Singular COO Susan Kuo nominated for Global Mobile Award by Mobile World Congress

We couldn’t be more happy to announce that Singular chief operating officer and cofounder Susan Kuo has been nominated for a Global Mobile Award from Mobile World Congress.

This is the 25th annual Global Mobile Awards ceremony.

Susan Kuo, COO of Singular
Singular co-founder and COO, Susan Kuo

That means that GLOMO has been handing out awards literally since the flip phone. (A flip phone  actually won one of the very first Global Mobile awards in 1995!)

Susan’s nomination is for outstanding achievement in the Women4Tech category.

Susan Kuo and women for tech

Not only has Susan been a long-time advocate and supporter of women in technology, this past year she spearheaded the launch of THRIVE. THRIVE builds community for women in tech and provides a forum for women to connect, share, learn, and grow together.

“There are more women in our industry today than ever before. Women are now holding roles as key decision-makers,” Susan Kuo says. “But there’s still room to grow.”

Typically, Susan is taking this picture of other female leaders at Singular and not actually in it.

That’s why THRIVE was born: a community where women can come together as technology executives and contributors. And, of course, help each other out through mentorship and knowledge sharing.

“I’m a firm believer that any successful business or venture in life starts first with drawing inspiration and establishing friendships with people in your community,” Susan says. “Without this core foundation, it makes the journey much more challenging and quite frankly, not as fun. My hope is to impart this collaboration and mentorship across of our larger industry. This will enable women to help each other to thrive both professionally and outside of work.”

“We are so proud of Susan and all that she has accomplished,” says Singular CEO Gadi Eliashiv. “Susan has been a key part of Singular from the very beginning. Without her we would never have achieved what we have. This nomination highlights that she’s had an impact not just here, but on our entire industry.”

Congratulations to all the nominees

The full list of nominees, ordered alphabetically, is:

  • Amdocs
  • Dr Athina Kanioura, Chief Analytics Officer and Global Lead for Applied Intelligence for Accenture
  • Elena Sinel, Founder for Teens In AI
  • Dialog Axiata for Ideamart for Women
  • Susan Kuo, COO for Singular

“I want to congratulate all the nominees,” Susan said. “It’s not about me or any one of us individually. It’s about what we’re trying to build together.”

Susan Kuo has an extensive background in mobile and marketing technology and is one of the early female pioneers in the gaming industry. Prior to Singular, she was the SVP of Sales & Business Development at Onavo, a market intelligence company that was acquired by Facebook in 2013. Throughout her career, Susan has been a senior leader in companies across the industry such as InMobi, Booyah, and Electronic Arts.

Susan participates in several communities focused on empowering women in tech and women in leadership.

On the weekends, you can find her running after her two rambunctious little kids or tackling one of her latest remodeling projects.

The award ceremony is at Mobile World Congress in Barcelona in February.

Singular wins 2019 Technology Innovation Award from Frost & Sullivan

We’re pleased to announce that Singular has won the 2019 Technology Innovation Award for marketing analytics in North America from Frost & Sullivan.

Past recipients of Frost & Sullivan awards include Google, Verizon, Cisco, and IBM. Frost & Sullivan is a global research consultancy. 98% of the Fortune 1000 are clients, and the company creates original research for dozens of industries and sectors.

Singular wins Technology Innovation Award

That research included investigating five key technology attributes including Industry Impact, Product Impact, Scalability, Visionary Innovation, and Application Diversity.

Frost & Sullivan’s study also examined five future business value criteria: Financial Performance, Customer Acquisition, Technology Licensing, Brand Loyalty, and Human Capital.

“Against the backdrop of extensive primary and secondary research across the entire value chain, Frost & Sullivan is quite pleased to recognize Singular as the Technology Innovation Leader in the marketing analytics industry,” David Frigstad, Chairman of Frost & Sullivan, wrote in a letter of congratulations.

“Achieving excellence in technology innovation is never an easy task, and it is made even more difficult considering today’s competitive intensity, customer volatility, and economic uncertainty—not to mention the difficulty of innovating in an environment of escalating challenges to intellectual property,” Frigstad wrote. “In this context, your selection as recipient of this Award signifies an even greater accomplishment.”

It’s important to note that this was an independent study. Singular did not pay for it to be produced; we did not request that this report be created, and we did not apply for a technology innovation award.

And all of which, of course, makes winning that much sweeter.

Global brands have lauded Singular’s marketing intelligence platform as one of the strongest and most irreplaceable tools in their arsenal that has helped them obtain a clearer picture of their marketing effectiveness and maximize their return on investment.

– Frost & Sullivan report

More than anything else, Singular is focused on the success of our customers — the best marketers in the world. That makes this external validation of our recent progress particularly gratifying.

[Singular] has been an integral partner to some of the most innovative companies worldwide that have achieved phenomenal success with their marketing efforts.

– Frost & Sullivan report

That’s precisely what we’re seeing with customers like Ilyon: growing 98% with a little help from Singular. And Postmates: decreasing cost per buyer 80% with unified marketing analytics. And LinkedIn, which established a single source of truth for marketing performance using Singular.

