Player profiles and motivation. Why do people play mobile games? What motivates them to choose one app over another, or to click on one ad over another? What motivates them to continue playing a game rather than just playing it once?
The answer lies in a concept called “horizontal segmentation.” It’s an idea that remade the food industry a few decades ago and has remade several other industries since then. We believe it’s about to remake user acquisition and Facebook ads, too.
Pasta sauce, pickles, and Howard Moskowitz
The first champion of horizontal segmentation in consumer marketing was a guy named Howard Moskowitz. Moskowitz, a food researcher, and psychophysicist had been asked to find the perfect sweetness level for a new type of Pepsi. But after he dug into the problem, Moskowitz discovered there was no perfect level of sweetness for the new type of Pepsi. There were only perfect levels of sweetness.
Or, as Malcolm Gladwell explains in his TEDTalk on the subject, there was no perfect Pepsi – there were only perfect Pepsis.
Pepsi, unfortunately, wasn’t sold on this concept. Moskowitz had to keep pitching it far and wide, with tepid reception, until a pickle company gave his idea a spin. And Howard, true to form, found them not the perfect pickle, but their perfect pickles – zesty, classic, etc.
That was a good start, but the revolution of horizontal segmentation didn’t take off until Prego, a pasta sauce company, hired Moskowitz. Moskowitz, once again, went out and did a ton of research about how people felt about pasta sauce. He returned with not the perfect pasta sauce, but with the perfect pasta sauces.
Most famously, Howard returned with a recommendation to make chunky pasta sauce. No other company had been selling chunky sauce, and pasta sauce makers weren’t aware that people even wanted chunky sauce. But because Prego trusted Howard and the research that pointed to this unmet need, Prego launched a line of chunky pasta sauce. And went on to sell $600 million of it over the next few years.
Horizontal Segmentation for Facebook User Acquisition
So, what does all this sauce talk have to do with user acquisition? Everything, actually. Because in essence, Moscowitz had unearthed the following ideas:
1. People are not all the same.
2. While people are not all the same, if you study them in large groups you will find they tend to cluster around a certain set of profiles or preferences. Personas, if you will.
3. With enough data and data analysis, these preference clusters can be found.
4. If you develop products expressly tailored for each of these preference clusters, you can sell more stuff. A lot more stuff.
TEDTalk
Gladwell explains in his TEDTalk that, for example, if his audience was to get together and make one perfect brew of coffee together, they would individually give that brew a collective average score of about 60 on a scale of zero to 100.
But if the group was allowed to break up into coffee clusters based on certain dominant preferences and was able to make coffee expressly designed for each of those preferences, the satisfaction score for those preference-driven brews would rise to about 75 to 78.
As Gladwell explains, “The difference between coffee at 60 and coffee at 78 is the difference between coffee that makes you wince and coffee that makes you deliriously happy.” (Gladwell 17:16).
So, to paraphrase what Moskowitz and Gladwell have explained and to bring it into the context of Facebook and Google user acquisition:
There is no perfect game. There is no perfect way to advertise or marketing for that game, either.
But there are perfect games. There are perfect ads.
Finding the persona clusters or player profiles in gaming for these games and ads (and more specifically, finding the motivations of these persona clusters) will let us create ads that make people dramatically happier and take the action we want them to take (click, download, purchase, or view ads).
Revolution or Evolution?
This is clearly an evolution – if not a revolution – in how most UA and design teams have been doing creative for user acquisition.
We’ve all been doing reasonably well by focusing oncreative testing, but we’ve been largely focused on the creative elements of those ads that have the largest appeal (though good competitive analysis does take emotional hooks and the tone of ads into account). But most UA and design teams have been approaching their advertising and creative strategy with the assumption that there was a perfect game, a perfect app, a perfect ad. “A platonic ideal,” as scholars say.
What if that’s not so? If there are only perfect apps, perfect games, perfect ads.
What if we could talk to people based on what motivates them to play the game?
Maybe, we could take a video ad – the exact same video – and then adjust the copy or the call to action based on what motivates different users to want to play. By tailoring messages according to what those player profiles will most respond to, could we increase ROAS by… 20%? 30%? Or could we expand the game into a whole new audience segment?
Game Theory and User Behavior Models
If you know your way around game theory, you’ll know we already have quite a few detailed studies about different gaming profiles and user personas.
So, we actually have quite a lot of information about what motivates people to play games.
You can look to “Fogg’s Behavior Model,” or Scott Rigby’s Player Experience of Need Satisfaction (PENS) model. Or you can view your users through something simpler, like “The Big Five,” or the “Five-Factor Model.” Paula Neves breaks that model out well in her articleLooking at Player Motivation Models:
Openness to Experience: If one is inventive and curious or consistent and cautious.
Conscientiousness: If one is efficient and organized or easy-going and careless.
Extraversion: If one is outgoing and energetic or timid and reserved.
Agreeableness: If one is friendly and compassionate or challenging and detached.
Neuroticism: If one is sensitive and nervous or secure and confident.
The five traits, under the acronym OCEAN, are therefore treated on a spectrum where you can be open or closed to experience, conscientious or unconscientious and so on.”
Bartle’s Player Types, in particular, might be the model that the gaming industry embraces the most.
Bartle’s Player Types
Image Credit: repignite.com 2014
In fact, given the names of the player types and their descriptions in Bartle’s model, it almost seems like some of these player types have already filtered down into ad creative.
Ad Creative
Image Credit: ConsumerAcquisition.com 2020
Or maybe behavior models aren’t quite your thing. Maybe you want to view things through the lens of which types of emotional hooks appeal to different player types.
Emotional Hooks
Image Credit: ConsumerAcquisition.com 2020
As a UA manager or designer (or even a game designer, but that’s another article), you can start seeing your user base through any of these different models. And you can start customizing your advertising accordingly.
Examples of Player Profiles
Cutting-edge UA and design teams are already exploring these different models and applying them to their user base. When we polled our clients, we learned that about 10% of them have these sort of persona profiles in place. Typically, these models are being generated in marketing departments through the use of user surveys.
So, here’s what an actual player profile might look like. This is actually fake data, but it gives you an idea of what information gets included and how detailed these profiles tend to be.
Image Credit: ConsumerAcquisition.com 2020
Note how the graphic shows what this particular player profile most cares about in the game. The Demographic info is included, but what will really shape our creative is in the left column. Demographics might help a little, but user motivations are the driver.
Competitive Analysis
So, we can take the motivations from these different player clusters and look at what’s unique about them. We can also see which other titles they’re playing. This helps us come up with new creative ideas and a new list of competitors, which in turn lets us dobetter competitive analyses.
Armed with this information, almost any designer would immediately understand that they need to create different ads for these different player segments. These are different audiences. Even if the ads are ultimately meant to sell the same game to all the different profiles, ads tailored to each profile are going to perform dramatically better than one ad designed to try to please them all.
Even being able to swap out different bullet points for each player persona could result in tremendous improvements to ROAS.
Media Buying for Player Profiles
There are alsomedia buying implications to a player profile methodology. Each of the player profiles above, for example, tends to favor somewhat different websites, apps, and even YouTube channels. So if a user acquisition manager wanted to squeeze every possible drop of ROAS from an audience, they would segment that audience based on different player profiles.
If you’re acquainted with Facebook’s newStructure for Scale recommendations, you may have frowned a bit at that last suggestion. Per the Structure for Scale recommendations, Facebook would rather we not get too granular with audience targeting. Because the smaller a dataset we’re using for our ads, the less effective the algorithm will be.
But that’s where the art of UA management intersects with the science of the ad tools we have now. It’s up to each UA manager to find the sweet spot between audience targeting and audience automation.
It will take some experimentation to figure out whether it’s appropriate to segment audiences for player profiles. Or, if it’s better to just make different ads for the different personas and to let the algorithm figure out optimal performance.
People Can’t Always Tell You What They Really Want
Before you try to apply all this by launching a user survey or research study, know this: People may not be able to tell you what they really want. Because they don’t know.
Sometimes, you don’t know what you really want until you happen to stumble upon it.
For example, Moskowitz, the pasta sauce researcher, discovered that many people wanted chunky pasta sauce, not the perfectly smooth pasta sauce that was only available at the time. Only with some unusually clever research was Moskowitz able to unearth this desire.
Gladwell describes this elegantly by talking about coffee. As he explains, if you ask the typical American what type of coffee they like, they’ll say “I want a dark, rich, hearty roast.” (10:40). But in actuality, most of us don’t really want a dark, rich, hearty roast. Only about 25-27% of us actually like a dark, rich, hearty roast. Most of us like milky, weak coffee. But we’ll never, ever say that.
Product Research
All this, of course, turns standard product research on its head. It means all those surveys, focus groups and other tactics we’ve been using to figure out what people want from our games are not the full stories.
But this is exactly the principle that put Apple on the map. Steve Jobs didn’t go find out what people wanted from a better cassette player. He built them something beyond their imagination: The iPod.
Henry Ford is often quoted with a similar sentiment: “If I had asked them what they wanted, they would have said faster horses.”
So what we may be aiming for here is a quantum leap, not an iteration. Whoever can figure it out, systematize it, and apply it to game design and creative strategy could well become a billionaire. Or another Howard Moskowitz.
Player Profiles as UA Creative Strategy 2.0
For the past two years, we’ve watched Facebook and Google move towards fully automating UA advertising. And because the algorithms of those two ad platforms have been getting better and easier to use all the time, much of the qualitative side of UA management is nowbest done by machines.
Creative, however, is stillbest managed by humans. This includes all aspects of creative – creative development, creative strategy, and creative testing. Creative is our best competitive advantage. Especially, now that the ad platforms have removed the competitive advantage third-party adtech used to deliver.
But now, with player profile theory, there’s a whole new dimension to creative. It takes everything to another level.
Once we can see what’s motivating people to play games, we’ve discovered a whole new level of creative theory. It makes getting overly focused on button colors look downright shallow.
All the creative elements we’ve been focused on up until now (colors, sounds, even ad copy) need to be realigned. This will serve these customer motivations.
With player profiles theory and player motivations, we have crossed into Creative Strategy 2.0.
Check out the 2020 Definitive Guide Of Facebook Ads Creative Strategy, Creative Testing and Launching New Games, published by Consumer Acquisition February 11, 2020.
Find the best practices for launching a game or app in 2020 using Facebook and Google AC social ads. Also, receive tips and tricks to successfully prepare for worldwide launch through scaling and optimization tests and understand how automation and machine learning have reshaped UA.
Learn what is driving “Creative Strategy 2.0” and why creative is the king of performance and how to beat your control video! In addition, discover the best media buying practices for identifying the most efficient campaign set up for a soft launch and worldwide scale, how to increase audience size, and combine placements to maximize ROAS.
We offer you real-world, actionable insights to your launch and profitably scale your new game or app using Facebook ads.
