Meet Us At MAU Vegas

Well, it is finally the week of MAU and we are excited to meet you in Vegas! MetricWorks is proud to be a Gold Sponsor of MAU Vegas. There are many opportunities to meet our team and learn how you can close the gaps in your measurement stack with our MMM-based incrementality MMP, Polaris.

For a full listing of what MetricWorks is up to at MAU, visit MetricWorks At MAU22. Here are the quick highlights.

1) Don’t miss our MAU Speaking Session with our CEO, Brian Krebs on Wed, June 8th @ 11:40am in Terrace Ballroom 152. Brian will describe “Why It Is Time To Close The Gaps In Your Measurement Stack With MMM-Based Incrementality Measurement”

2) Drop by our Booth 644 to win fantastic daily prizes! Book a time to grab a drink with us.

3) Be among the first 50 to get an exclusive peek on our webinar at the very first Incrementality Industry Report. Sign up now for this exclusive webinar and the report that will follow.

4) Get our FREE GUIDE on How Marketing Mix Modeling Can Fill the Gaps of Attribution.

Hit us up with any questions or for a demo of Polaris at See you soon at MAU! 

Polaris Turns One!

Exactly one year ago, we publicly launched Polaris on VentureBeat (thanks again, Dean Takahashi). At the time, it was the first of its kind: a measurement product pairing media mix modeling (MMM) and geo lift experiments to provide incrementality performance metrics in the same form factor as an MMP. Actually, it remains the only product of its kind, one year later.

While we didn’t fully open up the floodgates until Q3 2021, the past year has been a wild ride. The response from the mobile industry was far greater than I could’ve imagined. Our partnership with Meta and collaboration with their world-class Marketing Science and MMM teams have helped us to continually improve Polaris and the science behind it.

Our growth over the last 2 quarters since we went fully live is nothing short of incredible. We’re adding new people to our amazing team and expanding to new regions across the globe. We’ve onboarded so many notable customers over these months it’s been an absolute blur. None of this would have been possible without my teammates at MetricWorks. I’m humbled to be a part of this journey with all of you.

To learn more about Polaris, submit a demo request or visit our Polaris product page.

How does incrementality enhance UA prediction and optimization?

When COVID-19 hit the world, digitalization skyrocketed. People had to find new ways to communicate, be entertained, and live their life from home. Brands also had to find new ways to survive, grow and interact with their customers. Just like that, the number of apps boomed and so did the competition. It won’t slow down any time soon. That’s why apps need to advertise successfully. Otherwise, it is difficult to differentiate from the competition and acquire new users.

So here’s the secret sauce for successful advertising: innovative UA driven by innovative measurement. Innovative UA provides the ability to make smart, and fast decisions today that outperform the competition tomorrow. This new type of decision-making is powered by prediction. Prediction itself is fueled by innovative measurement that is accurate, unbiased, granular and grounded in scientific methods that have been honed over many years. This type of measurement is known as incrementality.

What is incrementality?

You may have heard about incrementality and wondered what all the fuss is about.  Well, in the post-IDFA world, last touch is certainly a flawed and unreliable form of marketing measurement.  Fortunately, incrementality does not depend on IDFAs or GAIDs, or arbitrary attribution window. It is far more accurate than last touch or SKAN in powering your UA prediction and optimization.


Incrementality is the lift, in terms of any KPI (such as ROAS), over all other media spend plus organic demand. It’s really all that matters. If certain media spend is cannibalizing organic lift or overlapping with other media, true value can be significantly impacted.  

MetricWorks’ incrementality MMP, Polaris automates every step from designing experiments and calculating ground truth to training econometric models and deriving incrementality results. Incrementality is delivered in the form of the KPIs you rely on today including ROAS so that your UA or marketing decision-making processes don’t need to change. Polaris is also designed to offer an easy, turnkey “MMP experience” so that you can get started in 24 hours. No IDFAs or GAIDs. No migrations, no extra SDKs, and no heavy implementation lift. Polaris integrates directly with your last touch MMP to provide a single repository for side-by-side comparisons of your last touch and incrementality data.

Why are traditional MMPs and SKADNetwork not enough anymore?

The general issues with traditional MMPs and SKADNetwork is that they are built on the simplified yet flawed view of the world (see Fig. 2a below) and they ignore the fact that conversions are the product of a cohesive media mix (Fig. 2b below) rather than a single ad.

Last touch attribution ignores this complex reality, producing random results that are skewed toward self-attributing networks and dependent on arbitrary attribution windows. This has major impacts on business value. Due to this, UA optimization and prediction are also affected. 