“We’re very excited to get this award from Frost & Sullivan,” says Singular CEO Gadi Eliashiv. “It confirms that the most important thing we’ve been working on over the last year — our customers’ growth and success — is actually happening.”

Mobile ad monetization: Analyzing true ROI by tying in ad revenue

Can accounting for ad monetization revenue in your user acquisition ROI analysis help your app business grow smarter and faster? According to Singular Product Manager Lisi Gardiner … yes, it can!

It’s not shocking to most in mobile that ad monetization is growing fast. In fact, App Annie says that 60% more apps will build ad monetization into their overall mobile revenue strategies this year. So it’s clear that in-app advertising is a major — and growing — contributor to mobile publishers’ revenue.

For hyper-casual gaming publishers, it could get to 100% of their revenue.

The ad monetization challenge

But there’s a challenge.

When you only drive revenue via in-app purchases, your income is pretty easy to calculate. And so is your ROI on app install ads, because purchases can be connected to users. And, thanks to Singular and other companies like us, users can be connected to where you acquired them from.

But ad revenue is different.

measure-optimize-ad-revenue-AD-MONETIZATION

It’s harder to connect granular ad view or ad click information, such as the publisher, line item and payout, to individual users, and consequently to calculate cohorts. It’s harder to total up receipts from mediation platforms, ad networks, and monetization partners. And it’s much harder to connect those revenues with user acquisition costs to make smart, informed decisions about future marketing investments.

Enter Singular’s Ad Monetization Attribution & Analytics.

I spent some time with Singular’s Product Manager for Ad Monetization, Lisi Gardiner, to learn more.

How savvy marketers are figuring it out

Koetsier: Let’s start at the beginning … what percentage of revenue do apps get from advertising?

Gardiner: Easy question, harder answer. Ultimately, it really depends on the vertical and the individual app developer. For each, it’s really about finding the right balance of in-app purchase revenue and ad revenue.

Many app developers will constantly play around with that balance, trying to ensure they don’t show so many ads that it would cause the users to churn.

Hyper-casual games often have between 50-100% of their revenue from ads, and media apps (news apps, lifestyle apps, etc.) might have more ad-focused monetization as well. Other kinds of apps have much lower levels if any at all.

Koetsier: I’m guessing that figuring out the right number of ads is not the only challenge …

Gardiner: Absolutely not. Another key challenge is figuring out the right network fit. Many ad networks focus on gaming apps and gaming consumers, while others cater to other verticals, and you’ve got to find the ones that access consumers — potential users — who fit your app.

Koetsier: So how do most app publishers account for incremental ad ROI? How do they even know how much they’re making from each new user in their apps?

Gardiner: It really depends how sophisticated they want to get and how many resources they have. App developers that have a big percentage of ad revenue have some sort of way to combine the data, but ad networks don’t typically provide transparency on a user-level of how much ad revenue they make, although that is starting to change.

So publishers look at other measurements: sessions, geos, clicks.

But the most important thing to take into account is the business model of how you are getting paid.

If you’re getting paid on a rev-share basis, you’re getting paid for the user to not only view an ad, but click on an ad or complete an install. As an app publisher you ideally want to be selling your traffic on a CPM model, because then it doesn’t matter whether the ad actually works and an app gets installed: you get paid.

And, of course, the more eyeballs you have in your app, the more you’ll get paid.

Koetsier: What about those semi-mythical creatures, ad whales?

Gardiner: Some publishers just want to maximize those eyeballs — any eyeballs. But the other perspective we see is app developers who really only care about ad whales … the 20% of their users who view and/or engage with a lot of ads. And they come up with different flows to maximize ad whales.

Koetsier: What kinds of flows?

Gardiner: As an app developer, I have different places in the game where I can place ads. Like Candy Crush, which I play… if you’re out of lives, you can watch an ad and get a new life. But they’ve also added a different flow. If you’ve lost the game, you can add five more moves by watching a video ad.

So app publishers are testing different placements and formats for the ads.

And, of course, different perspectives: focusing on all users versus focusing on ad whales.

Koetsier: That’s quite a difference.

Gardiner: It is. When I talk to people in the industry, no-one is completely sure they’re doing it the right way. Everyone is asking: what is the best method? They want to know what everyone else is doing, and they want to know what maximizes revenue.

They also want to know: what is the best way to measure ad revenue? Should I measure it via eCPM, or track individual ad whale activity?

Koetsier: How do app publishers typically combine ad revenue with any IAP revenue or other revenue they might have, in order to understand overall ROI?

Gardiner: They’re either doing it manually, or they have a BI team that’s helping them combine it.

Since you’re trying to combine the data on device IDs, that’s a lot of data to be ingesting. Especially for hyper-casual games with small BI teams … that could be really costly in terms of time and money.

Koetsier: I assume you have a better way? Give me a high-level overview of the Singular’s ad monetization solution.

Gardiner: On the highest level, we pull in your user-level in-app purchase revenue and your ad monetization revenue, and then connect it with your cost data so you have a complete view of ROI and ROAS.