Facebook Ads Creative Strategy, Testing & Launch
Section 1: User Acquisition Is Dead!
How We Got Here
What Facebook’s New Structure for Scale Best Practices Mean for Campaign Management
As User Acquisition Becomes Automated, What Are The Roles For Machines, and for Humans?
Third-party Adtech Tools Are Obsolete
Section 2: Creative Strategy Best Practices
Most Ads Fail
Creative Audit
Competitive Audit
Competitive Trends
Asset Folders For Winning Ads
Player Profiles
Creative Strategy
Mini Creative Briefs
Winner Variation Testing
Section 3: Hidden Challenges In Creative Testing
Creative Testing: Statistical Significance vs Cost-Effective
IPM Testing is Cost-Effective
Why Is The Control So Hard To Beat?
Creative Testing 2.0 Recommendations
Section 4: Video Ad Creative Strategy and Best Practices
Ad Copy
Buttons
Start and End Cards
Text Placement, Fonts, Colors, and Emojis
Aspect Ratios
Section 5: How To Launch New Games in 2020 With Facebook Ads
Phase One: Early Creative Testing in the Soft Launch
Structure For Scale
Complement the Algorithm
Results During the Learning Phases
#1 Lever Increase Audience Size
#2 Lever: Combine Placements: Select automatic placements for better results.
Need to feed the creative beast? Most advertisers do. Creative fatigue sets in fast… and the larger your ad spend, the faster it sets in. But creative fatigue actually isn’t your biggest problem. Your biggest problem is maintaining ROAS (return on ad spend). It’s in developingmobile ad creative good enough to replace your top-performing ads.
This is a bigger problem than most people think, because most creative fails. Most new mobile ad creative doesn’t perform anywhere near as well as a campaign’s top-performing ad, aka “the control.” After managing over $3 billion in ad spend, we’ve found it usually takes twenty new pieces of creative to find a replacement for a campaign’s previous control. Only about 5% of new creative performs well enough to make the cut.
This has two major consequences:
1. You don’t need just one new ad every week or so to stay ahead of creative fatigue. You need twenty new ads. And (as mentioned before), the more you spend, the faster your ad creative will wear out. The table below shows roughly how many ads you’ll need every month depending on your ad spend.
2. If you don’t want to see a dip in ROAS, and you don’t want to waste a lot of time and blow a lot of ad budget, you’ll need a testing system that can rotate through those twenty new ads and find the new winner fast.
Let’s tackle that first problem first.
How to Produce Enough Mobile Ad Creative to Stay Ahead of Creative Fatigue
Most in-house creative teams struggle to stay ahead of “creative fatigue,” aka the dip in ad performance that happens when audiences have seen an ad so often they start to screen it out.
Ironically, the more successful creative teams are, the harder they have to work. Because if their ad creative does well, ad spend usually increases. And as ad spend increases, their new ads burn out faster. They have to work harder to maintain the same results.
Even if a creative team can keep pace with the demands of scaling up, they’ll often run into another challenge: Getting out of their creative “comfort zone.” This is the inherent tendency of sticking to what works. It isn’t a bad thing – focusing on what works drives performance – but creative teams often get stuck in ruts of just sticking to what’s worked in the past. Eventually, the creative gets a little stale, performance dips, and you have to think beyond what’s worked in the past.
Three Ways to Get Out of a Mobile Ad Creative “Comfort Zone”
1. Competitive Analysis
One proven way to stretch beyond the curse of “what’s worked before” is to do regularcompetitive analyses. TheFacebook Ad Library is a great free tool for this, but other paid tools can give you insights into how ads are performing.
Doing regular, documented competitive analyses can help creative teamscome up with more ideas. But it also helps them to be more data-driven. That’s critical for success right now.
2. Agency Bake-Off
Another way for creative teams to stretch is to participate in an “agency bake-off.” This is where the in-house creative team competes against an external creative team. The bake-off has rules, and information on ad performance is shared between both teams every week. We do agency bake-offs in 30-day sprints, with set rules designed to maximize learning and performance and minimize any downsides of competition. To date, we haven’t lost one. But be aware that 30-day tests often fail.
3. Outsourcing
Then there’s the most common way to expand a creative team’s capacity: to outsource. OurCreative Studio does that for hundreds of advertisers. It gives them access to a world-class team with Disney-level storytelling skills and data-driven user acquisition expertise. We’ve built a streamlined system that makes it easy to request and approve any amount of mobile creative you need.
So if you’re ready to move beyond a one-off style of mobile creative development, it can be done. Using an outside team like Creative Studio can be particularly helpful if you want to scale quickly, or if you want to be able to expand or contract your creative development without having to hire (or fire) an internal team.
But just having “enough” mobile creative is only the beginning. Next, you have to test it. Finding that new magic ad as efficiently as possible is an advertiser’s best competitive advantage, especially now that Google and Facebook have given every advertiser access to machine learning-driven tools thatmake many third-party ad tech tools obsolete.
How to Use Quantitative Creative Testing to Optimize Mobile Ad Creative Fast
Quantitative Creative Testing is an A/B split-testing methodology we’ve developed specifically for mobile creative. It’s designed to be super-efficient with both time and ad spend, to find the sort of breakout ads that can replace a campaign’s previous control.
Quantitative Creative Testing separates new mobile ad creative into two buckets: Concepts and Variations. Concepts are totally new, out of the box creative approaches. They tend to fail a lot, but when they succeed, they often blow the doors off everything else. About 20% of the mobile ad creative testing we do is with Concepts.
Variations are just what they sound like. They’re small tweaks we make to existing creative to see if we can get it to perform better. Testing Variations is much “safer,” in that Variations don’t tend to tank as hard as Concepts do, thus they don’t risk wasting as much ad spend. Testing Variations also lets us find out whichelements of an ad are driving its performance. This is precious information for optimizing the ad and for refreshing it later on. Being able to refresh creative lets us extend its life and thus radically improves the ROI of creative assets.Strategically expanding the audiences we advertise to helps a lot as well.
To do Quantitative Creative Testing for mobile ad creative, we make batches of new Concepts and run them against each other in an ad set. Each new Concept gets about 50,000 impressions before we decide if it’s a winner or a loser. If it’s a winner, it gets moved up into another ad set where it will run against other winning mobile ad creatives, including the current control. If the new ad can outperform all the other ads in that ad set, then it gets moved into another, primary ad set and gets the bulk of the campaign’s spend.
There are two shortcuts we take when we’re running new ads through their first 50,000 impressions, and two very good reasons we take them.
1. We don’t insist on perfectly brand-compliant Concepts.
Don’t get us wrong: Brand compliance is important. It supports long-term customer loyalty, coherent messaging, and many other good things. But sometimes, when we’re testing every attribute of an ad, we run into a situation where the brand guidelines are suppressing results. Or, we simply want to test something that could work… but we can’t because it would break the brand guidelines.
To get around these limitations, we’ve developed two levels of brand compliance: “Flexible Brand Guidelines” and “Strict Brand Guidelines.” Here are their key differences:
Flexible Brand Guidelines
Make entertaining ads
1.5-2.0 seconds per scene
Embrace rapid prototyping of ads
Go beyond play and brand assets
Obsessively study competitors
20-second maximum length
Strict brand guidelines
Detailed strategies assist with buy-in
Large brand assets and logos
Understand reasons to believe
Segment ideas for user clusters
Show up-close action
End with real gameplay
We aim for Concepts that are 80% brand complaint, but we don’t insist on the ads being any closer than that. Why? Because we’re beingdata-driven rather than brand-driven. We want to know what works, even if it doesn’t necessarily achieve brand compliance. Also, because very few people have seen the ad in our tests, we’re minimizing exposure to what is ultimately a fairly small brand deviation.
2. We don’t insist on high statistical confidence.
If you didn’t take statistics in school or you don’t do a lot of A/B testing in your job, “statistical confidence” may be a new concept. So here’s the thumbnail explanation: To be sure the results of any split-test are valid, you need a large enough body of data (or a large enough number of actions) to know the results you’ve gotten aren’t just random chance.
In traditional testing methodology, split-tests require a 95% or even 99% “confidence level” to be considered statistically valid results. Trouble is, to get that level of confidence you need to run ads for a fairly long time. We don’t have that much time, and we don’t want to spend any more on losing ads than we have to. Running losing ads kills ROAS.
But here’s the secret: Most A/B tests have to run a long time because the ads being tested perform pretty much the same. If the ads were to perform very differently, then the test doesn’t have to run as long. In other words, if one ad just crushes the other performance-wise, then we don’t have to run the test for very long at all.
Here’s an example of this in action: If Ad A is performing 300% better than Ad B, you don’t need to run the two ads very long at all. But if Ad A is only performing 5% better than Ad B, you’d have to run the two ads for quite a long time to know for sure if Ad A was really better.
Remember: For our mobile creative testing we aren’t looking for 2% or 5% improvements. We’re looking for breakout ads. We’re looking for 100x performance. So we will just toss out ads that don’t perform significantly better than other ads. We don’t care about 2% or 5% improvements. We’re looking for 20%, 40% improvements. We’re looking for earthquakes, not tremors.
Those two shortcuts let us run new ads through our testing machine MUCH faster than if we had to obey the traditional laws of brand compliance and statistical relevance. Compressing that testing window saves an enormous amount of ad spend and lets us generate new high-performance mobile ad creative fast enough to stay ahead of creative fatigue.
Why Quantitative Creating Testing is a Competition-Killing Competitive Advantage
And the speed is just the first benefit. Being able to do these tests quickly also means they cost far less than traditional testing would require. We’ve saved a lot of ad spend with the abbreviated testing cycle, and we’ve optimized the budget we did use by not spending money on underperforming ads.
This has one other powerful consequence: Because our ads are so much more efficient than our competitors, we can outbid them. And even while we’re outbidding them, we’re still getting dramatically higher ROAS than they are.
This dynamic can be so extreme that even a small (but way more efficient) advertiser can sometimes take out a larger, better-funded competitor. If you’re getting 200% ROAS and your primary competitor is only getting 20% ROAS, even if they’re a Fortune 500 with a pile of money to burn, you can still outbid them and take the best ad placements.
Being able to outbid competitors is especially critical for mobile advertisers, too. Because of the nature of mobile advertising, there isn’t a lot of space on the screen. It’s a winner take all situation where the best ad can hog all the inventory, leaving scraps for everyone else.
In principle, any advertiser who can own the top ad placement (often the only ad placement in an app or mobile interface) can basically own that entire ad platform for certain audiences. And that’s why strategic, ongoing A/B testing of mobile creative is the ultimate competitive advantage.
It is time to rethink how we manage Facebook advertising campaigns, and Facebook has a new framework to do it with: Structure for Scale. Learn how to prepare for automated media buying within this new framework.