In simpler terms, an inferior measurement signal results in inferior optimization and prediction. Tempr. is a fantastic prediction and automated optimization solution. However, to get the most out of your investment in Tempr., it is critical that you feed it a superior accurate signal tied to business value, namely incrementality. This ensures that Tempr. will make the best predictions and optimizations for you based on the best measurement inputs from MetricWorks Polaris.

Prediction: today’s decisions, with tomorrow’s data

The focus on the protection of personal information increases, but marketers still need to understand how to run successful ad campaigns. And we’ve great news: with prediction, marketers can get an accurate estimate of how their campaigns will perform, before wasting any budget or time. Even with limited data.

Where does prediction come from?

To be fair, prediction is no novelty for big app studios. Their teams usually design their own internal BIs. Most of them have data scientists working on predictions, because key decisions for the future should never be made blindfolded.

But here’s the thing. Any app studio, no matter the size, the language, the revenues, the structure, the MMP, etc., should have affordable and easy access to predictions. Not just the big guys. That’s why Tempr. – a predictive tool that adapts to each app vertical, and their KPIs – was created. Picture it: one big team helping UA Managers make today’s decisions, based on tomorrow’s data.

How does prediction work?

Prediction gives clarity on what’s going to happen next. The core principle is to use the past to predict the future

This marketing approach uses data science to forecast the strategies that are the most likely to succeed. In a simple way, here’s how it works:

1: Historical data mapping
The past is what we call historical data. On the graph below, each black dot represents the revenues made on a specific date.

2: Historical data understanding
The aim is to find trends, seasonality, and any other metrics that affected the past revenues – taking into account bids, budgets, traffic, app verticals, KPIs, etc.

3: Calculation
With machine learning, a mathematical formula that reflects the trends and seasonability of the graph in one curve is calculated. The objective is not to precisely follow each dot but come to a global result like the blue curve on the graph.

4: Data enrichment
The source’s data is enriched with data from MMPs such as MetricWorks for more accurate results.

5: Prediction
The prediction model powered by machine learning create thousands of scenarios with changing parameters and suggests the best bid-, budget-, and creative combination that will achieve the highest results (ROAS or CPE) in the future.

Fig. 3: How predictions work

Fig. 3: How predictions work

Benefits of prediction in UA
Now that the privacy era is on, Attribution and Measurement are more and more limited with SKAdNetwork. To make competitive decisions, mobile marketers need specific data. Not only yesterday or today’s data, but also tomorrow’s. 

UA Managers have to make assumptions on their users journey all the time. How active they will be, what type of events they like, and how much revenue they will generate. And since they don’t exactly know what to expect, they use A/B testing. Experimenting, testing, retesting, deleting. And wait to see what works, and what doesn’t. Which can be very costly.

However, when marketers associate predictions and A/B testing, they limit as much as possible the risks, the time-consuming guesswork and the loss of money along the road. Because prediction shows the successful path to follow, and adapts to the environment every day. And most importantly, Tempr.’s predictions give actionable insights.


More than just prediction: actionable insights

To predict the future is good. To recommend exactly what to do in order to achieve greater results? Even better.

Tempr. recommends campaign optimizations based on the predictions. The algorithms predict the combinations of bid, budget, and creative that will have the highest returns or that will decrease costs to achieve a certain event (first listening, download, etc.).

Marketers can finally drop the doubts and let predictions run the thousands of scenarios for them instead.

Fig. 4: Basics of Tempr.

Key takeaways

With prediction, apps increase their competitiveness, their efficiency, and ad campaign profitability. Mobile marketers can focus on what’s important: design better strategies, expand into new markets, and develop new features and apps. As predictive marketing fills in the information gaps left wide open by privacy rules, it’s now a vital practice to adopt.

How incrementality and prediction can work together

As we mentioned above, inferior measurement signal results in inferior optimization and prediction.  Incrementality’s superiority lies in its ability to model complex reality where media sources do interact, both beneficially and detrimentally, due to high audience overlap. Marketing can also both boost and cannibalize organic demand. Therefore feeding Tempr. with a superior incrementality measurement signal from MetricWorks ensures that your predictions and optimizations are far better than those derived from the SKAdNetwork signal.

In particular, as described in Fig. 5 above, with MetricWorks’ incrementality serving as the measurement signal, Tempr. will be able to more accurately identify situations where you might be underspending on a good media source or overspending on a bad media source.

Fig. 6: MetricWorks x Tempr.