Singular gives you four different methods for calculating ad revenue:

  1. First, the in-house Singular solution
    For customers using our attribution, we have a plug and play solution that calculates the average revenue per session and automatically connects it with your user acquisition cost. The result is accurate, cohort-level insights into your ROI which you couldn’t get before because you were missing the ad revenue stream. We’re also able to account for more sophisticated setups that use multiple ad revenue events and more complex calculations.
  2. We also work with Ironsource
    We pull in all the user-level revenue data from Ironsource’s mediation platform and then connect it to your cost data.
  3. And we work with MoPub
    MoPub offers impression-level revenue data. We receive Mopub events that report revenue data, then cohort it for every device and combine it with ad spend across all of your acquisition sources.
  4. Finally, we work with Soomla
    We pull ad revenue data from Soomla and then apply/combine it to your cost data, or any other upper-funnel metric, for comprehensive ROI analytics.
Ad Monetization Attribution

 

Koetsier: How is this different from competing ad monetization solutions on the market?

Gardiner: First off, Singular is the strongest in pulling and calculating ad spend for every media source you’re working with. That’s in our DNA. We guarantee complete coverage and are not limited to Google and Facebook. That means that ad spend is accounted for in every type of report, at every granularity. You can see the ROI for any aspect of your marketing, whether it’s a channel, campaign or creative. There’s a lot of proven tech around this, which now also applies to our Ad Monetization solutions.

Second, we are integrated with all of the platforms that report user-level and device-level ad revenue. This means that regardless of which vendor you’re working with, we make the best effort to account for every portion of your data.

Third, we are the only MMP that has done the extra work to build comprehensive Ad Monetization Analytics that monetization teams can leverage to grow ad revenue. We want everyone to use Singular and have a single source of truth, and that should not be limited to the UA team. Our Ad Monetization reports can replace your manual reports or reporting vendors, and again uses our tech for pulling data from every type of format with automatic error detection and scalability.

Lastly, we’re flexible. If you want to use a custom ad revenue event, that’s supported. If you’re working with multiple vendors across your app portfolio, we will connect to all of them.

Koetsier: So, let’s say I’m an app publisher. What can I expect if I implement this?

Gardiner: As soon as you implement our SDK and connect your publishers to Singular, ad revenue will be available in every single report we have.

First off, we’re giving you complete visibility into your total ROI, which you never had before. Channels and campaigns that you thought had a specific ROI could look completely different once you factor ad revenue into the ROI calculation. Now you can make better decisions about the actual performance of campaigns and channels.

Plus, we’re going to save you a lot of time. If you’re doing this manually and optimizing just one network every day, we’re easily saving you a couple of hours a day.

Koetsier: And what does that change about how I do my job?

Gardiner: You can optimize your campaigns much more frequently … and you have way better insight into how to do it.

Koetsier: So, bottom line: how does it make me better at growing my app?

Gardiner: We provide a full picture of all your revenue … before you didn’t have a complete view of your revenue.

That means you make better-informed marketing decisions. That means you have what it takes to hit your goals. And, that means you know which media sources provide the most valuable users.

For example, some ad networks might be more expensive, and you might be tempted to cut them, but having ad monetization data from them could indicate that they provide more valuable users, who engage with the ads in your app … so they actually have high ROAS and you should be increasing spend with that network.

At the end of the day: you know more and you’re smarter. So you grow faster.

Koetsier: Thank you for your time!

Want to learn more about tying in ad revenue to your ROI analysis?

>> Schedule a demo today.

DraftKings unifies siloed marketing data to uncover deep insights for growth

We sat down with DraftKings Senior Director of Growth Marketing, Jayne Pimentel, to discuss how her team leveraged Singular to unify their siloed marketing data and uncover deep insights for superior optimizations.

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Introduction

My name is Jayne Pimentel and I’m the Senior Director of Growth Marketing at DraftKings. DraftKings is a sports media technology company. Most people know us for daily fantasy sports. We’ve recently entered the sportsbook category, which has been around outside the US for centuries.

When I joined DraftKings, we had one product. We now have three. We brought everything in-house. We got rid of our ad agencies. To scale and to grow a business like that requires a lot of infrastructure. A lot of disciplines that aren’t really core competencies to DraftKings but things that we need to invest in third parties to allow my team to stay agile.

Why Singular?

I remember being a consultant when I joined DraftKings and my first call actually was a Singular call. And then before that when I was at Cognant, even at Machine Zone, over half our clients used Singular. So I was familiar with all of these great brand names that were out there, people like Lyft that we were working with, that also leverage Singular.

Establishing a single source of truth for marketing performance

Singular has helped us become fluent between different kind of siloed teams. The fragmentation of data is something that is achievable to overcome but requires a lot of ingestion of data as well as leveraging something like Singular if you can’t adjust that data yourself.