Facebook’s Structure for Scale: How to Prepare for Automated Media Buying
Structure for Scale is designed to optimize campaign setup and management in a post-AI advertising world. Basically, it’s a set of best practices advertisers should adopt if they want the Facebook advertising algorithm to operate at peak performance.
Reducing the number of campaigns and ad sets to prioritize quality over quantity
Setting campaign optimization budgets (CBO) so each ad set can achieve 50 unique conversions per week
Moving optimization events closer to the beginning of the sales funnel
Reducing changes so campaigns and ad sets spend less time in the learning phase and move to the optimized mode
Increasing reach and minimizing audience overlap so the algorithm can more efficiently find conversions, and thus has enough data to further calibrate and optimize all other campaign settings
Bidding aggressively enough to maintain delivery
Using creative asset customization and auto placements to let the algorithm figure out what works best
If you pay close attention to Facebook advertising and the announcements and best practices they publish, you’ll remember the Power5 recommendations they announced this summer. Structure for Scale has a lot of similarities to the Power5 best practices, but it is a further evolution or iteration of how to run Facebook ads right now.
It also appears to work. That might seem like an odd thing to say, but we sometimes hear advertisers express suspicion about the best practices Facebook recommends. There is a sentiment of “well, using those tactics may be best for Facebook, but it’s not necessarily best for me.”
Facebook is building up a library of case studies to dispute the naysayers. You can review several dozen different examples of how effective the Structure for Scale best practices are.
Here are just a few examples:
The National Holistic Instituteapplied Structure for Scale by consolidating their account’s ad sets and turning on campaign budget optimization and automatic placements. It got them a 77% decrease in cost per lead, 4.9 times more leads, and a 230% increase in school enrollment.
Bombas simplified its account structure and got a 2x increase in purchases.
Mino Gamessimplified its ad campaign structure and ran Facebook video ads with automatic placements. This increased installs and in-app actions, earning them a 90–95% higher average revenue per player on day 7.
Jet.com expanded its placements to Facebook, Instagram, and the Audience Network. That got them a 334% lower cost per purchase and an 86% lower cost for traffic on the Audience Network.
The Key Principles Behind Structure for Scale
So the benefits of Structure for Scale are clear. Here’s how to apply them to your own account or to your clients’ accounts.
Simplify Your Campaign Structure
This is a recommended best practice you may have heard of before, but it’s at the crux of this new approach to Facebook ads.
Basically, Facebook wants you to KonMari your campaigns and ad sets. To go from this:
To this:
Why? Because it gives the algorithm more room to learn and efficiently optimize.
To work well, the Facebook advertising algorithm needs the correct amount of data to crunch. This is core to how machine learning works, and it’s a concept you must grok if you want to advertise successfully now and in the future.
Before we had an algorithm to manage campaigns, it made sense for human ad managers to create many (sometimes hundreds) of campaigns to control all sorts of variables – bids, targeting, audiences, placements. This lets us test settings, isolate audiences, and manage many other levers of campaign management.
But now that the algorithm manages so many of those things, creating lots of campaigns just gives the algorithm less data to work with. And with less data, it doesn’t work as well.
To understand how the algorithm works, it helps to know which data signals it’s working off of. These are just a few of the ones used for the type of goals an eCommerce site might use:
Ad engagement or video viewed
Add to cart actions
First day on brand site before purchase
Purchase device
Number of pages viewed each day on brand site
Number of days on brand site before purchase
That’s some very detailed information. When you create a lot of campaigns, you’re basically putting a blindfold on how much data the algorithm can use from campaign to campaign to optimize your ads. As a result, campaign performance gets crimped.
Facebook is calling for advertisers to increase “liquidity (flexibility) by removing constraints” on their campaigns. One of the constraints to be removed is to open up your account structure by minimizing how many campaigns and ad sets you have. Another way to improve “flexibility” is to use Campaign Budget Optimization to free up how your ad budget is spent. And yet another way to give the algorithm the flexibility it needs is to allow it to pick ad placements so it can test your ads across the 14 different placements available.
This means, of course, you will be giving up lots of control. Some advertisers do not like that. But here’s how Facebook views this “control” versus “algorithmic optimization” quandary:
Which would you want? More conversions, or more control? Personally, if I could be assured the value of those conversions is as good as what I was getting before, I’d go with more conversions.
But what happens if you can’t cede control? Your ads will stay in what’s called “the Learning Phase” or “Ad Set Calibration” longer because the algorithm can’t crunch data in the way it was built to do.
Staying in the Learning Phase for any longer than necessary is not a good thing. It suppresses return on ad spend by anywhere from 20-40%. To shift out of it, each ad set needs to generate approximately 50 unique conversions per week and to stay out of it, advertisers need to not make any “significant edits.”
So as we walk through each aspect of Structure for Scale, understand that the overarching principle here is to remove limitations on the algorithm in order to gain conversions. This means setting campaigns up in a way that allows the algorithm to do its work, and to minimize tinkering with the system once it’s running, lest you trigger any “Substantial Edits” that force the algorithm to recalibrate again.
Ultimately, Facebook just wants you to adjust a few key levers so the algorithm can do its job.
Automated Media Buying
Lever #1: Increase Audience Size
Facebook recommends four ways to do this:
Increase retargeting windows. Many advertisers have tight windows, like one to seven days. That only works if you have substantial website traffic. So, to expand your audience and give the algorithm more room to work, try increasing your retargeting window and see if you don’t get better results.
Merge your Lookalike audiences into larger groups. Facebook recommends 0-1%, 1-2%, 3-5%, and 5-10%.
Group interest and behavior targets with high overlap together. Just make sure the creative strategy is the same for the groups you’re merging.
Minimize the audience overlap. Get smart about audience exclusions, including screening out past purchasers.
Lever #2: Combine Placements
Facebook has 14 ad placements across its family of apps right now. Trying to manage that is complex, to say the least. It’s really better to let the algorithm manage placements, especially when you add in other performance factors like ad sizes. So select Automatic Placements and turn your focus to other things, like doing competitive analysis or enhancing your creative strategy.
Still don’t want to give up control? Then consider this: Facebook says shifting to automatic placements reduces the cost per conversion by 71%. That’s an awful lot of freed-up ad budget. And if you just really must have control over where certain ads appear, use “Asset customization.” It will let you choose which images or videos people see in your ads depending on where your ads appear.
Lever #3: Increase Budget Liquidity
Here are a few ways to give the power of the purse strings to the algorithm. Because even if you minimize control on every other aspect of campaign management, without freed-up budgets, the party can’t really get started.
Increase budget-to-bid ratio. Calculate your daily budget based on the 50 conversions per week threshold needed to get out of the Learning Phase. So if you want to pay $1 per conversion, set your budget for at least $50 a week (probably about 10-15% higher because bids usually end up being that much higher than what you’ll actually pay).
Use Campaign Budget Optimization. As Facebook says, “Free the budget!”
Test creative at the ad level. Don’t create multiple ad sets, each with one piece of creative, and then run the ad sets against each other. Put the different pieces of creative all within one ad set.
Lever #4: Bid High Enough, and With the Right Strategy
The days of human-managed bidding are over. It’s vastly better to let machines manage the complexities of changing bids. But choosing a bid strategy is still best left to humans… if humans take the time to think about it carefully.
Facebook breaks out three types of bid strategy:
Lowest cost. Want maximum conversions from your budget? Then pick the lowest cost. Use this especially if you don’t know lifetime value.
Target cost. This gives a cost per result on average. If you want consistency, go with the target cost.
Lowest cost with a bid cap. Got a broad audience that’s not as likely to convert? This is a good option to control costs. Or, if you are willing to spend up to a certain amount for a given conversion, but no more… then choose the lowest cost with a bid cap.
Two extra pieces of advice:
If at all possible, bid according to lifetime value. It makes no sense to bid like all your prospects are created equal. They aren’t.
If you are using a bid cap, make sure that the cap is high enough. Otherwise, you’ll be severely limiting how often the ad gets shown, and thus you’ll be limiting the amount of data Facebook gets to work with, and… you know the rest.
Lever #5: Optimize for the right event
For niche or higher-value products with fewer purchases, try moving your conversion event up towards the beginning of the funnel. This usually gives the algorithm more data points to work with because for every step of the funnel toward the purchase, there are fewer conversion events.
Here’s how this can work: When we first launch a game/app, we’ll optimize for App Installs. These clearly, aren’t as valuable as purchases, but if we haven’t generated enough purchases (or aren’t generating enough purchases) we can optimize the overall system much faster by focusing on App Installs. As soon as we’ve got enough App Installs to move down the funnel, we’ll optimize for App Events like a purchase.
Facebook Advertising: Past, Present, and Future
Facebook and Google have made major strides in the last year towards simplifying and streamlining their ad platforms. This is overwhelmingly a good thing because of:
Allows more people to effectively use the platforms, regardless of their advertising skills
Saves ad managers’ valuable and very limited time
Gets better results
However… if you’re an experienced, proactive, and performance-driven advertiser, letting go of all that control is tough. It means completely rethinking how you advertise. It may also mean updating your skills in automated media buying because the algorithm can do a lot of Facebook ad management tasks better than people can now. We recommend you switch how you spend your time over to creative strategy and competitive analysis.
For a more detailed view of preparing for automated media buying, click here.
Video ads work. Really well. We have been watching them outperform still image ads for years now. If you have been holding back from using more video ads because they are expensive, stop. You might be reducing costs by avoiding video ads, but you are also avoiding their upside, which is significantly better performance. The performance improvements or testing of video ads are so significant that if you are not running them, we know you are losing money.
But maybe you know how to do all of that. Maybe you’re running several video ads right now, and you’ve already got a nice system set up to generate and test new video ads every week.
Great start. But you’re not done. Because no matter how much testing you’re doing, we recommend you do more.
A lot more.
5 Facebook Best Practices For A|B Testing New Ad Creative
Here’s why: From what we’ve seen after working with nearly a thousand companies and managing more than $3 billion worth of ad spend (much of it through video ads), we believe creative testing is the highest-return activity in user acquisition. It beats campaign structure, audience expansion, and ad copy testing – all of it.
Simply put, creative testing separates the winners and the losers in UA management right now.
So with creative testing being that important and that powerful… how do you do it with video ads? And if you’re already testing a lot, how can you do it better?
Here’s how:
1. Use Quantitative Creative Testing
If you’ve been doing standard A/B split-testing up until now, Quantitative Creative Testing may blow your mind. It’s something we designed expressly for the performance UA ad testing environment. It gets around many of the problems with traditional A/B split-testing, like the expense and time required to reach statistical relevance.
Quantitative Creative Testing operates on the principle of earthquakes over tremors. It works because most ads don’t perform. Maybe some ads do okay, but they don’t perform like breakout, 100x ads. And it’s the 100x ads you need if you want to compete today.
