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:

CreativePeriod APeriod B
  CTR    Conversions    eCPM    CTR    Conversions    eCPM  
Creative 1    3%7,500$9.501.5%3,300$11.75
Creative 2n/an/an/a3.5%15,000$11
Creative 3n/an/an/a1.5%3,400$9
Creative 41%2,200$3.402.3%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.

 

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

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

  1. Accuracy
  2. Granularity
  3. Actionability

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

This is what we call “dual integration.”

WTH is the Dual Integration approach?

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

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

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

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

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

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

dual integration singular

Singular’s dual integration visualized

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

Sounds like a pain in the @$$?

Good thing Singular has already done it for you!

This is the dual integration approach

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

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

Inherent flaws with tracking links

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

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

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

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

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

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

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

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

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

Jam City optimizes campaign performance and creative strategy with Singular

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

Video

Transcription

Introduction

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

Connecting fragmented marketing data

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

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

Aligning with Creative Product Marketing

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

Next-level Performance Analytics

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

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

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

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.

Singular’s ROI Index Ranks the Top Mobile Media Sources

We’re excited to announce the second edition of the The Singular ROI Index, ranking the top-performing ad channels driving the highest quality ROI for mobile marketers on iOS and Android. This is the largest study of its kind with over $1B in revenue, 315M installs, 1.7K+ apps, and 1.2K+ mobile media partners analyzed. The rankings account for three factors:

  • Quality Factor (based on ROI or revenue divided by cost)
  • Scale Factor (total ad spend)
  • Fraud Factor (penalty based on the percentage of fraudulent installs driven by a media source)

You asked, we answered: We’re bringing you even more granular insights. This year’s edition includes vertical and regional breakdowns of the Top 15 ranking mobile media sources. Singular is uniquely positioned to offer the most comprehensive set of ROI data as the leading mobile attribution and campaign analytics platform used by top marketers at Lyft, Yelp, Rovio, Postmates, Airbnb, and Blizzard.

Comparing Singular’s ROI Index 2016 to 2017, many movers and shakers appeared. In terms of platform ROI, iOS held its lead for the Gaming category; however, Android made significant headway in closing the gap. A number of other players achieved considerable growth in both volume and return as newcomers burst onto the scene, big names increased their game, and video-focused media sources continued to climb the ranks.

Movers and shakers include:

  • Snapchat Enters the Scene on iOS: Snapchat debuts in the rankings as marketers increased their spend on iOS driving the 6th highest ROI for the Non-Gaming category and the 15th highest ROI across all verticals
  • Apple Search Ads Accelerates into the Top 5: Apple Search Ads captured a larger share of digital marketing budgets, rising from the 23rd rank to the 6th highest-volume media source on iOS from 2016 to 2017
  • AdWords Takes Back the Top Spot on Android: While Facebook surged to the top spot in 2016, AdWords took back control as the top leader on Android in 2017
  • Facebook Edges Out AdWords in EMEA: AdWords held the top spot in Americas and APAC on Android, but Facebook beat out AdWords in EMEA
  • Twitter on the Rise: Twitter delivered the 2nd highest ROI across both Android and iOS in the Non-Gaming category
  • Top Video Media Sources Keep Climbing: Video media sources – Vungle, Unity Ads and AdColony – climb into the Top 5 on Android and iOS
  • iOS Dominates in Gaming: iOS drove 1.2X higher ROI in the Gaming category, however Android is quickly closing that gap driving 8% annual growth in ROI

For the complete list of rankings including the Top 15 on Android vs. iOS, download The Singular ROI Index now.

Postmates’ Tips for Analytics-Driven Growth in the On-Demand & Ecommerce Vertical

Singular sat down with Patrick Witham, Director of User Acquisition at Postmates, to get a few of his tips on analytics-driven marketing in the on-demand and ecommerce vertical. Thus far in 2017, Witham and his team have aggressively scaled Postmates’ marketing efforts while achieving dramatic cost savings on new user acquisition.      

Spending & Cost Management

Postmates is a double-sided marketplace and manages user acquisition with separate portals for fleet drivers and for consumers. These portals help Postmates grow both sides of the market and maintain a state of equilibrium between supply and demand.

User Targeting

Test aggressively on Facebook in terms of your demographic. Once you have identified it, you can start optimizing your spend to make it more efficient. Postmates has a well-defined view of what our customers look like. Many live in high-density urban areas, which makes targeting easier but also requires more granularity. In verticals like gaming, you can think of a country as a geo. You don’t have that luxury with on-demand, which forces you to get a lot more specific. 

We’ve been very successful in large urban areas. Of course, moving forward we are considering smaller cities (there are only so many cities like New York or Los Angeles).