So if you don’t want to pay for a ton of servers to ingest impression-level data, click data, I mean that’s also just one piece. Then you also have user data within our apps. Then you also have revenue data and how we monetize. And the fragmentation even on the monetization of a user, how much they’re valued, needs to be also tied to how much we’re willing to pay for that user. And so that true lifecycle value of that user is something that requires data coming from email, S3 buckets, garbage Excel files, whatever it is. But you have to be able to have some sort of system to make sense of all that and to ingest it and unify it.

Democratizing creative reporting & optimization

And it’s also been helpful with our creative team. We actually use the creative tool within Singular often because our creative team, they’re visual people, they’re talent and they like to see the performance but in a more visual way. And also having the creative and the image that is actually associated with the performance, it’s been really helpful to start conversations to help with testing agendas and to make everyone accountable across teams now that we have a baseline around the data we’re bringing in.

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

Personal Capital tackles cross-platform measurement

We sat down with Rachel Chanco, Director of Digital Marketing & Mobile Growth at Personal Capital, to discuss how they’re connecting cross-platform user journeys.

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Introduction

I’m Rachel Chanco. I’m with Personal Capital. I lead all of the Digital Marketing and Mobile Growth initiatives.

Personal Capital is a digital wealth management company. How we differentiate ourselves from other FinTech advisors in the space is that we are a hybrid model. We leverage toolset technology but we connect you with a personal advisor that can actually really help you plan things out.

Personal Capital currently uses Singular as its mobile measurement partner.

Connecting users’ cross-platform journeys

The user journey is pretty unique. A lot of times people will come from the desktop and then download the app. A lot of times people come from the app and then convert on a desktop.

One of the things I really love about working with Singular is not only am I able to understand data from the mobile side but because of the custom integrations we can do with Singular, I am able to understand a user journey from mobile app install to a conversion that may occur on desktop.

So rather than just sticking to standard mobile measurement events, I’m able to leverage the platform to connect if an event is actually happening on desktop, even though the user came from mobile. I can say that this user was actually valuable even though on a standard analysis they would not appear to be valuable.

So we talk a lot about cross-platform being a real problem within the industry and Singular is helping me solve for that.

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

Fixing a $13B problem: How Singular is killing app install fraud

You probably saw the news that we released last week: deterministic Android app install validation. This, along with a number of other improvements we’ve recently made, is a massive industry breakthrough that is completely game-changing for many of our clients.

Some of them are now saving massive amounts of money:

“Singular’s updated Fraud Prevention suite is the most powerful mobile app install fraud prevention I’ve seen,” says Channy Lim, Head of BI Department at Com2uS, maker of the hit mobile game Summoners War. “This will save us literally hundreds of thousands of dollars every month, and lead us to make more effective marketing decisions.”

The news is exciting, but I wanted to dive a little deeper.

I would like to share a little more detail about how app install fraud works, the problems with existing methods of finding it, and what we doing differently at Singular.

How app install fraud works

One of the ways fraudsters steal billions of advertisers’ dollars annually is app install fraud. Or, to put it another way: fake installs.

App install fraud is a collection of fraud methods that create fake mobile users and app installs. As opposed to attribution manipulation fraud, which steals credit for existing legitimate app installs, app install fraudsters take matters into their own hands and create app installs out of thin air.

There are multiple ways to perform fake installs fraud, and naturally, some are better than others.

The simplest and most low-tech way is a device farm. You get a bunch of devices, click a lot of tracking links, install a lot of apps, then open them, delete them, and reset each device’s Advertising ID (Android) or IDFA (iOS). Rinse and repeat regularly, and you’re collecting ad dollars.

But there are far more complex and advanced ways to perform fake installs that generate a lot more money far quicker.

One of the other ways fraudsters scale up their device farm operation is to use emulators and bots instead of real devices and real human beings who use the devices. This can be done in the cloud, and potentially on multiple servers in multiple locations, to try to look authentic.

One of the most notable techniques leveraged by smarter fraudsters is SDK spoofing.

Mobile marketers place software (an SDK) from a Mobile Measurement Partner (MMP) in their apps to monitor and measure the results of their marketing. In SDK spoofing, no app is ever actually installed … but an install is being reported to the MMP and potentially other analytics providers by faking the SDK’s traffic. This can be done by technically advanced fraudsters who understand how communication with the measurement service works and how to emulate that communication.

This is far more scalable than running a device farm, because once they have done the initial work, they can create a script to run on servers around the globe. That creates fake installs on fake devices. Alternatively, they can write code that can run on legitimate users’ devices anywhere, reporting installations of apps that have never been installed: fake installs on real devices.

Another example comes in the form of malware, where malicious apps install and run legitimate apps on real users’ devices. This happened for example with the Viking Horde malware. In such cases the user is real and the app is real but the install itself is fraudulent.

As fraudsters become more advanced they tap more and more into the power of the high-tech fake install techniques, and for good reasons. These attacks are highly scalable and hard to find, therefore netting the fraudsters huge amounts of money.

Detecting and preventing fake installs is hard

There are multiple ways to detect fake installs. The problem is that many are unreliable, inaccurate, and most importantly, ineffective.