There’s an illustration below of what earthquakes look like in ad performance. We allocate ad spend based on an ad’s performance. So when you look at the chart below, you’re looking at the distribution of ad spend for over 520 winning ads. That slim orange column on the far right of the chart represents the tiny handful of ads that performed well enough to eat 80% of the budget. It’s that tiny fraction of all the ads we tested that Quantitative Creative Testing is designed to find.
This is what we mean when we say we’re looking for earthquakes, not tremors. We do not focus on ads that perform 5% or 10% better than the portfolio. We do not care about the middle of that chart. We’re looking for new winners – super-high performing ads that are good enough to replace the current winning ad before creative fatigue sets in. Typically, we have to test twenty ads to find one that’s good enough to beat the old control. Having run and tested well over 300,000 videos, on average we see a 5% success rate in finding new winning ads.
Quantitative Creative Testing uses two types of creative: “Concepts” and “Variations.”
Concepts are completely new, out-of-the-box creative approaches. They usually fail, but when they win, they tend to win big. About 20% of the ads we test are Concepts. Variations are small tweaks made to Concepts. We take the big ideas and modify them just a bit to see if we can make them work better. 80% of what we test are variations.
We test a lot of Concepts quickly, accruing just enough exposure for these ads to know whether they could be big winners or not. Often, these ads are only 80% brand-compliant. We like to play a bit fast and loose with these early ads, both in terms of statistics and branding, so we can find the big winners fast. This saves time, saves a ton of ad spend, and gets us just “good enough” results to go into the next phase.
We’ll take the winning ads and – if they’re not brand-compliant – tweak them ever so slightly so they meet brand requirements, but still perform well. We’ll also take the ads that performed almost well enough to be winners and retool them a bit to see if we can’t get them to do better. We take the winners from the first round and start running them against our current best-performing ads.
This keeps us several steps ahead of creative fatigue, and – combined with careful audience targeting – lets us extend the life of our best creative. It is a constant process. We don’t just find a new winner and then twiddle our thumbs until its performance starts to drop a week or two later. We have a constant pipeline of creative being developed and tested.
2. Create a System for Creative Refreshes
Staying ahead of creative fatigue is challenging. The better your ads perform, the more likely you are to want to pour more ad budget onto them. And the more ad budget you spend on them, the faster they fatigue.
There are several ways to beat fatigue (we just mentioned our favorite, Quantitative Creative Testing). But one other way to extend the life of creative is to develop a system for Creative Refreshes.
We developed a Creative Refresh strategy forSolitaire Tri Peaks that leveraged our Creative Studio, competitive research, and gaming design best practices to deliver five new high-performance videos every week. This allowed Tri Peaks to get much more ROI out of their existing creative assets, all while maintaining (and even improving) ad performance and ROAS.
There are three flavors of creative refreshes: Iterations, Revisions, and Simple Changes. Each of these approaches uses the original ad much like a template but switches out one or two key aspects. After testing thousands of ads like this, we’ve figured out which elements of an ad tend to improve performance the most, so we know which variables to play with.
Iterations
Add or remove one element from the original high-performing ad. In the ad examples below, we’ve iterated the original “New Concept” by adding a second, smaller brown cat to the iterated ad.
Revisions
Give us room to make several changes to the template for revision. Typical tweaks include resizing one or more elements, changing the header around, and/or swapping out different music.
Simple Changes
Typically have one significant change, like making a localized version of the ad, changing the CTA, adding or swapping Start and End Cards, or changing the text in some way. In the examples below, the Simple Change was just to switch the ad’s language from English to German.
These four types of creative are our current approach to a la carte testing. It’s a process we are constantly testing and changing. Even three months from now, we’ll probably be using it just a bit differently.
3. Give the Algorithms What They Want
If you’re a creative who hasn’t been updated onhow the advertising algorithms at Facebook and Google work now, you need to fix that. Like yesterday. Because any UA team that wants to succeed now needs to let the algorithms figure out ad placements. The machines do this work more efficiently than humans can, and not handing over this part of campaign management in Q4 2019 is going to hurt campaign performance.
This full shift over to algorithm-managed bids, placements, andaudience selection actually has big consequences for how we design video ads. We’re now basically just telling the ad platforms what we want and then sitting back and letting them deliver those requests, so we need to give the algorithms enough raw creative assets to do their jobs.
Testing
At the very least, create four versions of each ad: one for each of the viewing ratios shown below.
You have to do this because humans aren’t controlling ad placements anymore. The algorithms do that now. If you give the algorithms only one video ratio, they’ll be severely limited as to where they can show your ads. You don’t want that – it will cripple your ads’ performance.
If that’s more work than you want to do, try this trick: Don’t make ads in all three ratios at first. Instead, run the first versions of all your new ads at theimage ratio used in the newsfeed: 16:9. This will allow you to do a quick but effective test in a controlled “apples to apples” way. Then take the stand-out ads from that first round of testing and make them into the other two ratios. This saves a ton of work, as you only have to make three versions of winning ads, not three versions of every single ad you test.
By the way… just making your winning ads into three versions with three different ratios isn’t enough. At least not according to Google. They recommend you also give their platform three different lengths for each of your videos: 10 seconds, 15 seconds, and 30 seconds. Depending on where the algorithm shows your ad, those different lengths could result in very different performance metrics. And again, because the algorithm – not a human – is the entity figuring out optimal placement and audience combinations, we need to give it every opportunity to find the sweet spots.
If just the idea of making nine different versions of every winning video makes you feel tired (three ratios times three lengths), no worries. OurCreative Studio can make those versions for you.
4. Do Regular Competitive Analyses of Your Competitors’ Ads
Now that the advertising algorithms have taken some work off your plate, we highly recommend you spend more time doing competitive analysis. And do it in a systematic, documented way. We recommend using SensorTower’s share of voice feature to help you uncover the advertisers and creative that matter the most. It’s also enough to give you hundreds of ideas for your own ads.
Here are a few possible columns for a competitive analysis spreadsheet:
Advertiser
Title
Date
Platform/Network
Length in seconds
Call to action
Main Colors
Mood
Text density (the amount and size of copy)
Which words they emphasize
Screenshot or recorded video
Logo placement
Similar to other ads they’ve run… how?
What to test in your own ads based on this ad
As you review your competitors’ ads, also look for:
Storytelling tactics
Messaging
Which emotions they’re trying to evoke from the viewer
How the product is used in the ad
Colors and fonts
There’s a real art to doingcompetitive analysis. To do it well requires someone with both a creative and analytical mindset. So if you’ve been worried about keeping your skills current through the massive changes going on in UA, learning how to do great competitive analyses would be a really smart way to keep your skills up.
5. Test Ad Features
There’s an old concept in testing that describes testing different levels of things as testing “leaves” versus “trees” versus “forests.”
For instance, you can test call to action button colors. That’s an easy, simple, and very popular thing to test.
But it’s only testing leaves. It’s not going to get you very far. Sure, button color can get you a lift. A little lift. But most testing experts will roll their eyes at button color tests and beg you to think bigger. They’d want you to test “trees” and “forests,” not tiny, somewhat insignificant things like button colors.
Start and end cards are a great example of this. These two frames – at the end and at the beginning of ads – can make a major difference in performance. They help lure viewers in, and they can impress viewers with a strong call to action at the end of the ad.
But if you’re caught up in testing button colors, you aren’t going to have time to test big things like Start and End Cards. Or any of the other powerful ad features Google and Facebook are rolling out practically every week.
So be choosy about what you test. Test to find earthquake-level improvements whenever you can, not just tiny improvements.
This approach is, of course, very similar to Quantitative Creative Testing and the idea of concepts versus variations. But it refers more to the structure and features of the ad vehicles themselves. Google and Facebook are giving us some really cool toys to play with these days. Go try them out… even if it means you can’t test ten different shades of red on a button.
BONUS: Creative Testing Best Practices
Once you have creative ready to test, we recommend the following Creative Testing Best Practices. While you can test all your ads to statistical significance (StatSig), we don’t recommend this approach. StatSig is an extremely expensive and slow process. Instead, we recommend testing as follows:
Test radically different concepts and only modify winning concepts.
Restrict Targeting: US, Facebook Newsfeed, iOS, or Android. The Newsfeed, Instagram, and the Audience Network all have wildly different performance KPIs. We recommend isolating your testing to reduce variables.
Target 100 Installs. For most games/apps, getting 100 installs is enough to get a read on IPM (installs per thousand), so use this as your initial KPI to determine “potential winners.”
Always run a Facebook Split Test (3-5 videos + Winning Video).
For the three to five concepts you should test at a time, the goal is to kill the losers quickly and inexpensively. You will get both fast negatives and false positives on the margins, but you’ll know an earthquake when you see it – 100X ads stand out. The ads that do well, but don’t blow the doors off, are what we call “potential winners.”
ROAS Test: Take your potential winners and drop them into ad sets with your winning creative. Then let the gladiator battle begin. If the new video gets a majority of impressions, Facebook has named it the new winner. If it gets some impressions but it stops delivering quickly, we recommend re-testing/enhancing creative if it is within 10% of the winning video’s performance.
Testing New Ad Creative Conclusion
The smartest performance advertisers have always known creative testing is where the bulk of performance gains are to be found. It was true back when Claude Hopkins published Scientific Advertising in 1923, and it’s true now.
If you really want results, test. Then test again. Then test more. Test strategically and repeatedly. Even in late 2019, the vast majority of advertisers aren’t doing anywhere near enough creative testing.
If you have a new mobile game and you want to bring it to a worldwide market, you will need a new launch strategy with effective user acquisition at its core.
Launching a game is more than getting it into the stores and managing user feedback in the forums. Success is largely hinged on attracting and retaining the right users for your game in each market.
Here, we’re focusing on how Facebook can help you achieve this goal. What’s particularly challenging is that user acquisition advertising evolves fast. What worked for Facebook user acquisition advertising in 2019 won’t work nearly as well in 2020, and here, we’re going to help you focus on the future.
Worldwide Launch Strategy in 2020
Facebook pivoted toward algorithm-driven advertisinglast February and never looked back. Their new requirement forCampaign Budget Optimization is more evidence of the algorithm taking over, andFacebook’s Power5 recommendations for advertising best practices just drove the point home further. Both Facebook and Google have simplified and automated a lot of the levers user acquisition managers used to rely on. That trend will continue and accelerate in 2020. Now is the time for you to prepare your accounts for that acceleration by structuring them for scale with the best practices and new launch strategy we will describe here.
The big takeaway of Facebook UA advertising in 2019 is it’s best to let the Facebook algorithm do what it does best: Automated bid and budget management and automatic ad placements. Let humans do things the algorithm can’t do well (yet), like optimizing creative strategy.
If you want to take your game from soft launch to worldwide launch strategy using Facebook’s 2020 best practices, we have created this three-part series with our recommendations.
