We sat down with Jason Zhu, Director of Marketing at Zynga, to learn how his team currently uses Singular to determine the best marketing channels for acquiring customers, the main hurdles they’re overcoming, and the most important elements of a world-class marketing analytics solution.
My name is Jason. I’m the Director of Marketing at Zynga.
Zynga is a company that develops and publishes mobile games.
High-level use case
Zynga is currently leveraging Singular by using its campaign analytics to help us decide which marketing channels to use to acquire customers.
One of the marketing challenges that Zynga is facing is because we are working with so many channels—for example, Facebook, Google, and Twitter. Sometimes it’s difficult to understand exactly which channel has the best performance.
Single source of truth for performance
So Singular helps us with that by giving us an insight into exactly which channel has the highest revenue and which one is the highest performing. Singular helps us connect fragmented marketing data by giving us a singular source of truth.
For example, when we work with marketing we also work with cross-functional teams like creatives as well as finance. So by having Singular we have access to the same set of data across our teams.
It’s important for Zynga to optimize the marketing campaigns at a granular level, because when we are acquiring customers from all over the world, once you know what exactly certain customers have in common—so, by breaking it down to levels like age gender and what devices they’re using it helps us most effectively deploy our marketing budgets.
Cohort reporting is not limited to a month, three months, or even six months any more. Singular now supports a full year: 365-day cohort reporting periods.
In other words, if you’re doing cohort tracking or cohort analyses in verticals that need more data than a D30 retention rate report, you’re in luck. And if your marketing campaign time period is three to nine months, you can now get extra margin and extra insight in your cohort statistics.
Web-based marketers recognize cohort reporting from Google Analytics, where you can see your retention rate and the impact of your marketing efforts on the web. Mobile marketers need the same — in fact more detail — analytics in their mobile marketing reports.
More data makes you smarter. More data means your marketing campaigns bring in more revenue.
In mobile marketing, more data tells you invaluable information such as your customer life cycle. Your average revenue per group of acquired users. Your average sessions per user, by cohort. This goes far beyond vanity metrics and gets to the most important behavioral analytics that define user lifetime value (LTV).
That’s why cohort analysis tools are so vital, and how marketers looking at a cohort table can see trends and create opportunities that, like product improvements, can have cumulative benefits.
I recently took some time to talk about the change with Singular VP of Product Alon Nafta.
John Koetsier: Singular recently updated its cohort lengths to 365 days. But before we talk about why, let’s talk about cohorts. What are the primary reasons to do cohort analysis, and what do marketers learn from them?
Nafta: When we say cohorts it’s important to define first what these are in the context of marketing and user acquisition.
By definition a cohort is a group with shared characteristics. In the context of user acquisition, a cohort often refers to users acquired in similar manner. At the most basic level this could be the date, but it can extend to the marketing channel, the campaign, and even the creative. (Imagine what knowing which creative a cohort responded to could tell you about that group of people.)
A cohort report takes these groups of users, and looks at how they behave over time, say after one day, one week, one month and so on.
By “behave” we often refer to retention or any other important KPI you want to measure your product by. That gives you a very clean view since for example different dates can correspond to different marketing activities or product releases. And different channels, campaigns, or publishers may result in an acquired user profile that can be dramatically different from each other.
So a cohort analysis also helps me establish my baseline — for example, how my organic users are behaving over time — and benchmark acquired users for different campaigns to these profiles.
As a marketer, that can ultimately teach me if my paid acquisition is exhibiting the right results, and point me on what should I focus on when trying to improve. Equally important, it also gives me insight into how fast I’m earning back my acquisition costs, which ultimately is one of the most important things for effective paid marketing.
There’s just so much you can do with cohort analysis. It really is a fundamental tool for the mobile marketer.
John Koetsier: What are the primary ways to define a cohort, and when would you use each? Time of acquisition is of course one … what else is interesting?
Nafta: Interestingly enough, even time of acquisition is not a fully strict definition since acquisition is not just one singular point in time.
For mobile marketing, a common way is to look at the time (or rather, date) of install. But you can also define the starting point of a cohort by the timestamp of the attributed click or impression (AKA critical touchpoint), which tends to be the case for web marketers.