SDK Message Hashing
Since SDK spoofing aims to fake an MMP’s SDK traffic, MMPs (including Singular) protect each message sent from the SDK. That’s typically done via hashing: taking the data from the message, a secret key that is different for each app, and combining them to create a blob of data that can be verified on the MMP’s backend.

The problem is that the secret is not so secret, as apps that run on users’ devices can create these hashes, so SDK fraudsters can extract the secret and algorithm from the publicly available app binary. At times they don’t even need to reverse engineer the algorithm since the SDK is open source.

Abnormal numbers of new devices
One interesting statistical technique to fight fake install fraud is to look for a high percentage of brand-new or never-before-seen devices coming from specific ad networks or publishers. When you see abnormally high ratios, it’s generally clear that something fishy is happening.

The problem however, is that fraudsters sometimes leverage existing devices or mingle their fake traffic with traffic from real devices, making it harder to spot anomalies.

Abnormal retention rate or other KPIs
Marketers can sometimes identify fraud by seeing abnormal rates of retention, in-app purchases, or other KPIs. For example, if your average retention is 15% on D14, but installs from a particular campaign, publisher, or network show a 1% retention rate, it’s clear that there’s something that deserves further investigation.

But Singular research shows that fraudsters have learned to fake retention and post install events/purchases.

For example, Singular uncovered a case of extremely sophisticated SDK spoofing campaign on iOS that fools most fraud prevention solutions in the industry. The fraudsters not only generated seemingly legitimate app installs but they also continued to send post-install events, in essence faking real users’ activity. They have even tried reporting in-app purchases, and while doing so reported revenue receipts for these fake purchases.

Sensor data and user behavioral analysis
Sensor data based solutions take post-install fake user detection one step further. These solutions try to detect abnormal devices or users by looking at non-marketing data points such as device movements (via a smartphone’s accelerometer and/or gyroscope), battery data, and user-screen interaction.

How?

Simple: sensor data for real devices should look different than simulators that don’t move.

The challenge is that this can be faked as well as shown in the huge “We Purchase Apps” scandal revealed in October 2018. In this massive ad fraud campaign the perpetrators bought real apps, studied the usage patterns of their real users, and then created fake users coming from those same apps.

One of the biggest targets of this campaign was none other than Google itself, the company who has probably put the most effort into profiling real user activities and protecting advertisers from fake user emulation.

And more …
There are multiple other methods, each of which has its strengths and weaknesses.

The problem with post-install fraud determination

While post-install methods do an important job of raising the bar against fraud they have some inherent caveats that stop them from being effective fraud prevention tools.

1: Statistical (in)significance
Post-install methods are statistical tools that work by looking at groups of installs and checking if one or more of these groups exhibit anomalous activities. Usually these groups would be installs coming from the same publisher. For example, when looking for new devices it’s unsurprising to see a legitimate user with a new device, as new devices are constantly being sold to consumers.

However, for a publisher driving thousands of installs, seeing 95% of those installs from new devices should be highly suspicious. Fraudsters have figured out that they can’t be so blatant, and so they take action and hide. Some drive their traffic from many different publisher IDs and even networks to keep numbers low; some mix their fraudulent installs with legitimate installs to make the anomaly less apparent.

Utilizing such techniques allows fraudsters to avoid detection by making the anomalies statistically less significant, making it a lot harder to distinguish legitimates traffic from fake traffic and so making it harder to stop the fraudulent activities without incurring high false positives.

2) Post postback friction
As the name suggests, post install methods only come into effect after an install has happened, and might be processed days or weeks after the install. That also means that they are evaluated after an install postback is sent to the media source, which means after conversion and billing notification in CPI campaigns.

The result is that the media source will charge for the now-known-to-be fraudulent conversion … unless a process of reconciliation is done. This process is often manual, messy, and a cause of great friction between ad networks and advertisers.

3) Non-optimized optimization
Ad networks often perform real-time optimizations based on initial success analytics: evidence of conversions such as app installs. Now, however, those optimizations will be skewed by fraudulent activities.

In effect, having been rewarded by fraud, they will now optimize for MORE fraud.

As an example, if publisher A drives more installs than publisher B for some advertisers, the network might prefer to prioritize publisher A over publisher B and send more ads its way. Now imagine publisher A is actually driving fake installs which are not prevented in real time (as happens in post-install detection). The network will funnel more budget to A over B.

Even if those fraudulent installs are detected post-install and reimbursed, the damage has already been done and goals will not be met because of the optimization changes and budget shift.

Singular’s solution: deterministic pre-attribution fraud decisions

Singular strives to have no false positives. We want to clearly identify fraud at a granular level. So Singular’s fraud results apply to actual individual installs, devices, and users, not blanket-level sources or publishers (although we can – and do – block those too).

We also want to find fraud as conversions or installs happen.

Anything less will suffer from the problems outlined above.

When we took time out earlier this year to consider everything, it was clear that we needed a different approach here. We needed something that would work in real time — install-time — and have an extremely low false-positive rate while still maintaining effectiveness.