These are the launch strategy best practices we have developed by working with hundreds of clients, profitably managing over $1.5 billion in social ad spend. Based on an awful lot of trial and error (and quite a lot ofsuccesses, too), this is how to get the best return on ad spend possible and to launch your app efficiently.
It breaks out into three phases:
Early Creative Testing in the Soft Launch
Taking Your Campaign to the Next Level in the Worldwide Launch Strategy
Scaling Worldwide Through Optimizations
Phase One: Early Creative Testing in the Soft Launch Strategy
We have been doing well with “soft” launches. It gives us a great opportunity to pre-test creative, test campaign structures, identify audiences, and help evaluate our client’s monetization strategy and LTV model. By the time we’re ready for the worldwide launch strategy, we’ll have found several winning creatives and have a strong sense of the KPIs necessary to achieve and sustain a profitable UA scale.
Soft launches tend to work best if we focus on a limited international market. Usually, we will pre-launch in an English-speaking country outside of the US and Europe; Canada, New Zealand, and Australia are ideal picks for this. Choosing countries like those let us conduct testing in markets that are representative of the US, but without touching the US market. As we are not launching in the US, it will not spoil our chances of being featured by Apple or Google.
Once we’ve got the market selected, we pivot to:
Identifying the most efficient campaign setup
A simplified account structure, rooted in auction and delivery best practices will enable you to efficiently scale across the Facebook family of apps. We typically hear things like “My performance is extremely volatile.” “My ad sets are under-delivering.” “My CPAs were too high, so I turned off my campaign.” “I’ve heard I need to use super-granular targeting and placements to find pockets of efficiency.” The best way to avoid this is to structure your account for scale based on Facebook’s best practices. Facebook defined those best practices in Facebook’s Power5 recommendations earlier this year. But – in evidence of how rapidly the platform evolves – they fine-tuned their best practices again lately in their Structure for Scale methodology.
Structure for Scale
The gist of Structure for Scale – and of what Facebook wants advertisers to do now – is to radically simplify campaign structures, minimize the amount of creative you’re testing, and use targeting options like Value Bidding and App Event Bidding to control bids, placements, and audience selection for you. Facebook is building up a considerable body of evidence that this approach results in significant campaign performance improvements, though if you’re a UA manager who likes control, it can be an adjustment.
Compliment the Algorithm
The underlying driver of all these new recommendations from Facebook is we need to build and manage our campaigns to compliment the algorithm – not to fight it. One of the key benefits of adopting new best practices is to minimize Facebook’s Learning Phase. Ad sets in the learning phase are not yet delivering efficiently, and often underperform by as much as 20-40%. To minimize this, structure your account to give the algorithm the “maximum signal” it needs to get you out of the Learning Phase faster.
Results During the Learning Phase
Expect somewhat volatile results during this exploration period (aka the Learning Phase) as the system calibrates to deliver the best results for your desired outcome. Generally, the more conversions the system has, the more accurate Facebook’s estimated action rates will be. At around 50 conversions per week, the system is well-calibrated. It will shift from the exploration stage to optimizing for the best results given the audience and the optimization goals you’ve set.
Through all of this, keep in mind that Facebook has built its prediction system to use much data as possible. When it predicts the conversion rate for an ad, it takes into consideration the ad’s history, the campaign’s history, the account’s history, and the user’s history.
When the system says that an ad is in Learning Phase, it’s only a warning that the ad has not yet had enough conversions for the algorithm to be confident that its predictions are as good as they will be later. The standard threshold for confidence is 50 conversions, but having 51 conversions is not that much different from having 49. The more conversions you give the system, the better its predictions will be.
While it is best practice to let the algorithm manage placements and bids, we do still have quite a lot of levers of control over specific parts of campaign management.
Lever #1 Increase Audience Size
Increase retargeting windows beyond 1 day, 3 days, or 7 days and make sure retargeting increments align with website traffic volume.
Bucket Lookalike audiences into larger groups. For example:. 0-1%, 1-2%, 3-5%, 5-10%.
Group interest and behavior targets that have high overlap together, but make sure your creative strategy is the same for each segment.
Minimize the audience overlap. Use proper audience exclusions and make sure you are excluding past purchasers.
Increasing audience size can help us gather more data and prevent inefficiencies caused by targeting the same audience across multiple ad sets.
Exclude past purchasers and website traffic from prospecting campaigns. This allows us to better track KPIs and to ensure we reach new users, not those who have recently purchased and are no longer in the market for your app or offer.
Structure initial launch audiences for maximum performance. Here’s an example of how we do it:
Once you have about 10,000 installs you can move to AEO (App Event Optimization for purchases). Then your audience structure can shift to something more like this:
And once you have about 1,000 purchases, you can move to Value Bidding and reselect your audiences again:
Lever #2: Combine Placements: Select automatic placements for better results.
The more placements your ads appear in, the more opportunities you have to reach or convert someone. As a result, the more placements your ads are in, the better your results can be. And you won’t get penalized for letting the algorithm test new placements. After the Learning Phase, the algorithm will just not show your ads where they don’t perform. It can do the placement testing for you.
Facebook’s system of Discount Bidding (Also known as “Best Response Bidding”) will always try to find the lowest cost results based on a campaign’s objective and within the audience constraints set by the advertiser. But if you’re willing to widen the delivery pool by including additional placements, you’re giving the algorithm more to work with. That gives it a better shot at finding lower-cost results and delivering more results for the same budget.
Asset customization gives you control over placements
The ad sizes and ratios you use, of course, determine which placements those ads can appear in. So you’ll want to choose the images or videos people see in your ads based on where those ads may appear.
If you elect to manually select placements, use asset customization. It will let you specify what ads are shown for specific placements to ensure your ad displays the way you want. Asset customization also allows organizations to easily choose the ideal image or video for some placements within one ad set. If you have a content strategy that requires specific assets to appear in specific placements, this option is your best bet.
Lever #3: Increase Budget Liquidity: Select automatic placements for better results.
Increase your campaigns’ budget-to-bid ratios. Calculate daily budgets based on the cost to achieve Facebook’s 50 conversions per week threshold.
Use Campaign Budget Optimization. Our current best practice for CBO is to separate prospecting, retargeting, and retention into separate campaigns. Otherwise, CBO will push the budget toward retargeting and retention. Focus on a split of roughly 70% prospecting, 20% retargeting, and 10% loyalty (loyalty is optional). Segment your budget at this high level, then let CBO do the work within those objectives.
Test creative at the ad level instead of creating separate ad sets for individual creative assets. Some clients will have one ad set for each piece of creative they want to test. This isn’t the best practice because they are likely targeting the same audience within each ad set (which creates 100% audience overlap) and each ad set only has one ad. Instead, set up multiple ads with different creatives in a single ad set. It’s a fast, streamlined way to test how multiple creatives will perform.
Use Placement Asset Customization. This is the setting to use if you want to build complementary messages across platforms and benefit from utilizing placement optimization, but you want to be able to specify which creative asset is used for each platform or placement type.
Lever #4: Bid smarter.
Never underestimate the power of choosing the right bid strategy. Make your pick carefully (and test it) based on your campaigns’ goals and cost requirements. Whatever bid strategy you pick is basically giving the Facebook algorithm instructions on how it should go about reaching your business goals. Here are a few things to consider:
Lowest Cost: Directs the Facebook algorithm to bid so you achieve maximum results for your budget. Use the Lowest Cost when:
You value the volume of conversions over a strict efficiency goal.
You have certain audiences you just want to get in front of, and the conversion rate is high enough to justify the spend.
You’re unsure of the LTV of a conversion.
You’re already using the lowest cost bidding and are satisfied with the cost per result.
Target Cost: This aims to achieve a cost per result on average. So even if cheaper conversions exist, Facebook will optimize for the specified cost per result. Use it when:
You want a volume of results at a specific cost per result on average, and you want consistency at this cost.
You’re willing to sacrifice some efficiency for consistency.
Lowest Cost with Bid Cap: Sets a limit on how high Facebook will bid for an incremental conversion. Use it when:
You know the maximum amount you can bid per incremental result, and any incremental conversion above this value would be unprofitable and unwanted.
You’re targeting a broader audience with a lower likelihood to convert, so you want to appropriately manage costs.
You have a highly segmented audience with a defined LTV for each segment, and you understand the associated bid.
Whenever possible, assign a value to your audience. Ignoring LTV when you bid just doesn’t make sense long-term.
If you’re using a bid cap, make sure that the cap is high enough. We suggest setting your cap higher than what your goal actually is. Still not sure what’s high enough? The average cost per optimization event your ad set was getting when you weren’t using a cap can be a useful starting point. Just keep in mind that bids are often higher than costs. So setting your bid cap at your average cost per optimization event could result in your ads winning fewer auctions.
A word about campaign structure
Campaign setup matters. A lot. We need to figure out which campaign structure is going to work best for the particular app we’re launching. That usually means using Campaign Budget Optimization settings, but we also have to decide if we want to initially optimize for Mobile App Installs (MAI) or App Event Optimization (AEO).
Typically, if we don’t already have a large database of similar payers, we will need to start with a limited launch using (MAIL) app installs as our campaign optimization objective until we’ve got enough data to shift to AEO (app event optimization). For initial testing, we like to buy 10,000 installs to allow for testing of game dynamics, KPIs, and creative.
Once we have 2-3 rounds of initial testing and data complete, we recommend switching UA strategies to focus on purchases using AEO and eventually VO to drive higher value users. This one-two punch of AEO and then VO is a great solution that allows ROAS to start to flow through the system for LTV modeling and tuning. Ultimately, what we’re doing is training Facebook’s algorithm for maximum efficiency and testing monetization and game dynamic assumptions.
Audience Demographics
Audience demographics get a lot of attention at this phase, too. We’ll review the performance of our campaigns at various age and gender thresholds, first using broad audience selections to build up a pool for evaluation and eventually allowing Facebook to focus on AEO/VO audiences to test monetization.
Facebook’s recommendation is to start as broad as possible and run without targeting, and we agree with this. Also, keep your account structure simple by using one or two campaigns and minimal ad sets where you reduce or eliminate audience overlap and set your budgets to allow for 50 conversions per week per ad set. Facebook refers to this as “Structure for Scale” and it gives their algorithm the best opportunity to learn and adapt to the audience you’re seeking. It will help get your ads out of the Learning Phase and into the optimized mode as quickly as possible.
Testing and optimizing creative
We believecreative is still the best competitive advantage available to advertisers. Because of that, we relentlessly test creative until we find break-out ads. Historically, this focus on creative has delivered most of the performance improvements we’ve made. But we’ve also found that new creative concepts have about a 5% chance of being successful. So we usually develop and test at least twenty new and unique creative concepts before we uncover a winning concept.