Data Enrichment 

Having clean data is imperative — which is achieved through consistent naming conventions for campaigns and ad-groups that make sense for your business.

Testing

Postmates relentlessly tests creative and targeting tactics. At any given time, we’re running at least a few dozen experiments to test out hypotheses the team has come up with. A piece of advice is to make sure you set up learning experiments properly. When you enter a new market that you don’t necessarily know, you should test as many hypotheses as possible. However, a lot of companies do not take the time to set a proper control group or make sure they achieve statistical significance on these experiments.

Always test new channels and be at the forefront, otherwise you’ll get left behind. Of course, there is a tradeoff between going deep into a few channels and testing everything with a shotgun approach. We set 20% of our time (including BD, relationship management, media buying, experimentation) to new partners that we think could be promising.

Singular has been instrumental in helping us better understand our strategy and help us be more efficient. Getting out of Excel with our reporting and into an automated platform was magic.

Download The Singular ROI Index to see the world’s first ranking of ad networks by app ROI.

How To Shrink Your Marketing Analytics Stack

For top mobile brands, marketing analytics has become dramatically more in important in recent years as the emergence of a multitude of tools to track advertising spend, attribution, creative performance and in-app analytics has advanced mobile app marketers’ ability to measure their UA efforts across channels and optimize their campaigns.  

Digital Marketing Analytics Are on the Rise — But “Utility” Is Stagnant

Despite the challenges digital marketing teams face in corralling and crunching performance data, they recognize the growing importance of marketing analytics in driving decision-making. A recent survey of CMOs commissioned by Deloitte and The American Marketing Association found that companies plan to increase their spending on marketing analytics by 376 percent in the next three years.

Yet while most companies have increased their focus (and budget) on their marketing analytics stack, and the sophistication of analysis tools has grown, for many companies, the investments are yet to pay off. According to data derived from the same survey of CMOs, the use of marketing analytics in decision-making has actually remained stagnant for the last five years.

Marketers surveyed in the study cited a lack qualified people who can utilize marketing analytics tools as well as a lack of data tools measuring “success” through analytics as the primary factors preventing their companies from using more marketing analytics.

Fragmentation Follies

A separate survey commissioned by Google found that only 13 percent of digital marketers were confident in their ability to measure marketing performance data. The number one reason marketers gave for why they have such a hard time exposing marketing performance data is a lack of integration between their marketing analytics tools.

Fragmentation in the modern-day marketing stack creates workflow inefficiencies as well as holes in performance data. Simple tasks like aggregating performance data across channels often require marketers to toggle between multiple dashboards and manually update metrics in unwieldy Excel files. The process is particularly painful for marketers who wish to analyze their performance data not merely by click-through rates or raw install metrics but rather by the actual quality of those users, as measured by ROI.

Hence, Singular’s name serves as a constant reminder of our mission: To build a single marketing analytics platform that unites all your disparate data feeds, enabling marketers to do their jobs more efficiently and more effectively.

In essence, when your marketing analytics are centralized under one single source of truth, your stack’s output becomes smaller and more manageable, making reporting, analyzing and optimizing performance across channels less error-prone and time-consuming.

Take cross-device attribution, for instance. In the past, we’ve seen marketers make investments in the wrong marketing campaigns because cross-device data was not properly integrated into their marketing analytics. This highlights the fact that just because a campaign drives high performance metrics on one device or platform, doesn’t mean it drives high performance metrics on other devices or platforms. When user engagement data across devices and platforms is taken into account, marketers are able to expose the true ROI metrics of their campaigns to drive better spending decisions and optimizations.

Analytics-Driven Experimentation, Without the Data Deluge

At the core of effective UA strategies is experimentation. As new channels, media formats and targeting strategies emerge, they can lead to outsized returns for marketers, especially in their early days. For example, when they first launched in 2016, Apple’s auction-based Search Ads drove low CPIs and high conversion rates as demand for Search Ads remained relatively low and curious users noticed the new ad slots.

Thus, the advantage of testing new channels and media formats is clear – but marketers must do so in a way that is deeply integrated with existing analytics systems. Yet due to the lack of standardization among ad networks, extracting detailed data from partners for analysis often requires custom integrations and constant maintenance. This can add layers of complexity to your analytics stack — which is why outsourcing partner integrations to a third-party analytics and attribution provider will allow for smarter testing and analysis without the data deluge.

Using this approach, one Singular customer says their team dedicates roughly 20 percent of their time to experimentation with new partners that they think could be promising, including BD, relationship management, media buying and testing. The remaining 80 percent of their time is spent investing in larger platforms they know well. In both cases, Singular serves as the analytics backbone, providing the most detailed and flexible performance data across nearly 2000 networks, thereby allowing the team’s stack to stay small, nimble and powerful.

