the years. Legacy mobile attribution providers defined this market in a silo with no understanding of how media sources actually deliver campaigns. While attribution providers built their products to gather the bare minimum data needed to attribute an app install, media sources have infinitely outpaced them in complexity and depth with campaigns using enhanced targeting and multiple variations of creatives. Instead of matching the sophistication of the sources to provide marketers with more insights to optimize on, legacy attribution providers focused on one-upping the other with marketing jargon-filled features lists and a race to be the cheapest solution out there.

The irony here, is this siloed approach to mobile attribution is the #1 reason massive gaps exist in datasets today, making it nearly impossible for marketers to trace the customer journey. We’ve often been asked to solve for these gaps within Singular, but there was nothing we could do since the problem originates with the way legacy attribution platforms were built. So in 2017, we decided to kill the status quo.

Mid last year, we came across an opportunity to acquire Apsalar, an established mobile attribution provider and an exclusive Mobile Measurement Partner of Facebook, Google, Snapchat, Twitter and Pinterest. This was a big and bold decision, but we had solid conviction that we needed to own the Mobile Attribution piece of the puzzle to fill the drastic data holes that had become standard for app marketers.

So how does our approach actually solve the data challenge?

1: We did not pursue “sufficient” attribution functionality or just reach industry parity – we built the best attribution product in existence, by any objective measure.

Recognizing Attribution as critical infrastructure, we utilized learnings from the entire mobile attribution ecosystem to build the industry’s best of breed attribution stack from the ground up. Just imagine if the legacy attribution providers could rebuild their platforms with today’s knowledge of partner integrations, fraud, data privacy regulations, real-time data processing, and data warehousing.

2: We’ve given marketers a dataset that is entirely complete, accurate and extremely granular spanning all of the tools and sources in their marketing stack.

We built attribution natively into Singular’s larger data platform, rather than patching in another siloed attribution stack. Using the knowledge acquired over 4 years of mapping the ecosystem to understand every datapoint reported by every source (and how to standardize it), we built Singular’s attribution to fit all of our proven workflows and data connectors.

3: We redefined how businesses consume marketing data.

Where previously BI teams struggled to assemble different datasets sourced from various APIs, exports and postbacks, we invented new streamlined APIs for easy access to all marketing data. Once-challenging BI projects are now completely trivial.

4: We provide a team of business strategists to help scale marketing in the best way possible.

In accordance with the Singular philosophy, we’ve focused on the customer experience. We provide 24/7 global support, in-region customer success teams and continued improvements on the product based on feedback.\

And what can we provide that a siloed mobile attribution provider can’t?

Our customers now have unparalleled control over their data, standardized marketing and attribution datasets, the analytics on their marketing campaign performance at the most granular levels, the tools to further analyze this data in their own BI and most importantly, the ability to construct the full customer journey.

I’m proud to say our decision to create a new standard, the “Singular Standard,” for complete marketing data was one of the most important and successful decisions we’ve made in Singular’s history. Companies like Rovio, LinkedIn, Sam’s Club, N3TWORK, are among the pioneers of the future marketing stack, and they are being joined by the masses every quarter.

In fact, I’ll let the number speak for themselves:

If you’re ready to remove the data deficit in your stack caused by legacy attribution providers and bring on an innovative approach, we would be happy to talk to you.