That’s far more work than most advertisers put in, so to stay efficient we’ve developed a methodology for testing creative that we callQuantitative Creative Testing. QCT, combined with somecreative best practices, allows us to develop the new high-performance creative concepts that clients need to dramatically improve their return on ad spend (ROAS) and to sustain profitability over time.
Our overarching goal with all this pre-launch creative is to stockpile a variety of winning creative concepts (videos, text, and headlines) so we’re ready for the worldwide launch and can launch in the US and other Tier 1 countries with optimized creatives, audiences, and a fine-tuned Facebook algorithm.
Collect lifetime value data
This is where optimizing the game’s monetization comes in. While we’re working on campaign structure, what to optimize for, and developing creative, we’re also collecting lifetime value data. This helps us meet the client’s early ROAS targets based on their payback objectives. Most mature gaming companies want a payback window of one to three years, which is pretty easy to attain if all the other aspects of a campaign are on track.
Low expectations for pre-launch
Post-launch metrics tend to be noticeably stronger than pre-launch metrics. Several factors contribute to this:
The same creative we tested at pre-launch will usually perform better when it’s used for the worldwide launch.
The US audience we had held back from advertising for pre-launch will ultimately make up about 40% of the total ad spend once the worldwide rollout is underway. This gives us a lot of potential user base to go after in the most cost-effective ways.
We tend to see a correlation between higher reach and higher ROAS on Facebook. So the worldwide targeting we use post-launch also gets a boost in performance over the limited market targeting we did pre-launch.
Shifting Towards The Worldwide Launch Strategy
Pre-launch campaigns can run from anywhere between a week to a month. They are an investment, but they let us hit the ground running with proven creative, an efficient campaign structure, and a monetization strategy that further boosts profitability. For advertisers who want to scale fast, this is absolutely the way to go.
Phase Two: Taking Your Campaign to the Next Level in the Worldwide Launch Strategy
Creative testing early and often was one of the key takeaways from our first post in this three-part series. We discussed how to complete the pre-launch phase of launching a gaming app and structuring your account.
Now, we’re ready to gear up for the worldwide launch because we have tested creative, optimal campaign structure, and a monetization strategy that gives you the payback window you want.
To prepare for this global launch, we typically start by casting a wide net with different campaign structures so we can identify top-performers and scale them quickly.
We also focus on:
Which geographies to use
We’ll test Worldwide, the United States only, and Tier 1 minus the United States to see which performs best. Then we’ll drill down further as soon as we have enough data to decide which option to prioritize.
Testing audiences
We’ll test different interest groups, and we’ll also do a ton ofwork with lookalike audiences as soon as we’ve got enough purchases to start working with that data. We do so much work with audience selection that we built a tool to make it easier. Now ourAudience Builder Express tool lets us create hundreds of super-highly targeted audiences with just a few clicks.
Which optimization goal works best
We did this in the pre-launch, but it has to be re-tested again now that we’re advertising in dramatically larger markets. Typically we’ll choose Mobile App Installs (MAI), App Event Optimization (AEO), or Value Optimization (VO).
Developing a campaign structure grid
These are spreadsheets that block out campaign structure and different campaign settings including ads sets, the budgets for each campaign, and more. They are basically a blueprint of the entire launch strategy.
Here’s what one section of a campaign structure grid might look like:
Sometimes we’ll have two campaign structure grids – one from our team, and one from Facebook. Generally, Facebook’s recommended best practices are the right way to go. Those are well summed up in the first post in this series, intheir Power5 recommendations, and reviewed in detail in theirBlueprint certification training.
We agree with this approach, but every company is a little bit different. So while we usually follow (and always endorse) Facebook’s best practices, it’s critical to understand the backstory and the technical side of why those best practices work. When you look at the underlying principles and the new features we have to work with, every so often, for a particular client situation, we’ll bend those best practices a bit.
For example, heavy mobile app installs are recommended for the first week of launch. We’ve seen success with this strategy, and we’ve also seen some games scale more profitably with Value Optimization in week one than they did with Mobile App Installs. This is why we recommend casting a wide net instead of exclusively optimizing for Mobile App Installs.
The Learning Phase
We also want to get the campaigns out of thelearning phase as quickly as possible because it tends to suppress ROAS by anywhere from 20 to 40%. Getting out of the learning phase typically requires 50 conversions per ad set per week. Once we’re out of the learning phase, we’ll also avoid any “significant edits” to top-performing campaigns and ad sets, as those would put those campaigns back into the learning phase. Facebook’s system defines a “significant edit” as a campaign budget change of 40% or more or any bid change greater than 30%.
Then there’s the issue of budgets. We aim to balance budgets within one to three days of launch so we can then shift spend to top-performing segments. We do that by first reducing the spend from underperforming geographies and optimization goals, and then reallocating it to top-performing geographies and optimization goals.
Once that’s all balanced out, we can safely increase the budgets for CBO campaigns and ad set budgets. We can also launch new campaigns with these same optimized settings.
We will have achieved a successful worldwide launch strategy – the exposure will have gone global. The campaigns will be profitable and operating with the best efficiency we can deliver for now.
The next step is to fine-tune that efficiency and try to scale up further with audience expansion.
Phase 3: Scaling Worldwide Launch Strategy Through Optimization
In the first and second segments of this series, we did our pre-launch strategy work and successfully launched a global campaign with positive ROAS. Now it’s time to optimize what we’ve got and make it even better.
The launch strategy shown below is a snapshot of everything we’ve done so far. It summarizes bid strategies and optimization goals, geographic roll-out, budgets, placements, and which audiences we’re targeting. Everything, basically. It’s not the sort of thing you would want one of your competitors to get hold of.
Audience Expansion, Creative Testing, and Creative Refresh
These are the three fronts we will tackle to optimize this campaign launch strategy.
1. Audience Expansion
Once again, we’ll fire up our Audience Builder Express tooland start isolating specific audiences. In addition to the standard in-app event audiences (registrations, payers, etc), we’ll build and test audiences based on these KPIs:
Spend – all time
Last 7 days spend
The last 30 days spend
First activity date
Last activity date
Last spend date
First spend date
Spend in first 7 days
Spend in first 30 days
Highest level
Value-based audience by setting a minimum value
Split by Android and iOS and select individual countries
This is what it looks like as you build up your payer base. As the data accrue, you’ll be able to build manipulated audiences like this:
A little bit later on, as your title continues to grow, you can build even more audiences focused on users who pay early and pay a lot:
The other benefit to audience expansion
Great creative takes a lot of work to develop, so we want it to last as long as possible. We also want to find every single person on the planet who could be a high-value customer. So we very carefully expand audiences toavoid audience fatigue as much as we do it to avoid creative fatigue.
But being able to tie these audiences and rotate through them means our creative lasts significantly longer. It allows us to find a huge potential customer base, and we get thousands of conversions we might never have found or would have spent way too much to get.
Being able to control and expand audiences like this will also be valuable later on, as we scale up and the spending goes up because audiences burn out even faster as campaigns scale. Exploiting every possible audience expansion trick, at every step of the campaign, is critical. Being able to do it efficiently and effectively is a massive competitive advantage.
2. Creative Testing
Just as audiences burn out faster with high-velocity campaigns, creative burns out faster, too, of course. So we have to be aggressively testing all the time. And we do: We are constantly rotating through new creative.
To find creative that performs at the level we need, we usually have to test twenty ads to find one piece of creative good enough to replace control. That means we need a constant stream of new creative – both new “concepts” (completely new, out-of-the-box creative approaches) and new ad variations. Our creative development work is about 20% concepts and 80% variations.
This creative testing machine is running all the time, fueled by creative from ourCreative Studio. It has enough capacity to easily deliver the 20 ads we need per week and can handle delivering up to hundreds of ads every week.
Because the game is global by this point, we’ll also need localized creative assets. Creative has to be in the right language and may even be optimized for localized placements or cultures.
So we don’t need just one winning ad every week. We need that winning ad cloned into every language and optimized for every region. Of course, all those ads also have to be at the right aspect ratios. In addition, optimized for Facebook’s 14 different ad placements. That’s when creative development gets really work-intensive. But the Creative Studio can handle that. They’re adept at creating all those variations efficiently.
Dynamic Language Optimization
That’s the creative development side. There’s also a huge amount of testing strategy required to grow campaigns like this. First, we have to decide when and how we’re going to use Dynamic Language Optimization. Also, when and how we’ll use Direct Language Targeting. These two levers can make a nice difference in campaign performance, but they don’t always work. Or sometimes they need tweaks to work well.
We’ll also test worldwide versus country clusters, optimizing for large populations based on the dominant language of those populations. With dozens of countries and at least a dozen languages in play, this gets complicated. Fortunately, we have tools that make sorting all these inputs easy. And it is worth the work. Matching the right ad, language, and country cluster can improve performance by 20% or more.
3. Creative Refresh
Even with all the optimization like creative development and audience expansion tricks, creative is still going to get stale. So we have to be slowly rotating new, high-performance creative into ad sets all the time. We don’t just stop showing one piece of creative and jump over to the new ad. As you probably know, even if a new ad tests well, it doesn’t mean it will beat the control. So we do careful “apples to apples” creative tests to ramp up new assets.
New Concepts
Change Many Elements
Large Changes & Impact!
Low Succes Rate – 5%
Change Only 1 Element
Use A/B Testing Methods
Small Change & Impact
Worldwide Launch Strategy Conclusion
So that is how we’re launching new gaming apps on Facebook using their newest best practice, structure for scale. This process is working well and we’re constantly testing, tweaking, and enhancing it.
That’s the fun thing about user acquisition on Facebook: It’s constantly evolving. The article we’ll write for how to launch a gaming app in 2020 Q4 (and even Q1) will be different from what you’ve just read.
Scaling a user base requires great creative, systematic testing, and enough ad budget to run those tests. There is no way around it.
We’ve seen too many good products and apps fail because founders, CMO’s, or UA managers didn’t want to invest in creative and creative testing. They wanted to scale their user base based on “the strength of the brand,” or on word of mouth, or on tactics like influencer marketing.
Once in a while, those things work. Word of mouth can be enough to expand a user base. But it almost never happens at the rate the UA manager wants, or in time to meet quarterly growth figures. App store optimization can also help – a lot – but it will only take you so far. Most marketing tactics simply aren’t as effective and as reliable as properly planned and executed performance marketing, aka data-driven advertising.
So while it might sting a little to invest $10,000 or $100,000 to scale up, it’s the best investment you can make. Great creative isn’t free, but it’s the difference between being successful and cost-efficient versus using poor ads that waste ad money. And it takes money and expertise to find and amplify that 100x creative. It requires an ad budget that doesn’t skimp.
How to Scale Your User Base
Your first month of advertising isn’t “wasted ad spend”
We can’t stress enough the importance of great creative. From what we’ve seen of running over a billion dollars worth of ad spend, it’s the creative that determines whether apps can scale or not.
