Two months ago, we started interviewing User Acquisition (UA) Managers among our clients as well as top app developers, all with the hopes of getting a better understanding of the UA market. And let me tell you, there are a lot of mixed opinions about what’s going on and where the market is headed.
Here are some of the trendiest topics that were discussed: (i) having your DSP, or at least a Machine Learning (ML) Bidder; (ii) D7 ROAS as a central KPI to optimize UA campaigns; (iii) fraud; (iv) attribution; (v) and the overall role of Machine Learning.
While there was a lot of ground to cover and there are new challenges to face every day, there is one thing that remains true: the everlasting need to find new, innovative ways to attract users.
And while that has been the constant since UA came into existence, there are new influencing forces that come into play:
- A lot of developers have suffered by moving completely from ad networks (because of fraud) to Facebook and Google. Given their magnitude and reach, it’s natural for developers to feel like these two are all that’s necessary to scale UA campaigns successfully. Unfortunately, the ugly truth is that high CPIs can go up pretty quickly and it’s incredibly difficult for developers to stay profitable.
- The D7 ROAS continues to be the main metric to measure the efficiency of ad campaigns, but I’m sorry to say that it’s very far from giving you accurate predictive revenues. Which raises the question: what KPIs can you use to measure campaigns more efficiently?
- To our surprise, we’ve learned that fraud exists everywhere except Google and Facebook (of course, this is met with a lot of sarcastic jokes from our Data Science and Analytics team).
- Attribution providers have their hands full with cross-channel attribution, namely TV/OTT.
- This next one is very unfortunate, but it’s something I see regularly: Machine Learning continues to be a mystery to a lot of media buyers. Most don’t know how this technology fits with their strategy, even though it has been widely used by social platforms to fully automate UA activities for customers.
- Data restrictions, like the California Consumer Privacy Act (CCPA) or the General Data Protection Regulation (GDPR). How can you target and harness valuable users when it’s becoming more challenging to analyze personal data while staying compliant?
- Media buyers bet on creatives while trying to optimize ad campaigns since it’s the only thing left for them to do through Google and Facebook.
This heady mix has led to a lot of speculation in the market, but it has also fostered the creation of a lot of progressive solutions from data-driven marketers. Want to know what they are? Let’s dive in and talk about them.
DSPs Flywheel Effect
Let’s get to the nitty-gritty of the most infamous duopoly in marketing, Facebook and Google: they’re not as squeaky clean and fraud-free as they’ll have you believe.
In fact, they come with significant shortcomings that tip the scale and make marketers pay attention to smaller, non-trusted networks.
Here’s a breakdown of the main issues you’ll find in Facebook and Google: (i) very high bids; (ii) lack of transparency for optimization purposes (along with blind sources in the case of Google) that makes it hard to know where exactly your ad is placed; (iii) lack of control to customize your campaign and impact the optimization process since everything is already done for you. While this sounds like a good thing, the heart of marketing lies in tailoring to your customer’s unique needs, which is very hard to do with cookie-cutter campaigns. Just think of it this way: Are your competitors getting the same bids and ad placements? Kind of a real bummer, if you ask me.
It’s no wonder developers are beginning to give up on ad networks and lean towards DSPs as a new way to gain highly valuable users for less money. DSPs have evolved since their first appearance in the market; now, marketers have a greater chance to get users from DSPs than from other resources. Yeah, I’m talking about Real. Live. Users.
From the pool of 100 UA Managers we interviewed, 75 mentioned they want to scale programmatic channels, while 65 considered it’s time to develop the tech stack needed to buy more effectively and that includes having an ML Bidder or an in-house DSP.
Right now, the trendiest DSPs are self-served like Smadex or those who have open RTB (Real-Time Bidding) protocols, much like Verizon/Yahoo. In the case of Verizon/Yahoo, marketers can use it combined with their DSP or ML bidder. We know that MZ has gone through a trial-and-error phase about building its own DSP disruption platform. Being Prometheus means that you are the first but not the winner in the end.
Through great execution (that is real with your ML bidder as it’s, you guessed it, a personalized and reliable DMP!), and unparalleled access to cheap and large amounts of inventory, the DSPs flywheel effect can spin you into bigger volumes of traffic coming together with more transparency and lower bids.
Of course, there is always the challenge of fraud. Yes, it exists in DSPs as well — color me shocked. And it exists in places where it’s impossible to get refunds from some of the biggest players in space. But the truth about fraud is that if discovered and proved, you can reject it. Evidently, you need to add an advanced anti-fraud tool to your marketing tech stack to help you stay protected.
Another cause for concern is the auctions. To support big data flows and decide who the ad placement will go to, it’s necessary to employ true machine learning. Otherwise, it’s like trying to make a trip to the moon on a truck. Check your programmatic traffic providers how up to date they are.
Data management is also a big question. Several DSPs have their own built-in data management systems and compete with 3rd party solutions that are focused on a single task. There could be another shift in the market as MMPs try to revolutionize themselves into DMPs. Could that tactic work? Perhaps, but you need a really good one to analyze data deeply, predict further user paths, as well as revenue.
As the flywheel spins round and round, revenue increases without affecting inputs or costs. Why not try it out?
Ad Networks vs. Google vs. Facebook vs. ROAS D7
The battleground is set and everyone is ready for the punches. Or, so it seems.
Google and Facebook are sometimes the first and only options that marketers focus on to scale user acquisition campaigns. To prove our point, both tech giants are expected to collect an astounding 60.7% of digital ad spending in 2020.