Download The Singular ROI Index to see the world’s first ranking of ad networks by app ROI.

3 App Attribution “Gotchas” To Watch Out For

Mobile app attribution is one of the cornerstones for growth-oriented apps and a critical layer in the mobile marketing stack. Roughly 80 percent of the Top 500 mobile apps on iOS have implemented an attribution solution, according to a study by mobile app analytics software Mobbo.

In short, mobile app attribution allows you track the source of incoming app installs or engagements. To identify the channels of user acquisition that work best in the long-term, attribution also covers in-app events that occur after the download, also known as post-install events.

Yet when it comes to mobile app attribution, there are “gotchas” that can trip up even the most seasoned digital marketers, leading to wasted time, skewed or opaque analytics and under-performing campaigns.

App Opens vs. App Installs

Marketers must keep in mind that mobile app attribution systems define an “install” as the first time the app is opened on a user’s mobile device. In fact, a mobile app open is the earliest time a third-party attribution platform can track a new user, so they take this first open and call it an install. The reality is that the only systems that know about actual installs are the app store owners, Google Play and Apple iTunes.

As a result, discrepancies often exist between the statistics in your attribution platform and App Store dashboards. For instance, a user might have installed the mobile app on Tuesday, but launched it a few days later on Friday. The App Store dashboard would attribute the install to Tuesday, while the attribution platform would attribute the install to Friday. Or if a user installed the app, but never launched it — attribution platforms wouldn’t register the download, while App Store dashboards would.

While marketers should seek to reduce mobile app install data discrepancies wherever possible, it’s important to recognize that a host of reasons make minor data discrepancies inevitable. Marketers, then, are tasked with identifying thresholds for acceptable levels of discrepancies. When a discrepancy between two data sources — for instance, your attribution platform and your network dashboard — exceeds a certain threshold, it usually means something is wrong and needs fixing.

What’s my App Attribution Window?

The Attribution Window is the amount of time that can pass between a user’s click or view of an ad and their install. Consider the example of a user who clicked a mobile ad on the 10th of December, but didn’t install the app until the 13th of December. If the attribution window is set for 3 days or more, the install will be attributed to the ad. But if the attribution window is set for only 1 day, the install will not be attributed to the ad.

Data discrepancies can arise when the attribution window in your attribution platform is not aligned with the attribution window in your network. In many cases, networks will set as a default an attribution window that is different than the attribution window in your attribution platform. It is advisable, first, to work with an attribution platform that allows you to customize your attribution window and, second, to ensure that you have the same attribution window set up in your ad network and attribution platform.

Who’s Click Is It Anyway?

Advertising networks don’t know about user interactions with ads on other ad networks. As a result, the same mobile install might be attributed to two or more ad networks.

Consider the following example: yesterday the same user clicked on a Facebook ad and then a Google AdWords ad before installing the mobile app today. In this instance, Facebook will take credit for the install in the Facebook dashboard, while AdWords will also take credit for the install in the AdWords dashboard.

Attribution platforms that operate according to a “last click” attribution model will “de-duplicate” the conversion and attribute the install to AdWords activity. In turn, a discrepancy can arise in the number of Facebook-driven installs that appear in your Attribution platform and the number of installs that appear in your Facebook dashboard.

To monitor such discrepancies, marketers should seek to work with attribution providers like Singular – which displays both figures, the statistics reported by the network and the statistics reported by your third-party attribution solution, alongside each other, instead of marketers having to toggle back-and-forth between their attribution platform and their network dashboards. In addition, using Singular, marketers can customize which source they want to use as the source of truth and set alerts when discrepancies between sources exceed a given threshold.

In sum, mobile attribution is complex – with a host of “gotchas” that can create major headaches when performing data analysis and performing optimizations based on inaccurate or misleading data. In order to succeed, marketers must stay cognizant of the intricacies and leverage partner tools that are both transparent and make it easy on marketers to spot broken campaigns and illegitimate data.

Download The Singular ROI Index to see the world’s first ranking of ad networks by app ROI.

Mobile Analytics 101: ARPU versus ARPPU

This post is designed to help mobile marketers get more ROI from their mobile app businesses by better utilizing the data and measures in their mobile analytics platform.

Here we are going to discuss how Average Revenue per User (ARPU) and Average Revenue per Paying User (ARPPU) can be used to make better investment decisions on App Install and Re-Engagement campaigns.

As always, we recommend using ROI (not ARPU or ARPPU) as the key metric for any effort to measure and optimize app marketing. ARPU and ARPPU can also be valuable mobile analytics because they provide guidance on appropriate CPIs for planning. They are critical components of ROI calculations.