Some marketers, especially in the digital commerce space, may want to look at a different event such as registration or first purchase. They may consider that a much more meaningful and significant starting point in defining a cohort.
Once you define how the cohort is calculated, a good cohort analysis tool or report should give you as many breakdowns as possible to differentiate between important characteristics of these groups of users.
That includes data in a table or in visualizations around:
How they were acquired
Key properties of the acquisition campaign … the customer journey
Whether they were new users or retargeted former users
What types of post-acquisition activities are they engaging in, including the active user rate
Conversion rate to purchase or other value-creation activity
And more, depending on your app, your vertical, and your KPIs
Lastly, you also define how many time units — commonly days — you’re looking at after the defined start time of the cohort, and if you’re measuring accumulatively.
Note, this may differ between different types of activities. For example a 30-day retention cohort would commonly mean how many users came back on the thirtieth day after install (or re-install). But a 30-day purchase sum cohort can either refer to the total number of purchases made in the first thirty days (which is more common), or just the sum on the thirtieth day.
Both are applicable. Which you’re using needs to be determined to understand what are you looking at, and avoid confusion with coworkers.
John Koetsier: Does Singular make cohorts available based on re-engaged and re-attributed users?
Looking at cohorts for re-engaged or re-attributed users just as important as it is for newly acquired users. In fact for some verticals, acquired users in the sense of users who have just installed the app is almost not interesting at all for paid marketing, since almost everything focuses on re-engagement.
John Koetsier: OK, let’s get to the big story. Singular updated cohort periods to enable 365-day cohorts. Why?
Nafta: Well, as explained earlier, you’re trying to look at how different groups of users behave over time and draw conclusions accordingly.
In some cases you can draw conclusions or make some predictions based on a relatively short timeframe. For example, you might be able to predict the life-time value (LTV) of a user in a mobile game based on the first seven days of in-app purchases.
However, for some companies and products, the window of interesting activity that is important for prediction may take a much longer time.
For example, if I do a monthly subscription for my fitness app, I’ll probably need to review at least three to six months to understand how users are engaging, retaining, and upgrading their subscriptions. Or, if it’s a digital commerce product where customers are buying more expensive items that you typically don’t buy daily or weekly, marketers likely need to look at several months, half a year, or even a full year of data to be able to produce high-quality conclusions and predictions.
These numbers are extremely important for data science teams, who are often tasked with modeling LTV as well as LTV prediction. More time allows them to improve their models, test them better against reality, and iterate accordingly.
John Koetsier: What business types or app verticals typically benefit most from longer cohort periods?
Nafta: It really varies but I think it ultimately comes down to the expected activity profile beyond retention for your product. If users are taking actions on a monthly or longer basis, one-year cohorts — and even longer — are extremely important.
We see this for subscription services, digital commerce, fintech, and even gaming (with varying impact from hyper-casual to mid-core to hard-core games). It also depends on the level of sophistication and effort companies can invest.
Longer cohort periods produce more data and can allow better models, if you have the resources in place to take advantage of it.
John Koetsier: Often we look at cohorts individually over time to see return on ad spend (ROAS) for a group of acquired users. What else can you learn by tracking individual cohorts?
Nafta: ROAS is only one metric. It’s very meaningful of course to marketers who are working on paid sources. But cohorts are also meaningful to product managers, since by looking at retention against product release dates I can learn quite a bit about how my releases affect retention and adoption.
It’s also important for understanding seasonality, and many more insights.
A different creative asset, for example, which shows a different item to be purchased, uses a different coupon or offer, or just uses different design — such as more straightforward versus more artful — can say a lot about the types of users you’ve just acquired.
This is important data collection for mobile marketers, and an analysis report with insights here often leads straight to increased revenue.
John Koetsier: As you’ve said, cohort analysis is pretty important for LTV analyses. Does Singular automatically surface LTV and ROI for cohorts?
Nafta: Yes. By default we surface three important KPIs in our cohort report: LTV, ROI, and CPE (cost per event) for every event a marketer has defined as interesting.
Of course, a lot can be customized to meet individual needs. And specifically for retention we have a designated retention report which shows the same cohorts.