The perfect partnership between Tempr. and MetricWorks

This recent partnership between Tempr. and MetricWorks offers mobile UA teams the best of both worlds with superior prediction and optimization by Tempr. being driven by superior incrementality by MetricWorks. “We’ve seen first hand the impact of poor mobile measurement. Therefore, our goal with MetricWorks Polaris is to provide true-north measurement that innovative UA-focused solutions like Tempr. can count on to deliver excellence to their clients in the post-IDFA era,” said Brian Krebs, CEO of MetricWorks.

To move towards a powerful predictive-data-driven decision model, it is crucial for marketers to have tools that provide an accurate understanding of how users interact with their apps. This partnership provides just that, giving marketers the ability to not only understand their app user behavior but to also take action on that information to improve their efficiency and maximize their ad revenues. Prediction-driven UA is undoubtedly linked with reliable data to start with. Therefore we are very happy about our partnership with MetricWorks that offers a very promising, and complementary technology to the well-known attribution platforms, helping mobile marketers to take the right decision,said Cloé Dana, CEO of Tempr.

Together, MetricWorks and Tempr. create the most efficient mobile UA solution on the market today. If you are looking for an edge over your competitors that will elevate your UA performance, contact us today to see how easy it can be:

Vincent Schmiedhausler
Sales Director at Tempr.

Chris Hoyt
Chief Growth Officer at MetricWorks

Video: How To Apply MMM To Mobile Apps

After our Jan 26th post, we continue to receive a number of questions about privacy violations in Europe and the impact on measurement. So here’s a short video by our CEO, Brian Krebs, that clearly describes Europe’s impending crackdown on measurement and what you can do. In particular, shifting to privacy preserving measurement is going to be critical. MetricWorks Polaris was designed to preserve privacy and deliver superior marketing measurement. Book a meeting to find out why.

In summary, for most app companies, the only real options to avoid similar massive fines are:

  1. Block access to European users completely (avoid jurisdiction of European regulators).
  2. Remove MMP SDKs from all apps and completely cease measurement activities.
  3. Continue using MMP SDKs, but ensure no device data is collected unless consent is granted (e.g., disable fingerprinting), meaning only deterministic last touch would be available and only for the few users that the MMP has double opt-in for (this may not even be possible for many MMPs at the moment and you’d still need a custom consent dialog for Android since there’s no ATT equivalent).
  4. Migrate completely to measurement methods that don’t require the collection of device data such as SKAN (iOS only), MMM, and geo lift testing (avoid collecting device data for the purpose of measurement altogether).

If you’d like to discuss this topic further, feel free to book a time or contact us.


Guest Blog: Thoughts on the shift in measurement from a practising UA professional (By Claire Rozain)

This past year was challenging on so many levels and filled with constant doubt that each of us felt at both a personal and professional level. Apple certainly didn’t make things easy on us. Indeed, this was the year that Apple decided to challenge all of us in the mobile advertising space and by leading a significant change in the attribution landscape with the release of SKAdNetwork attribution.

Still today, I do not have all the answers on how this change is going to be standardized in the future and the true impact it will have on developers from the smallest to the largest. However, to me it really is an eye opener and appears as a unique opportunity to track and attribute advertising data via incrementality to arrive at a holistic view. In this article, I share my own experience with iOS14 and how excited I am to shift from last touch to incrementality.

SKAN’s challenges including dealing with only 100 campaign IDs

When Apple announced the 100 campaign limitation, it made me anxious. It meant a limitation in deterministic data but also in the number of campaigns a marketer is able to run. So what was I going to do? No more experimentation? My first thought was “what is my job going to look like and how will I still enjoy doing user acquisition? 

To me, the restriction in the number of campaigns was really perceived long term as a threat to my UA manager position. Indeed, fewer campaigns meant less fun. It also meant fewer ways to experiment and challenge my thinking, fewer ways to find new pockets of growth, fewer ways to extend my media mix, fewer ways to easily optimise on the next crazy event designed by data science, and fewer ways to get direct feedback from the market without a timer. 

This left me with the strange and disconcerting feeling that the advertising platform did not need UA managers anymore in order to set up a AAA campaign without targeting or anything similar as everything was now heavily simplified and restricted.

SKAN does NOT offer a way to cohort KPIs by install date

However, what I got from this situation was that SKAN attribution was the new rule and I was interested in the new areas that Apple wanted to innovate in. I was really keen to have that information, but for a year, it was really difficult to get a clear and comprehensive explanation from Apple. Apple made several announcements during the WWDC and each time I remember that my UA manager friends and I  were really nervous about these because we did not know how things would pan out. After a year, we finally saw the first shape of SKAN and ran the first test on it. 