Let’s start with simple definitions.

ARPU Measurement Defined

ARPU is one of the most useful measures in mobile analytics. It refers to the average revenue per user, meaning that it measures the total revenue driven by an app divided by the number of installs. You can use Singular to calculate this for all app installs, paid app installs, organic app installs or total/paid/organic installs for a particular time period. With Singular you can further parse your mobile analytics to measure ARPU data by country, vendor and campaign.

ARPPU Measurement Defined

ARPPU is a measure originally designed for subscription-based businesses, like a game that you pay a fee to use every month. The idea was to be able to examine the quality of paying game users by eliminating the free or non-revenue users from the math. This measure is particularly valuable for “freemium model” businesses where a small number of users are driving the lion’s share of app revenue. ARPPU data tends to be particularly relevant for game businesses that focus on sales of virtual in-app purchase sales (IAPs). Some chose to think of ARPPU as a measure of active users, but it’s more a measure of active payers.

ARPU Measurement and Install Campaign Vendor Allocation Decisions

ARPU is a powerful metric for both overall and comparative business analysis. Examining your ARPU data across all of your installs, or broad classes of installs like organic versus paid, helps you understand both overall business viability and the quality of your app experience. If, for example, you expected to drive a thousand dollars per user per year, and your business ARPU is running at $50 a year, you have experiential or other product problems that need to be addressed immediately.

Some apps are primarily designed not to drive revenue, but rather to improve overall user experience. These are usually non-game apps for industries like hospitality, where augmenting user experiences is seen as a way to drive loyalty and brand preference. An example would be a companion app for a hotel. Such apps often have relatively low revenue goals – perhaps to simply break even. In this case, you should compare your ARPU to your acquisition cost to see if your app is meeting this admittedly modest goal.

But ARPU data is primarily used to compare vendors and campaigns to one another to determine the quality of users that are being attracted. By examining user ARPU data from different vendors, for example, you can assess if certain partners are attracting higher or lower quality users/customers.

ARPU and ARPPU are both metrics you can calculate easily in the SIngular unified analytics platform.

Real-World Example: Average Revenue Per User/Per Paying User

Now let’s look at an example of how ARPU data can help you make better media allocation decisions.

Suppose you worked with just three media vendors to drive installs for your game. All were using the same creative in the same campaign. Over the course of 90 days, you found the following ARPUs:

ARPU

VENDOR A $544.63
VENDOR B $536.51
VENDOR C $213.65

Vendor A is delivering the highest ARPU, at 1.3% above Vendor B and 155% more than Vendor C. Clearly, then, Vendor A and Vendor B are attracting a higher quality user than Vendor C.

That’s important to know because even if Vendor A offers a bit lower cost per game install (CPI) than Vendors A or B, it may not make up for the difference in revenue per game user. If your cost per install for Vendor A were $5, then the CPI for Vendor C would have to be less than $1.95 for it to be as cost effective as Vendor A.

ARPU is a valuable directional measure to consider for gaming budget allocation. If we assume, for example, that Vendor C charges $4 per install, then putting more money into Vendor C is far less profitable than putting it into Vendors A or B. That’s because the ARPU from Vendor C is far lower. But without ARPU, you might rely on CPI to make your allocation decisions. Many companies do, and end up pouring more dollars into channels and vendors that are actually LESS EFFICIENT at driving revenue.

Obviously here, we are focusing on a component of ROI as a way of comparing relative ROI figures.

In the analysis above we focused on differences between vendors’ ARPU. But the same method of analysis can also be used to compare campaigns and creative executions.

Using ARPPU to Analyze Your Game Business

Using ARPPU is most useful for app businesses with revenue coming from a small fraction of total users. For example, a freemium game. ARPPU is a useful measure with which to assess your app monetization process and buyer flow. Because only a small fraction of users are payers, it will be far easier for you to see the effects of a new monetization process on existing buyers.

Here’s what we mean. A 10% improvement in average revenue per payer driven by a better monetization process on a business with 1,000,000 installs but only 30,000 payers would be easy to spot in a test. Half your buyers go through the test process, the other half the control, and we would see a 10% difference. But if we used ARPU, we would be dividing the revenue difference across 500,000 installs, so effects would seem negligible. See below:

USERS IN TEST PAYERS IN TEST REVENUE ARPPU ARPU

TEST CELL

500,000 15,000 $137,500 $9.17 $0.275

CONTROL CELL

500,000 15,000 $125,000 $8.33 $0.250

In this example, a 2.5 cent change in ARPU doesn’t look like much. 2.5 cents. But based upon ARPPU the difference is almost a dollar!

Net, ARPPU is useful in certain circumstances on businesses with far more users than payers.

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