To meet these requirements, we decided to disregard everything we thought we knew about ad fraud and look for something new. As we reported publicly last week, after an exhaustive search we found what we believed would be a high-quality deterministic fake install detection method that works at install time.

The new method we discovered depends on signals from the install device that allow us to verify that a user exists, they truly installed the app from the store, and they haven’t installed the app an unreasonable number of times (sorry-not-sorry, fraudsters who “install” an app on a phone hundreds or thousands of times).

Of course, once we found this method, we knew we needed to validate that it works as expected at scale, in the real world, on thousands of ad networks. To do so we tested with some of the most successful mobile publishers on the planet. And we validated our results against post-install metrics.

The actual implementation of our new fraud prevention method proved to have a tremendous effect on some of our customers, eliminating their fake install problem. (Find more about it in our report.)

In a later blog post we will share some more details about our findings, but it’s safe to say that we were blown away by the scale of the fraudulent activity we’ve found, and as more and more customers utilize the feature, the numbers are only going to grow.

Interested in learning more? Schedule a demo to go even deeper.

LinkedIn establishes a single source of truth for marketing performance

We sat down with LinkedIn‘s Senior Manager of Digital Marketing and Strategy, Jake Bailey, to discuss how they were able to align internal marketing teams with a single source of truth for marketing performance.

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Introduction

My name is Jake Bailey and I’m the Senior Manager of Digital Marketing and Strategy at LinkedIn. So we actually have had a great relationship with Singular. And we have been really happy with the integration so far.

Their Challenge

What’s really fun about my job is that I sit on what is essentially an internal digital marketing agency and so we get to see everything from our recruiting services and job services to our sales, learning, marketing services, as well as our member growth and app acquisition initiatives all in one place.

What is difficult about that, is each team has its own KPIs, it’s own targeting, its own data. But having one centralized place where at least the front end data can live has really increased our efficiency.

A single source of truth for marketing performance

Singular has been very smart in finding a niche in the advertising industry where we as a company, we at LinkedIn, don’t want to maintain APIs with every single app network out there, because it’s going to be way too hard. There is Google, there is Facebook, there are all these third party networks. And if we can pay a company like Singular to own those APIs and own those integrations we’re very happy to just pull that data from Singular back into our own systems to then build whatever robust capabilities we want on the backend.

Singular provides this great front end data across app attribution, as well as all of our other web data, and then we can pull it into one unified place and marry it with all our conversion data, downstream data, LTV data and we can have one Singular, you know attribution platform in place.

And being able to sit on a team that can see everything is really great from a growth perspective. But then having all the data in one place also really helps align us when we have our weekly meetings with executives, or when we have our quarterly business reviews with the top people at the company, having one centralized data source is really important.

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

CEO insights: Why creative fatigue isn’t as simple as it sounds

CEO Insights is a new column by Singular CEO Gadi Eliashiv focusing on some of the most challenging issues in scientific marketing.

Most sophisticated growth organizations we’re working with are placing an enormous importance on creatives. These companies usually have in-house design teams dedicated for making creatives, plus processes and metrics around the production and launch process.

All of it is designed to ensure optimized results.

These companies understand the power of creative optimization, and distribute shared responsibility for amazing creative throughout the organization. Designers have been educated about performance metrics, and they’re savvy enough to combine their art with science in the form of cold, hard metrics.

These top brands also have periodic meetings (bi-weekly or more) where the design team sits down with the marketing team. Together they carefully examine the performance of various assets, and find a balance between introducing new winning concepts, sustaining proven concepts, and eliminating bad ones.

More advanced marketers also apply particular conventions to how assets are managed and tagged, so that tens of thousands of creative variations can be grouped by a handful of key concepts, which helps identify key trends.

All of these workflows and analysis capabilities are available out of the box for our customers through Singular’s creative optimization suite, and it gives our customers an enormous edge. Click here if you want to learn more about that, or email me if you’d like to see a demo.

So: what is the right process?

One area that was of interest to me was the pace at which companies swap out creative assets.

When asking various companies, I got a range of answers from: “we don’t have bandwidth for that at all” to “we have a constant refresh rate.” Some companies update on a fixed period of time (every two weeks or a month), while others update their creative “whenever design creates a new one.”

Obviously, not all creative costs the same to produce, and some creative is super expensive to produce in time and money like playables and videos. Other assets, however, can be produced quickly and efficiently, and when infused with time-specific context (such as a big concert, or a particular live event in a game) they can produce great results.

A common theme I’ve heard is the following way to run analysis on your creatives:

  • Cadence
    • Weekly or bi-weekly
  • Data input
    • Creative asset performance from all channels (Singular does that out of the box: check out our API)
    • Campaign targeting option data, particularly around the major self-attributing networks, to identify targeting methodology (value optimization, bid optimization, etc. …)
    • Channel, country, region, plus any other breakdowns that makes sense to you
    • Four weeks of data
      • Period A: first 2 weeks of data
      • Period B: second 2 weeks of data
  • Two simple data outputs
    • Check the trend of currently running creatives to detect big drops that might suggest these creatives should be cycled.
      • The drops could be in clicks, installs, eCPM, or any other metrics that make sense
      • For customers using Singular’s attribution, we enable ROI granularity all the way down to the creative level, so you can check for a drop in your main KPI (which is often what the ad engines optimize against)
    • Isolate the creatives that did not exist in Period A, but existed in Period B, and identify how they are trending. Learn from new concepts that are succeeding well, and some that are failing to ramp up.