For now, the SKAN solution has  not met all of my expectations. Apple did a lot of back and forth that led in a year to the delivery of a disappointing SKAN attribution model, not at the level of the privacy promised but also disappointing from a market overview. A lot of simple things like offering daily KPIS by install date is currently really complicated with SKAN. All data is aggregated, the timer is super long and the sample size in order to receive installs postback data is still unclear to me. 

Funny enough, it made me happy that it was not perfect because it created opportunities for other smart companies to fill these gaps and they certainly did! I was happy that this complicated attribution was the real opportunity I was waiting for to push mobile companies to consider holistic marketing via incrementality and finally empower creativity in advertising!


Critical KPIs have been replaced by a single conversion value with its own significant limitation

Because advertisers were unhappy about the iOS14 change and all the limitations that it imposed by following only a single conversion value, I saw advertisers start to be resilient, to evolve and to innovate. People finally stepped outside of their comfort zones and challenged the status quo. The forgotten topics of creative and incrementality became the new topics everyone is now excited about to refashion their growth! 

I saw incredible growth masterminds and leaders that I deeply respect lead conferences about this topic, share resources to the entire market and open the doors to understanding incrementality. All of these people truly inspired me as a young UA manager. I could only imagine the struggle they endured everyday to explain to their high level management the change that they were implementing. At the same time, they also inspired other companies to help lead the change.

SKAN implications for budgeting, forecasting and strategy

SKAN budgeting is complicated at the company level and I think all leaders struggled and are maybe still struggling on how to figure it out. Investors are still unsure about the negative or positive externalities of spending heavily on iOS right now. Does it send a good signal to investors to spend heavily on iOS right now when we belong to SKAN? Or is it better to spend on Android? 

At the company level, Apple was NOT very vocal about the changes they were making and this created a lot of uncertainty. Almost every week, Apple was updating yet another release note with new information.  This made budgeting highly complicated for marketers as the day to day approach was to wait for an update prior to doing any forecast. Growth is not just a matter of days and months, it is a matter of years that involves you working with many people to be successful. Each decision can have a wide-ranging impact.

Last touch is the source of the above challenges

Last touch attribution on Apple was not an option anymore unless you had an ATT prompt where the user clicked YES. Since I worked in CRM before, I knew the optin rate would differ from one app to another and the deterministic data we get would certainly not represent 100% of the cohort. However, I was happy about it because last touch attribution has always been something I was really a fan of.

Indeed, to me it was always a way to favor the SANs, a way to say brandformance does not exist as it did when I worked in the past with an offline user acquisition channel. I also knew the last view or click was imperfect or non-ideal data as it also attributed to paid traffic what might have been organic, and was sometimes even fraudulent. So overall, I was really happy and excited about all the sparks that came out about this topic and the innovation in incrementality and holistic marketing views in organizations.

No need to lose hope as incrementality is real, usable and effective in overcoming the limitations of last touch

Don’t lose faith and adopt change! That’s really what all the leaders I watched on all the webinars made me think. They really made me happy to be part of this industry! As challenging as it was in the past to pitch for incrementality ; iOS14 was now an eye opener for many to lead efforts on sustainable growth and promote incrementality! iOS14 has shown us how painful it can be to get data and that deterministic numbers are the best answer to lead investment. 

Many companies now finally consider getting better and better knowledge about holistic views and are pursuing incrementality as a new way to increase and maximize their real profitability. I think this change was also driven by the fact that mobile generated more revenue during the pandemic. Advertisers discovered new channels already heavily used on the web (TV, influencer etc.) and expanded their media mix. Those channels also needed to be assessed in performance and iOS14 was only another reason to go deeper into measuring those incrementalities with a proper measurement approach.

Benefits of incrementality

For a long time, we called user acquisition growth hacking with the famous white and black hats. In the past, we had no rules as everything was new and the legal landscape did not exist. Now it does, and I am really happy about it. I am happy that it is not legal to send IDFA by CSV file anymore. I am also happy that companies have to ask me for my consent. 

As both a user and an advertiser, incrementality is the best solution to respect my privacy and also provide the necessary and accurate information that advertisers need to ensure sustainable growth. Privacy-compliance is at the heart of incrementality as it requires no device IDs. It is simply a clean scientific means of measurement.

Incrementality does not have artificially created attribution windows and prevents the over-attribution of SANs like Facebook and Google. This makes me really proud of our industry – we now can provide all channels the same chance of success. Once again, this is going to challenge how we think and what we think we know. Incrementality measurement is the only approach that now is able to show you the real impact of each channel based on fair and accurate data. I really hope this will reshape our industry and challenge the famous duopoly.