While some marketers feel it’s an unnecessary risk to use non-trusted sources since they’re oftentimes associated with high levels of fraud, thus only buying traffic from Facebook and Google, there are still a lot of stats on the market which say that another approach could be an option.
On average our clients earn from 30–35% in revenue from conditionally-trusted ad platforms compared to the total revenue earned from paid campaigns.
Those are pretty substantial numbers if you ask me. While smaller networks don’t typically offer fraud-free guarantees, their prices and broader reach are more than enough incentive to give them a try and expand your marketing horizons. Take a look at the stats below to see the average CPIs that some of our clients have on different ad platforms.
Why is it so important? I pay more on Facebook and get more qualitative users. And the answer would be: yes, sometimes you do. However, you need to think about the bigger picture and calculate more precisely.
Nebojsa Radovic from Nordeus has also published an article where he describes how it is better to measure ROAS, and what could go wrong with its calculations, and why you should not rely only on Facebook and Google. And he is damn right. Just check, it’s worth reading.
You must consider all of this along with the fact that it’s very challenging for Facebook and Google to reach every nook and cranny that needs to be addressed, which is why it’s a smart move to use lesser-known networks.
Misha Syrotiuk, head of Ad Networks & Programmatic for UA at Huuuge Games had this to say on the subject:
“We have limitations on Google in those countries where we can purchase media. For example, in Russia, UAC is not available for social casino, and whereas Facebook is available but it’s rather limited in terms of inventory. So if we, as a social casino advertiser, want to purchase media in Russia, it’s good to go beyond Facebook where Google is not available. So it happened a few years ago and we do work with those programmatic media partners up until today, and they became, probably in between 20 to 40% share in terms of UA spend depending on OS or country in Huuuge Games’ portfolio.”
Bottom line, if you want to avoid losing potential markets, which are closed for Google and Facebook, we recommend that you explore more ad platforms.
For example, some of the most popular sources to reach your users in Russia would be MyTarget, UnityAds, IronSource, Liftoff, Bidease, and AdMixer, to name a few.
As the popularity of Google and Facebook continues to soar, so do data restrictions. While they have the infrastructure and are experts in processing massive volumes of data, some aspects weaken under this weight. Examples include customization, third party integrations, access to data, reporting, etc.
Google, rather stealthily, deployed a new restriction in January 2020 where iOS app installs driven by search traffic on Apple devices can’t be attributed to third-party networks. Advertisers will feel the blow of this punch since they won’t be able to promote an app through attribution providers and verify which traffic source generated profits.
So, what’s left for you to do? Creative optimization can work when it comes to addressing end-users. But it’s like going into a game of poker with a blindfold — not seeing a trace or a telltale of the actual game. In other words, you’re working without actionable data.
With all of this in mind, the optimum route is, if your resources allow it, to create a healthy traffic mix that’s composed of the Facebook-Google twosome coupled with smaller networks that open up your reach.
Wait a minute…what about fraud?
Ok, I can talk about mobile fraud all day long. But I am afraid I may sound biased or slightly paranoid since I’m too close to the subject, something we definitely don’t need in the time of coronavirus spreading across the world.
In a nutshell, fraud is increasingly becoming more sophisticated. Our Data Science and Analytics teams work around the clock to look for anomalies in data. The challenge lies in the fact that there are a lot of fraud schemes working behind the scenes on many different levels. Data is the key ingredient to uncover non-legitimate or fake installs. Ad networks, media buyers, and the majority of inventory out there are represented by legitimate companies in the market, but they are blind without data. And, a little word of advice, even with the right data, it’s very hard to eliminate fraud on your own or through traditional methods. But to get it right, you need advanced algorithms in your arsenal of tools.
So, my recommendations would be to do the following:
- Choose your anti-fraud solution. When you partner with an anti-fraud solution, you can rest assured and know there are dedicated resources to analyze your traffic and ultimately, detect and reject fraud to protect your app against it, especially when using non-trusted sources. If your anti-fraud solution uses Machine Learning to detect fraud, that’s the icing on the cake (and the whole cake, too!) as it is the only technology capable of detecting fraud at any level imaginable.
- Dedicate an in-house team to analyze and validate traffic that comes from non-trusted sources. That way, the team can keep a close eye on specific KPIs and other performance metrics that can alert to any wrongdoing or the presence of anomalies. At the same time, understanding whether this “fraudulent source” is really harmful or if the portion of clean traffic it brings could get you a true uplift in revenue.
Overall, you should not deprioritize fraud, but rather treat it with an assertive data-driven approach. Sometimes we see up to 5% of all traffic as being fraudulent one month, but the next, it spikes up to 35%. With everything being measurable, a deep look into your numbers will help you see that not everything is black and white in your data…you can find the whole rainbow there!
What about attribution?
This post wouldn’t be complete without giving a shoutout to attribution. You see, most of the developers we interviewed asked our team about effective cross-channel, human-based attribution, and whether it was attainable.
Being the fraud nerd that I am, it’s hard for me to give an assertive comment here. But surely, this is something that we definitely need along with standardized protocols of attributing installs for all sources. I’m sure that by having this in the mobile market, attribution could reach new levels.
UALand, the place where anything goes. So, where is the treasure hidden?
I believe that staying truly data-driven and keeping an eye on your metrics will help you choose the right strategy to promote your apps, including which channels you should buy from. From where I’m standing, DSPs are looking very attractive and are gaining traction in the market that is always looking for novelty ways to attract new users. And who knows? Maybe they’ll end up becoming a force of transformation in the UA industry. For sure, we’ll be right here keeping a close look at how the chess pieces move next.
Until next time, folks!