If you want to stand out among the sea of ads and apps competing against yours, the creative has to stand out, too. Your creative has to nail your value proposition, employ proven conversion rate hacks, tell a compelling story, and look great. All at once. Without all those components in play, even a great app is likely to flop in today’s environment.
We’ve also reached the tipping point with AI-driven advertising platforms. While “the machines” can’t do a launch strategy or develop a creative strategy, they can take campaign goals and known success factors and weave them into a positive return on investment.
But even the machines need a body of information – a dataset, some ad spend – to get there. The algorithms are only as good as the information we give them. Without some account history, there is no data to interpret.
That’s part of why the money you’ll spend to build that advertising dataset isn’t “wasted ad spend.” It’s an investment…just as much as the wireframes of your app were an investment. You needed those clunky first drafts to produce the final product. There is no final draft without the first one.
The investment required to launch a new product
So the first month of advertising is never wasted. It’s the price you have to pay to reach month two and the type of ROAS you want.
Of course, none of this is new. PPC marketers struggled with this price of entry problem fifteen years ago, back when Google AdWords launched. John Caples and his peers wrestled with it back in the 20s. Launching a new product requires a certain investment to figure out which creative works.
Even now, with mobile app installs and value optimization and all the other conversion models, modern UA managers have, it takes a certain number of clicks and money, and creative to find out which messaging drives installs, and, eventually, high-value customers.
We can’t just flick a campaign on and have perfect campaign settings, perfect audience selection, and proven, 100x creative.
What’s required for scaling your user base (or create one from scratch)
Even when the owners of an app understand this, there’s always pressure to minimize the learning curve. That can be done, but it still costs money.
We generally expect it to take a month to find the right combination of creative, campaign structure, and audiences to turn a profit. It typically takes 20 pieces of creative to find one true stand-out performance driver because only 5% of new creative will outperform the previous winning creative. With a 95% failure rate, heavy creative testing is required to become profitable unless a big win comes very early in the testing process.
That high-performance ad’s lifespan can be extended with careful audience modeling and manipulation, but its performance is eventually going to decline. Then you’ll need another 20 pieces of creative to find the new super-performer that can replace it.
So how fast will each piece of creative burn out – what’s the rate of creative fatigue? It depends… on ad spend, on audience size, even on the time of year. But to give you a rough estimate, a piece of creative that has $10,000 a week spent on it can probably last 2-3 weeks. A piece of creative burning through $100,000 a week may last only 3-4 days.
How much creative you’ll need is directly tied to your ad spend. Some advertisers might be able to get away with as little as 40 pieces of creative in one month. Other, super-high volume advertisers may need four to six times that amount. Fortunately, that’s not a problem. You can offset some of the risks with creative testing by relying on competitors – you’ll need to establish a process to identify profitable ads for competitors and to use those ads for inspiration in our creative testing process.
Three things that will definitely cause you to fail
While there are a lot of unknowns involved in scaling a user base, there are some things we’re very sure of:
1. Cheap creative will kill your chances.
UA advertising is just too competitive now to put out second-rate creative and expect “the power of the brand” or amazing word of mouth to make up for the difference. Without great creative, your app doesn’t have a shot.
2. Testing is essential.
Testing probably has the highest rate of return for anything you do in your business. Yep – anything. No matter who you are, no matter how amazing your brand or your product or your customer experience, if you aren’t testing, and testing like your company’s future depends on it, you’re losing money.
Creative testing is the single best way to grow. And simply doing a few tests won’t cut it. You need a hyper-efficient, strategic testing methodology that can reduce the time it takes to find new creative winners. Without this, you’re toast. So focus on building a creative testing process that’s designed to reduce the amount of spend required to test each variation. Fail fast on losers and scale winners quickly.
The advertising algorithms have become better at bid management, ad placements, and audience selection than humans are. Facebook and Google’s ad platforms now reward (if they don’t outright force) advertisers that hand over these parts of campaign management to the algorithms. Trying to do these parts of campaign management manually will crush your chances for growth.
Scaling Your User Base Conclusion
Scaling a user base is expensive. We get it. It’s even more expensive for new app owners and teams who do not have as much ad budget as they’d like. But don’t view the early weeks of performance advertising as “wasted money.” Think of them as research.
Strong creative versus poor creative is the difference between making money and losing money. Any company that wants
to invest in paid acquisition must commit to the creative testing process.
There’s no way to know exactly what’s going to work until you’re in the game. Even with smart management, great creative, and a hyper-efficient testing methodology, it costs money to scale a user base.
It’s expensive. But it’s way less expensive than scaling a user base the wrong way.
So if you’re going to spend the money, why not get the results you want?
The holiday shopping season is upon us. For advertisers, Q4 and particularly the week surrounding 2019 Black Friday is unlike any other time of the year. Ad costs typically spike by 25% or more. The competition for quality inventory is fierce.
E-commerce advertisers are managing their boom time, while other advertisers – like mobile games and apps – are hoping to just close the year strong.
2019 Black Friday & Facebook Ad Playbook
Late Q4 is the busiest time of the year for retailers, so it’s not like the other ad platforms are quiet. But Facebook advertising gets particularly competitive from October through until December 23rd. But even though Facebook ads prices spike during late Q4, it’s still the best platform in town. Most major advertisers will be bidding aggressively.
Even with the inflated prices, most eCommerce advertisers will do well on 2019 Black Friday. A recent study from Shopify Plus showed that eCommerce marketers say Facebook ads are the most effective channel for new customer acquisition during the holidays.
Of course, it’s no surprise that each year ads get more expensive around 2019 Black Friday, Cyber Monday, and all the December holidays. Every advertiser knows this. They just go into the season with a brave face anyway, ready to bid high to hit their annual targets. Anybody who’s ever looked at a Facebook Ads dashboard during the holidays has had to swallow a lump of coal when they looked at their cost per click.
And sure enough: 80% of eCommerce marketers say “rising ad spend” is a concern for holiday marketing.
Despite the expense and the competition, Q4 is a massive opportunity. For retailers, it’s an opportunity to maximize the best buying season of the year. For mobile games and apps, the holidays precede the most cost-efficient advertising season of the year and what will be the lowest CPMs of 2020.
Done right, the ramp-up to holiday advertising can be as important as the holidays themselves. Advertisers can leverage retargeting, email lists, and other more cost-effective channels after December 8th – if they’ve scaled up their campaigns properly before that.
But don’t underestimate the after-Christmas shopping boom. Everybody likes to splurge with their Christmas money and buy themselves what Santa didn’t bring. That’s why the period after December 26th can be especially effective. Take this time to test out new device ads (like the iPhone 11), video, and new messaging/creative. And don’t stop until January 15th or even Valentine’s Day. Many traditional advertisers pull back their advertising at the beginning of the year, leaving another nice window of opportunity for the rest of us.
2. Increase Average Order Size.
When user acquisition costs rise, you have two choices for preserving profits: cut your overhead/product costs, or raise average order size. Fortunately, increasing average order size complements what’s going on in Q4 nicely – people are spending more, both on themselves and others.
There are plenty of ways to increase average order size:
Bundling products
Offering extra features for a discount
Using $-off discounts (“spend $X, get $ off” offers)
You may also want to just skip this average order size strategy entirely, too. Depending on your company and your situation, it could make sense to just go with a loss leader in Q4 and use it to build your customer base.
If you manage the loss-leader strategy well, you could break even (or make a very slim profit), but you’ll add a ton of people to your buyers’ list. Pair that with effective retention marketing, and Christmas could be a nice opportunity to just find as many new customers as you can.
3. Wait it Out or Find Pockets Of Efficiency.
Of course, not everybody is in eCommerce. If you do app marketing or lead generation, the holidays present a very different problem.
For Facebook advertisers who aren’t in eCommerce, the best time to scale spend during the fourth quarter is between October 1st through Thanksgiving. CPMs increase during that time, but not too much. Then we recommend you pull back or shift spend between November 28th through December 10th.
Here are some other suggestions to help you combat rising prices during peak CPM cost increases:
For Budgeting
If you’re going to spend money in the fourth quarter and you’re not an eCommerce company, try to frontload spend as much as possible in October and November.
For Audience Targeting
Focus on less competitive markets during high-demand periods.
Allocate more budget to Android. It tends to see a less pronounced increase in prices.
Leverage data from International campaigns to scale up in EMEA (Europe, the Middle East, and Africa), APAC (Asia-Pacific), and LATAM (Latin America) where holiday competition is not as intense.
For Bidding
Scale-up Worldwide targeting by using value optimization to scale in global markets, while simultaneously optimizing for the lowest cost per purchase. That tends to preserve ROAS while making the expansion work.
Facebook research for its new Structure for Scale (S4S) framework has shown that ad set delivery stabilizes when an ad set achieves at least 50 unique conversions per week. They’ve found a direct correlation between ad sets that achieve this volume, reduced CPAs, and stronger ROAS. Occasionally the ROAS improvement can exceed 25%.
Start small with Minimum ROAS bidding, but use it. Minimum ROAS bidding allows advertisers to input their desired return on ad spend for each ad set. You can set a minimum ROAS with a number greater than 0.01%, then Facebook will stop delivering your ad if they can’t hit that specified percentage. It works best if you start by testing a low ROAS goal (<1%) against a broad audience, then inch up incrementally if performance is not there (1%, 2%, etc). Don’t start high and scale it back; minimum ROAS works better being incrementally increased.
Use AEO for Purchase Manual Bids. If you’re experiencing under-delivery or lower-quality conversions with autobid, consider switching to highly competitive smart bids (the lowest cost with bid cap). With unpredictable bid conditions like during the holidays, smart bids are a good way to maintain a stable delivery.
For Creative
Plan for more frequent creative refreshes to fight creative fatigue. You’ll probably have to plan ahead of time for this, as most employees want at least some time off around the holidays. Or, if necessary, look to a creative partner to expand capacity.
Develop holiday-themed creative to increase relevance scores. This can help reduce some of the higher costs of holiday ads.
Test Playable ads on the Audience Network to drive more engaged, high-quality installs. Facebook says these ads are getting the best performance of any ad format right now.
Fortunately, the expensive days do pass. Almost like magic, on December 26th, costs drop. Most of the eCommerce marketers have spent their budgets, sold their inventory, and consider the year done.
This is when non-eCommerce marketers – like games and mobile apps – have their heyday. They will enjoy some of the most efficient CPMs of the year from December 26th through to Valentine’s Day on February 14, 2020.
CPMs After 2019 Black Friday
Take advantage of the drop in CPIs and influx inventory from December 26th through Valentine’s Day by using “Auction Sales.” After Christmas is also a great time to target new device users, and device-specific creative can often get you an extra bump in relevance. Of course, if you want to dominate the bidding during these magic days, you’ll have to have set aside some budget ahead of time.