The other thing that I’m really excited about is that cannibalization will now be taken into consideration. For a long time, marketers ignored cannibalization in order not to suffer a decreasing budget. I hope that cannibalization of paid and organic is going to be reduced and that incrementality’s holistic view will provide a better understanding of the true paid impact on organic. Indeed, it can sometimes have a negative impact but there can also be a positive impact on organic uplift for instance, provided by an app burst that increases your ranking. What about your TV campaigns? Now everything will finally be measurable to understand the true effectiveness of all of your marketing efforts.

Working in mobile means that learning is a day to day activity. As challenging as that is, everyday is about making our space a more advanced space. I am really proud to be part of the mobile space. Incrementality is one of the best results of iOS 14 and without it we would perhaps never have this opportunity to make measurement better for the advertiser and more importantly for the user. We can finally heave a sigh of relief because we survived! 

I look forward to your questions. Thanks.

Claire Rozain is passionate about growth strategy. She is currently a Senior UA Manager in casual games for Gameloft. Prior to that, she was in social casino for Product Madness, in dating for Match EMEA, and in mobility for Mappy. She had opportunities to explore different verticals in the UA and CRM world and is passionate about top of the funnel paid and organic strategies to get the right users. You can follow her YouTube channel here. 

Guest Blog: UA Adaptation in the post-IDFA world (By Hagop Hagopian)

It’s been almost two months since Apple enforced the IDFA depreciation and the ATT framework through iOS 14.5. Advertisers are freaking out about the loss of attribution data for their paid media investments. Basically, all iOS numbers are looking bad and every paid acquisition channel is reporting less install and post install numbers. In some cases there are more than 50% loss in post install reports. The new reality with all of its attribution data loss does not look surprising to us. Moreover, we have already found a solution that we’re pleased to share with you.

How to deal with attribution data loss and measure success?  

What’s interesting about the data that we are seeing is that most of the numbers are being attributed to organic. By looking at Organics and Paid ratio pre and post iOS14.5, we can clearly see that the paid numbers are constantly declining and organic numbers are rising. So, if you are an app owner, you shouldn’t freak out about it as you are still hitting the numbers the same way you used to. However, your marketing team that manages paid channels might be concerned because of not knowing which channels or campaigns are driving the results. 

What can you do to measure your success?

There are a couple of ways marketers are considering to identify success:

  1. Rely on historical data. For example, you know that Facebook or certain geos were constantly hitting your numbers. Therefore, unless you’ve made major changes in your app that caused a complete change of economy, it is fair to make the assumption that they continue to do so.
  2. Use incrementality to measure your paid media success. We know that we haven’t completely lost the data from iOS14.5+ because there’s still a rise in organic numbers. It is important to analyze which part of the UA activity is causing the rise, and this is where incrementality comes into play. Through incrementality measurements you will be able to tell what percentage of the increase of your organic numbers are driven by specific UA campaigns that you’ve launched.


Measuring KPIs (Key Performance Indicators)

It’s appropriate to aggregate both app events and marketing data at channel and country level and then measure the KPIs that each channel is driving. This way you don’t have to rely on last touch attribution or IDFA to measure the KPIs. All you have to do is relate the paid activities to organic numbers. Once you have the KPIs you will be able to tell which campaign, channel or geo is working better.

How to start today?

It would require a heavy investment in the data science team to analyze these numbers at a very granular level.  Therefore, be ready to invest a lot of time and resources to get there. However, there are some companies who are already measuring incrementality for post-IDFA deprecation and are able to split results within 24 hours. One of those companies is MetricWorks. We at Appvertiser have collaborated with them to help measure the  iOS14.5 numbers for one of our clients. With help from MetricWorks, we have been able to identify the underperforming and overperforming campaigns without relying on SKAD reports. Moreover, the client was able to see the results in a short period of time. Through MetricWorks, our client was able to continue to drive their UA investments without facing any issues and measure the success of their campaigns through the incrementality approach embedded in Metric Works Polaris. It is clear to us that this new privacy wave is here to stay and we expect Google to follow a similar path with Android phones. We at Appvertiser do believe that this is the future when it comes to UA measurement. Last touch attribution will be forever gone. For any marketing team out there, we highly recommend that you invest in your data science team and give them the needed support whether it means building it in-house or contracting top product vendors such as MetricWorks.

Appvertiser is a growth marketing agency for app developers and ecommerce companies. We have over ten years of experience in growth marketing and user acquisition, specializing in paid channels management, setting marketing strategies, and taking a data-driven approach to drive and unlock growth.

You can reach us at grow@appvertiser.ioWe look forward to your questions.


Hagop Hagopian
CEO & Founder of Appvertiser