John Koetsier: Anything else?
Nafta: At Singular, cohort reporting is at the core. Our philosophy is to ensure that everything can be reported in a cohorted manner.
While reporting by date — which we sometimes refer to as actuals — can give you insight especially in real time, reporting against cohorts is what truly uncovers the outcome of your marketing. This includes being able to attach the cost of acquisition, the type of campaign, ad set and ad, the bid, the strategy, and the type of campaign.
And of course … what all of these are generating in terms of business results.
Every day, we’re bombarded with stories about data breaches, successful hacks, and privacy violations. The world of advertising attribution is not immune to any of those.
Just in 2019, there have been 63 breaches exposing 100 million records in the fintech sector alone, according to the Identity Theft Center. Plus 363 in the medical field and 59 in government/military. Add in all the other sectors, and there have been over 1,000 breaches exposing data on over 146 million records … just this year.
Every attribution provider has to maintain a critical set of design principles and methodologies for building, testing and auditing every single part of their platform to maximize data security, and that’s exactly what Singular has done.
It’s true: there is no silver bullet in security.
But prioritizing security when building products, together with comprehensive security knowledge and organization-wide awareness will minimize the chances for a breach.
Serving the world’s top advertisers, and with our engineering team composed of cyber security veterans, we are more than equipped to secure your most sensitive data. To date, Singular has never had a security breach.
That’s good news, but we’re not resting on our laurels.
In fact, under GDPR regulations, attribution providers now have the regulatory requirement to report on any security incident within 72 hours from the time of breach.
Authentication is also of vital importance: your attribution provider must support two-factor authentication, single-sign on, and strong passwords to ensure your marketing data stays private.
Audits and pen testing
Security experts agree that periodic audits and penetration testing by respectable parties is another great tool to evaluate how secure your provider is with handling your data.
You have the right to see these proofs, and an honest vendor will be happy to show them to you. (So yes, you can ask us!)
Advertising attribution and privacy
Privacy, although often coupled with security, is a requirement on its own. It may not be something that most people think about when they think of an advertising attribution provider, but it is something we think about at Singular.
There are a number of important factors to consider around privacy:
Everyone says they comply, but Singular goes the extra mile. We comply with GDPR, CCPA, COPPA, and other standards. And we enable privacy-related requests such as Right of Erasure and Right of Access programmatically through a set of API endpoints. That’s scalable privacy.
Respecting your users privacy is critical
You need to protect your users’ privacy at all costs. That includes SDK-based methods to cease tracking for under-age users or users who did not consent. It also includes never, ever mixing one customer’s user data with another customer’s dataset.
Hint: if your attribution provider is touting people-based attribution as a core feature, you might want to ensure that no generally-available device or user graph is being enriched at your expense. And, maybe more importantly, your users’ expense.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
Without complete and accurate ad monetization data, app marketers can’t optimize growth decisions. Today, that means they need to include ad monetization data as well as in-app purchase data in their ROI calculations.
App marketers need to measure ad revenue on two distinct levels: aggregate and granular.
Granular ad revenue
Mobile app marketers need the ability to see user-level ad revenue so that they can accurately understand their return on ad spend for user acquisition. Without granularity you won’t know which users are ad whales … and which are ad duds.
Plus, you’re not sure where they came from. Or where to get more of them.
“Granularity is critical in mobile ad monetization,” says Singular CEO Gadi Eliashiv. “Understanding the relative value of their ad impressions helps mobile publishers optimize their apps for maximum revenue. It also helps them improve user experience by making decisions that can minimize irrelevant and wasted ads.”
Aggregate ad revenue analytics
You also need aggregated data so that you know exactly where you stand with ad-based revenue across all partners, plus of course IAP and product/service purchase data. Only then can you get a full picture of your growth efforts.
You also need to understand ad requests and fill rates.
“Smart companies check the fill rate all the time to optimize their waterfall,” says Singular CEO Gadi Eliashiv.
MoPub’s new impression-level revenue data
Singular has been providing ad monetization services for almost a year with a variety of partners, including IronSource. Now, MoPub is providing revenue information to mobile app publishers for every single ad impression. Not only that, MoPub is also surfacing what supported demand source was able to fill the ad slot and what country the user is in.