4. Focus on Mobile.
Everybody knows mobile traffic now exceeds desktop traffic. But many marketers still believe “mobile traffic doesn’t convert”. Or, at least that it does not convert as well as desktop traffic.
That might no longer be true.
A study of Google Shopping ads revealed a dramatic increase in mobile conversion rates in the last few years. The conversion rates for shoppers who begin and end their buyer’s journeys on mobile devices have increased by 252%.
But wait… there’s more: “The path of shoppers starting their search on desktop and completing their purchase on mobile rose 259% year over year.” In other words, some people prefer to check out via mobile rather than on a desktop.
Of course, that’s Google Shopping, not Facebook ads. But Facebook did its own research. They also found that mobile users have become mobile shoppers.
5. Use Video.
Suppose you have been hanging back from investing in video or investing more into video. Well, it might be the edge you need for Q4 2019.
And yes, Virginia, there is still enough time to get videos made before the major shopping holidays.
Next Steps to Prepare for 2019 Black Friday
How will your company or agency manage the Q4 Facebook ad cost increases? Did your strategies for Q4 work well last year? Think about where you’ve been to strategize for where you’re headed. Just do think fast; 2019 Black Friday is upon us.
A new year — a new decade — is just two months away. As you think ahead to your staffing and UA strategy, it’s important to consider just how much user acquisition advertising has changed in the last two years. Machine learning that drives the algorithms for Facebook and Google, and the massive increase in automation tools to maximize campaign performance, have shaken up the industry and the marketing department.
Due to this advanced, user acquisition advertising requires less work and less expertise than it used to. That may affect UA team staffing in 2020 and could push UA and growth teams to merge.
UA Strategy and Growth Teams in 2020
UA Automation is a Major Competitive Advantage
Facebook pushed automation forward in several ways, including by making Campaign Budget Optimization mandatory. They also rolled out best practice approaches to total campaign management like theirPower5. Google hasn’t held back, either. They’ve stepped back a little from a total “black box” approach, but they are still pushing advertisers to automate most of their campaigns.
What’s become clear in the last month or two is that the platforms aren’t just offering these automated features, and they aren’t just pushing advertisers toward them. Now, having human-managed campaigns has become an impediment. Not leveraging Facebook and Google’s new AI-driven automation like VO (Value Optimization), AEO (App Event Optimization), DLO (Dynamic Language Optimization), DCO (Dynamic Creative Optimization) and MinRoas Bidding (minimum Return on Ad Spend) make your business less competitive.
We expect this “automation as advantage” shift to become more evident – and more pressing – in the coming months. Both Facebook and Google are rapidly moving towards automation, possibly even total automation for certain types of advertiser profiles. And some market niches, like gaming, may be particularly ripe for expanded automation features. We’ve seen Google do this in the past with Google App Ads. Facebook usually isn’t far behind.
UA Teams Must Evolve
This shift towards automation is obviously going to take some work away from user acquisition teams. Smart UA managers willpivot over to creative strategy and testing. But if you had people on your team that was exclusively doing media buying or that were only adjusting bids or budgets and building new lookalike audiences … their desks may be empty soon.
So yes: Expect some layoffs and a reduction in user acquisition team sizes in 2020. EvenUber laid off a third of its marketing department this summer. We suspect this is driven at least in part by new automation capabilities.
But here’s the doozy: The automation capabilities driving those layoffs aren’t just going to affect a small group of well-funded advertisers who have access to expensive adtech. The automation driving these changes in user acquisition advertising is available to everyone. Facebook and Google’s AI capabilities are free.
This could affect not just the user acquisition community, but also ad agencies, marketing consultants, and in-house marketers all over the world, especially those firms that focus on the SMB market (small to medium size businesses). It takes less work and expertise to manage digital advertising than it used to.
UA Strategy
Again, smart UA team members and other individuals will pivot tocreative strategy,creative competitive analysis, andcreative testing and optimization. Or they’ll become specialists in how the algorithms work and in the new features the platforms are offering, likeGoogle’s Pre-Launch tool, so they can better manage their remaining levers of control servicing the top end / most sophisticated advertisers. But their skill set has to evolve too.
You can’t be in an industry that’s evolving as fast as UA is and not radically and continually improve your skills. Otherwise, you’re like a Morse code operator in a Verizon store. We’re just seeing that rate of evolution in the span of months instead of decades.
UA Evolution Affecting Employers
As you consider your own UA team now and for 2020, there may be an opportunity to reduce some overhead or hire less experienced media buyers who are less expensive. Team members must be agile, proactive, and strategic in how they manage both their time and the scope of their job description. Anyone who can’t do that may need to find another position.
So leaner UA teams are probably the future… and we mean “the future” as in Q2, 2020.
UA Strategy and Product Teams Will… Align, Overlap, or Merge?
So the roles of individual user acquisition teams may change. But the role of user acquisition teams as a whole may change, too.
For a long time, we’ve built a bit of an organizational wall up between acquisition teams and growth teams. The functions of these two teams were fairly separate. UA teams got the customers. Growth or product teams tested the app for better engagement, monetization, and lifetime value.
But is that separation really necessary – or even beneficial? As both teams are interested in LTV, and as advertising platforms simplify to the point where advertisers just give the algorithms a profile of the type of customer they want, and then send the algorithm out to get that type of customer, might it make sense to overlap or merge the staff and the functions of UA and product teams?
It’s essential to consider the role ofcreative development and creative strategy in all this, too. Ads use an awful lot of creative assets from the apps themselves. So could creative testing morph from a user acquisition role into aspects of product development? Could growth teams be influenced by user acquisition so that they develop different paths through apps for different types of customers?
Some companies are already exploring how UA teams and product teams can be more closely aligned. Creative andquantitative testing may be their common ground. And that could mean (among many other things) merged teams and thus some staffing redundancies.
Given the rate of change, we’ll know soon enough where it all goes.
It is a mobile world. With more apps and app discovery channels than ever before, the opportunities for advertisers are simply proliferating. The trouble is, your competitors know this, too. They are advertising like a house afire, further distracting your already distracted audience. So how do you stand out? Having a great brand matters, of course. And the type of ads you run can make a significant difference. Video app ads have risen to the fore of creative strategy for many high-performing app advertisers. This followed numerous studies that have shown video app ads to be particularly effective. Perhaps unsurprisingly, given their versatile and dynamic nature.
We have seen hundreds of clients get great results with video app ads, even if they are only adding motion to static images. Glu’s interior design gaming platform, Design Home, increased their return on ad spend by 70% with new video concepts and variations from our Creative Studio.
Not every video ad you make is going to lift ROAS by 70%, of course. We rigorously test creative. Usually, it takes about twenty tested ad concepts to find one ad that’s good enough to beat the control.
These seven best practices can help get you there:
1. Emphasize the user’s experience over your app’s description.
Video ads for apps and traditional TV-like ads are different animals. We interviewed Miao Xing, a Google product manager who oversees the company’s app ads creative solutions. She tells us “app users don’t need to be set up with sweeping establishing shots or touching montages.” While TV ads hinge on emotional appeal, she says, “video app ads should get to the app experience as quickly as possible.”
Storytelling and buildup are less important in-app ads, except for highly complex or sensitive products, such as finance or healthcare apps.
Try tightly-framed shots that help viewers focus on features they can use in-app. Streamline storylines and zero in on your app’s capabilities and benefits. Given how short app ads are, the best way to do this is the old “show versus tell” approach. Showing the app’s capabilities and benefits often work better than telling viewers what they’ll get.
2. “Be brief, be brilliant, be gone”
Brevity is the soul of wit – and engagement and action. This is especially true for video app ads. So keep your ads between 15 to 30 seconds, and consider testing even shorter lengths.
Why so short? Because mobile users have short attention spans.
We’ve found that ads are the most effective if they can grab viewers’ attention within the first 2-3 seconds. One way to make that very brief window work is to introduce a few quick cuts early to deliver a “moving” sense of the app experience.
3. Don’t forget audio.
Want to add a whole new level of depth and meaning to your video ads? Then crank up the audio. It’s an easy way to create another connection with users.
Audio captures attention, but it also gives you another tool to help you compress a lot of meaning, action, and fascination into the fleeting seconds your ads will run. We particularly like adding an audio punch to enhance branding and calls to action.
4. Brand early; brand often.
We’ve had success with using branding as part of apps UI (in shots), on the sides (in vertical ads), and through audio cues. Or try a combination of all three. If your brand has strong characters, use them in your ads, too.
5. Diversify.
You can get dramatically better results if you upload videos with multiple aspect ratios. Is this more work? Yes – and it’s worth it.
At a minimum, create versions of your videos in landscape (16:9), portrait (2:3), and square (1:1). Also, vary video lengths to maximize ad reach across Google channels. We like to have at least one version that is 10 seconds long, another 15 seconds long, and yet another version that’s 30 seconds long. Then let the algorithm figure out where and to whom to show the different ads.
6. Test. Test again. And then test your video app ads more.
We’ve tested tens of thousands of video app ads. As mentioned earlier, only about one in twenty will perform well enough to beat an account’s current control. True winning, game-changing ads take work.
To accelerate this process of testing and to minimize the costs of it, we’ve developed a creative testing methodology we call Quantitative Creative Testing. Quantitative Creative Testing starts with analyzing your competitors’ video ads so you can see what’s working for them. It will also spark your own ideas for new creative concepts.
Those new concepts then get tested as efficiently as possible in order to find big wins. Then we test dozens of variations on the most successful new concepts to fine-tune the ads for optimal performance.
Once we’ve found the unicorn ad, we’ll very carefully roll it out to specific audiences in a way that maximizes reach and lifespan. Then we start the creative development process all over again. Our brand new ad won’t perform well forever.
7. Keep your video app ads fresh.
Last but not least – ad fatigue happens. And the larger an audience your ads reach, the faster it will happen.
You have to regularly develop and test new creative to stay ahead. Even smaller advertisers need to refresh their videos every 2-3 months. Large advertisers often need refreshed creative every other week or more.
To minimize fluctuations in performance, gradually rotate low-performers out in favor of new content. Don’t just cut the old ads and launch new ones all at once.
How Consumer Acquisition Can Help Your Video App Ads
Not sure exactly how to apply these video app ad best practices to your campaigns? Don’t have enough time to make your successful ads into three different aspect ratios, much less three different lengths (3 x 3 = 9!). Don’t know how you’re going to tease more creative out of your already exhausted creative team?
We can help. Consumer Acquisition is a Google App Preferred Creative Partner. We develop best-in-class videos and creative assets for app campaigns, as well as for lead gen and user acquisition programs. See what we can do for you.
Read Our Whitepapers
Creative & UA Best Practices For Facebook, Google, TikTok & Snap ads.