One example:

Creative Period A Period B
  CTR     Conversions     eCPM     CTR     Conversions     eCPM  
Creative 1     3% 7,500 $9.50 1.5% 3,300 $11.75
Creative 2 n/a n/a n/a 3.5% 15,000 $11
Creative 3 n/a n/a n/a 1.5% 3,400 $9
Creative 4 1% 2,200 $3.40 2.3% 4,300 $4.23

Creative fatigue and time

As I look at all this data, the questions I keep asking myself are:

  • When is the right time to swap creatives?
  • Do companies know those times?
  • Can they even figure them out?

The answers to those questions, as I found out, are very complex. After dozens of talks with top tier marketers I got literally dozens of answers, and none of them was the silver bullet I was hoping for.

(Mostly likely, there isn’t any one single silver bullet. The techniques that work for one app are different than those that work for another brand.)

The one common thread in all these conversations was the favorite topic of creative fatigue detection. The formal definition of creative fatigue is that consumers/users/customers do not even see your ad anymore. They’ve become so used to it, that it is now just part of the default background for them.

Traditionally, the first thing people think about fatigue is that CTRs drop over time, because people have seen your ad again and again, and those who wanted to click have done that already.

But when I started researching some data, that naive assumption quickly surfaced as being incorrect.

When dealing with optimizing algorithms like Facebook’s and others, they will track the number of exposures each user had seen (frequency) and will cap that at a certain point, because their algorithm understands that it’ll be a waste of an impression, and also lead to a bad user experience.

So FB simply chooses another ad to show.

You can quickly see this phenomenon in the chart below.

In the first chart, CTR does not drop appreciably throughout the campaign. A campaign manager who looks only at this probably thinks that all is well with her ads.

CTR over time: no creative fatigue?

But there is actually a significant problem.

What’s actually happening behind the scenes is that Facebook knows that it has exhausted your chosen audience, and the number of people it is showing the ad to has dropped precipitously:

Creative fatigue … sometimes, Facebook is smarter than you

It’s important to say ads will not always behave that way. That’s why when analyzing fatigue you need to not only know what assets you’re using, but also what ad channels you’re running on, what bidding methodology is being used, and what their algorithms do.

(For example: due to saturation, the algorithm could also start increasing the CPM bid to generate more impressions, which will decrease your ROAS).

In general, even if these algorithms are smart enough to avoid audience fatigue, it is still the responsibility of the marketer to identify it and remedy the situation. You can find new audiences, add new creatives, and so on.

But there can be more going on

Sometimes when you’re looking for creative fatigue you’ll see data that doesn’t make sense at first. For instance, you might have a click-through rate chart like this one, which shows creative gaining strength over time:

Creative fatigue: can ads gain in CTR and conversions over time?

All looks well at first glance. But … if you check impressions, there’s clearly something else going on. The number of impressions is skyrocketing:

Creative fatigue: Oops, impressions are skyrocketing

Something very different is going on here.

Hint: this behavior can be related to changes in bids and budgets … another key thing to think about when testing for creative fatigue. Changing the bid (even if it’s a CPI/CPA bid) will directly impact the amount of money you’re willing to spend on a certain impression, therefore creating more impressions that were not accessible before at your previous bid.

In short: creative fatigue is one of those concepts that seems easy to understand and easy to diagnose … but actually isn’t. To find out if creative fatigue is actually happening, you need to dig deeper into the data than most can or will.

Fortunately, that’s where Singular can help

What’s next

That’s it for this post. In the next post, I’ll look more at how bids and budgets impact click-through rate, impressions, and conversions.

 

Singular CEO Gadi Eliashiv on chief growth officers and the rise of marketing intelligence [video]

Over the past decade we’ve seen the rise of the marketing technologist, who has one foot in the marketing department and another in engineering. And we’ve seen the data scientist role jump from almost nonexistent to being one of the fastest-growing jobs in just a decade.

Increasingly, as marketing is changing, technology is central to how marketers perform. Growth is now a key unifying function in brands and enterprise, and we’re also seeing the rise of the Chief Growth Officer.

We’re releasing a report on that in about a month.

But … our CEO Gadi Eliashiv gave a sneak peak at some of the results recently at Mobile Apps Unlocked in Las Vegas.

The rise of chief growth officers

Ultimately, the way chief growth officers lead their organizations is by using data-driven insights. Some of the most successful leaders drive those insights via marketing intelligence platforms like Singular.

The primary function of a marketing intelligence platform?

To provide insights for growth by connecting effort with outcome at granular and aggregate levels.

Ultimately, that’s how CGOs and other growth leaders get the score. Understand if they’re winning or losing. And know at both as high level and as granular as they want: how successful are our marketing, our campaigns, our ads, our creative.