This is extremely powerful.
With this data, you can understand ad-based life-time value of your users. That’s increasingly important, because just 2% of mobile app users are converting to paying customers via in-app purchases.
Ads are one of the key drivers of monetizing the other 98% of your users, so it’s no shock that ad monetization is increasingly critical for app marketers. In fact, 60% more apps are monetizing through ads in 2019 compared to last year, and ad revenue now represents more than half of many app publishers’ total revenue.
Starting today, Singular supports MoPub’s impression-level revenue data product, enabling marketers to measure granular ad revenue.
The result is better data precision, more accurate and complete LTV models, superior user acquisition and monetization strategies, and ultimately, the potential to earn more revenue.
And all of it, of course, right inside your Singular dashboard, providing a single pane of glass to understand your cost and revenue.
In the simplest possible terms, a chief marketing officer’s role is to implement strategy that ultimately increases sales. A chief growth officer’s role is even simpler and more explicit: grow the company.
And what tools do they need to achieve those goals?
Singular is privileged to work with growth marketers at companies like Lyft, LinkedIn, Rovio, Wish, AirBnB, DraftKings, StitchFix, plus many more. We’ve seen what the best growth marketers the planet do, and we know what technology they use.
“Marketers are drowning in data,’ says Jo Ann Sanders, a VP at Optimizely.
That’s the problem.
“With the exponential growth of data over the past decade … it’s becoming harder daily to turn information into action,” says SurveyMonkey CMO Leela Srinivasan.
Marketers are drowning in data thanks to the unprecedented data exhaust of our digital lives.
We browse the web, we install apps, we watch four million videos on YouTube every minute, we search on Google 40,000 times a second. The world will soon have almost six billion mobile subscribers, and American adults now spend more than 3.5 hours a day on their phones in branded apps, sponsored media, and ad-supported sites.
At the same time, marketers are dealing with an exponential rise in tech tools, more digital channels than ever before, and more billion-user platforms every year.
Add in global competition, and 76% of CMOs say they can’t measure marketing performance accurately enough to make truly informed decisions.
Marketing intelligence platform
What marketers need most is actionable insights for growth. So CMOs’ (and CGOs’) biggest challenge is simply mining nuggets of gold from all that data. That requires real-time measurement and analysis at scale across potentially hundreds of platforms, partners, and channels.
That’s why Singular built what we call a Marketing Intelligence Platform.
The new marketers are different. They speak data and write code. They form hypotheses and run experiments; then measure results and optimize. These new marketers are marketing scientists, and they need tools of their trade.
With a Marketing Intelligence Platform, marketers achieve three critical things:
Unprecedented visibility at scale
On-demand flexible reporting
Full customer journey insights
That’s seeing not just your data, but your ROI on every activity. It’s slicing and dicing not just by campaign, but getting CAC per creative asset. And it’s measuring not just conversions, but cross-device and cross-platform journeys that led to customer action.
This requires at least nine components, combined into a single platform, grouped in three sections. We’ll take a very brief look at each. For a full in-depth overview, however, check out our complete Marketing Intelligence Platform report.
The three things that CGOs and CMOs need to drive and accelerate growth are …
One: Unified marketing data
You can’t get the golden nuggets of actionable insights without mining your data, and that starts by unifying it.
Unifying marketing data includes:
Dimensional data combining/synthesis
Data governance ensures clean data from every source, and enables processing, enriching, and combining later on.
Ingestion is getting all your relevant data from every source, and it’s not easy. Processing is essential to standardize and normalize it, at which point you can conversion outputs to marketing inputs. Combining and synthesizing top-funnel and low-funnel data reveals deeper trends and granular results.
Two: Intelligent insights at scale
At a high level, marketers need to know the score: across all their campaigns, are they winning or losing? At more granular levels, they need to know if a specific campaign, partner, publisher, or creative is performing.
Generating intelligence insights includes:
Reporting and visualization
Reporting and visualization shows marketers what’s happening, and actionable insights provide clues for future profitable growth. Some of those insights are pull, but some need to be push: alerts about out-of-scope campaigns, click-through rate drops, poorly performing ad partners, and so on.