Knowing that — and getting smart insights for optimization — powers breakthrough improvement in conversions and ROI. And that’s exactly what most brands, enterprises, and companies need.

Finished the video?

Click here to get a demo. See how Singular enables unprecedented growth for the most sophisticated marketers on the planet.

Personalizing your fraud prevention strategy with Singular’s custom fraud rules

Mobile ad fraud is an ever-growing threat to marketers, with fraudsters continuously evolving attack techniques. The exact figures for how much ad fraud costs marketers is highly debated, but eMarketer’s Digital Ad Fraud 2019 states that the estimated impact ranges from $6.5 – $19 billion annually.

To navigate this complex problem and effectively prevent ad fraud, marketers need to have an understanding of the techniques used by fraudsters and employ an always-on fraud prevention strategy that proactively rejects fraud. Otherwise, ad fraud can be detrimental to marketers in two key ways: one is the wasted ad dollars on installs that are either fake or hijacked, and the other is dirty data that is inaccurately skewed towards fraudulent networks instead of high-value networks or organic traffic.

Fraud prevention that adapts and reacts

Singular’s industry-leading Fraud Prevention Suite is built and maintained by a highly-skilled set of scientists that are dedicated to staying one step ahead of ad fraudsters and their attack methods. The Fraud Prevention Suite provides a proactive approach to detecting and combating ad fraud at scale.

Singular’s fraud prevention dashboard

With Singular’s rules-based fraud prevention, marketers can automatically apply deterministic rules in real-time to block installs before they are attributed to a fraudulent ad partner, or flag activity that is suspicious for further investigation. Automatic fraud rejection gives marketers peace of mind from knowing their ad dollars are always protected and eliminates the need to spend time reconciling ad network invoices.

Singular’s Fraud Prevention Suite not only comes pre-packaged with industry-leading Fraud Prevention Rules but also offers marketers the flexibility to define their own rules, what we call Custom Fraud Rules.

Personalizing with Custom Fraud Rules

With Singular’s Custom Fraud Rules, marketers can personalize their fraud prevention strategy to meet their brand or apps unique needs. For example, they may want to have a more aggressive approach to combating fraud if they’re advertising in markets that are more susceptible to ad fraud, or even if they’re testing new networks, each with varying levels of fraud.

Marketers can create Custom Fraud Rules by defining the conditions and rules that trigger automatic rejection of attributions or flagging of suspicious activity. The flexible rule builder allows the marketer to define multiple conditions that need to be set for the rule to trigger on a touchpoint, enabling them to implement a variety of personalized fraud-fighting rules.

Some examples of Custom Fraud Rules marketers have implemented include:

  • Publisher Blacklisting
    Select specific sites to blacklist from your campaigns. While you will also want to confirm your site blacklists directly with your partner, this rule gives you the power to reject traffic that comes from unreliable or underperforming sites.
  • Fingerprinted Traffic Whitelist
    Define and whitelist traffic sources that are trusted enough to send fingerprinted installs. Automatically reject or flag fingerprinted installs from install sources that are less reliable.
  • Block Unauthorized Store Installs
    Most Android apps are only published on the official Google Play Store. Automatically reject Android installs that came from an unauthorized store.

The flexibility of the Fraud Prevention Suite also allows you to add additional fraud checkpoints. These rules take known characteristics of your apps and campaigns, and allow you to quickly filter out traffic that doesn’t meet your standards.

  • Country Mismatch
    Use this rule to automatically reject or flag installs that take place in a country that your campaigns are not targeting.
  • App Version
    As you update your app version, it becomes impossible for new users to click and install deprecated versions. Fraudsters can struggle to update their attack to include the newest app version from the traffic they send, so blocking deprecated app versions can eliminate a source of fraud.
  • Time-to-install
    When the amount of time between an ad click and the resulting install is unusually small, it can be a sign that the install was hijacked by fraudsters. Similarly, when the time between a click and an install is too long, click spamming might be taking place. Set a custom time-to-install threshold based on the size and usage of your app to automatically reject or flag installs with unrealistically short or long install times.

Savvy marketers from top brands are already taking advantage of this personalized approach to fraud protection, which is paying off in significant cost savings.

Recently, a leading e-commerce app in the APAC region implemented a rule to prevent non-approved publishers and sites from sending fingerprinted traffic. After implementing the rule, 16% of the traffic sent from these sources was automatically flagged and rejected.

Another client, a global giant of gaming, set their iOS receipt validation rule on. When testing a new source, they found that 100% of the 11,000 installs were flagged and rejected for fraud. An additional benefit: no make-good negotiation was required … since the fraud prevention did not allow these installs to be attributed!

When fraud strikes, the marketers that leverage Singular’s Fraud Prevention strike back with high-tech fraud detection and prevention. But this is just the beginning. We’re dedicated to further innovating our Fraud Prevention to keep up with the changing face of ad fraud.

Want to see how much you could be saving with next-level fraud prevention?Reach out to your Customer Success Manager for a complimentary fraud audit.