The volume of data flooding marketers’ dashboards, reports, and spreadsheets cannot be handled manually at scale. Automation is required, and it includes:
Alerts, fraud, audiences
And much more
It is not useful to have a system that only ingests data. Marketing data needs to move from systems of deployment to systems of analysis to systems of engagement, and sometimes in multiple directions. So building in the ability to do that via API, exports, or S3 to internal BI systems and hundreds if not thousands of external partner systems is critical.
And while modern scientific marketing is not a set-it-and-forget-it activity, marketers increasingly need to be able to automate actions within set parameters.
That includes automated creation and distribution of audiences for retargeting, look-alike campaigns, or suppression lists. It also includes built-in on-by-default configurable mitigation of fraud, along with both whitelisting and blacklisting of sources and publishers in paid media campaigns.
And at higher levels, it includes automation of bids and buys for ad campaigns at scale.
Results: what a marketing intelligence platform delivers
What does a marketing intelligence platform deliver?
Find out soon in part two of this blog post, coming next week.
The following is an in-depth overview of the growth strategies of a very well respected and profitable online business in India. This is a Singular customer who runs a transaction-oriented business, and the data is used with permission.
In the last 15 months this advertiser has accrued approximately 13 million installs on both Android and iOS devices. What is impressive is that they have focused on checking fraud right from the get-go and only paying the partners on Cost Per Transaction (CPT).
Here are some key areas this advertiser focuses for user growth.
Focusing on Android for mobile user acquisition
It’s no brainer – Android is the winner in India market and therefore is this advertisers’ key focus area.
iOS marketing efforts were light until this publisher accrued a respectable user base on Android.
Paid beats organic
Organic ranking is good; but paid marketing gets you there faster The advertiser has been very comfortable with a lower rate of organic installs.
When they started on this journey, they had approximately 35% organic installs on Android and 99% organic installs on iOS. Over the last 15 months, they have experimented with various sources and found the best ones for their vertical. After 15 months the advertiser finds they now have approximately 10% organic installs on Android and 60% organic installs on iOS.
Reducing the organic installs percentage can sometimes be perceived as cannibalization but given that the advertiser is focused on a cost per transaction, they ensure that these paid installs are coming with linked transactions which translates directly to revenue per user.
For mobile user acquisition, in experimentation you must trust
The advertiser started with 20 sources on Android and two sources on iOS. (For the purpose of this case study, a source is considered valid only if it delivered more than 100 installs in the calendar month.)
As the paid user acquisition program saw success the advertiser has scaled to 40 sources for Android and 15 sources on iOS.
On an average the advertiser tests configurations with over 180 sources. Once the tests have been successful, the advertiser goes live with the chosen few sources every month. This eye to detail and diligence in evaluating sources gives the advertiser an edge in optimizing spends and driving growth.
Singular helps the customer test new sources with the following:
The ability to add additional information in postbacks enables advertisers to know what works and enables them to further segment acquired traffic.
1,600+ network integrations
Singular is integrated with 1,600 plus networks. Chances are that a network the advertiser wants to evaluate is already integrated without any custom work, which boosts speed of execution.
Singular has a dedicated customer success and support team that helps with configuration. This team is governed by an SLA, meaning that the customer is not alone in this never-ending effort. Having Singular’s team available to action changes makes the UA manager a winner.
The following is a cumulative growth graph of the app installs that the advertiser has driven in the last 15 months.
Detailed logs and their usage
Singular’s platform enables the customer to extract detailed Click, Install, Postback, and Fraud logs via the interface, API, and firehose methods.
These logs are used for log for log validation in case of discrepancies. The API and firehose methods are used to have a complete repository of the data. This repository is processed in the customer’s internal BI system to manage the payouts and make goods with the advertising partners.
Singular has world-class fraud protection, but if they wish, customers can also work with a third party install fraud detection service by using the attribution logs. This gives them complete control of how advertising spend decisions are made.
Take these tools for a test drive?
Interesting in experimenting with Singular’s marketing intelligence platform to see you you can drive similar results?