Author: Lian

This is the second in our two-part Facebook user acquisition series. Consider this our Facebook UA Master Class! We are providing our advanced UA techniques and best practices to achieve scale and profitability.  If you missed our first post on how to profitable scale Facebook UA – here ya go!

User acquisition has been a numbers game for a long time. Back in the old days, like 2017, the numbers were largely managed by people. UA managers would edit bids, budgets, and placements and tweak audience targeting numerous times a day to game the algorithm to maximize return on advertising spend (ROAS).

All that changed in February 2018. Almost overnight, Google took back all control all at once at first. Then dolled a little bit back to us. Facebook took a different approach, starting small, and has been incrementally nudging us all toward full automation.

This has had a profound effect on mobile app advertising and user acquisition managers. We have far fewer levers left to outperform the AI algorithms than we used to have. And yes, the automation has simplified account management for budgets less than $300,000 per month.

But while some things are being taken away, other opportunities are opening. Creative strategy and manipulating lookalike audiences have become the primary drivers of ROAS.

And, as advertisers grow their business and exceed $300,000 per month, we have found that advanced UA techniques change quite drastically.  Here we provide our best practices to achieve both UA scale and profitability.  We like to call this our “UA Masterclass”.

Advanced Testing Strategies When Resources are Limited

The best way to improve performance is to test new media buying ideas and structures. Also, you should be running multiple tests simultaneously. Some tests can be launched immediately, where other tests (like producing new video concepts) require time and creative resources.

Here is a list of items you can test right now, without waiting for help from anyone else:

Creative

Text, headlines, and calls-to-action can be tested immediately without creative resources.

Text

The text has a strong impact on performance and should be tested regularly.

Headlines

Headlines have an impact on performance, but the impact is not as strong as the impact of the text. They should be tested at a 1:3 ratio vs text. This means we should test 10 headlines for every 30 text variations that are tested.

CTA

Call-to-action buttons are predefined by Facebook and are limited in options that are appropriate to test periodically for each client. CTA should be tested early for each client, but infrequently once a clear winner is established.

Audiences

Audience testing has a significant impact on performance and there are always audience tests that we can run.

Custom Audiences

By creating new custom audiences, we can reach net new users through retargeting and acquisition.

• For retargeting
• For lookalike creation

Lookalike Audiences

There are many ways to create new lookalike audiences.

• Test stacks of lookalikes
• Expand into a high volume of seeds
• Test country-specific vs. worldwide
• Expand into a broader affinity %
• Test nested vs. not nested

Interest Groups

Interest groups commonly perform worse than lookalike audiences but should be tested for all clients because some interest groups perform well.

• Competitors
• Industry
• Random
• Aiming for demographics

Behaviors/Job Titles

Like interest groups, Facebook allows for targeting certain behaviors and job titles.  These also generally perform worse than LAL audiences but should be tested.

Broad Targeting

Facebook generally charges a lower CPM for larger audiences. So running ads with no interest groups or lookalike audiences can drive down costs.

• No interest groups or lookalikes

Demographics

Facebook generally charges a lower CPM for larger audiences. As a result, running ads with broader age ranges and both genders can outperform ads that are more narrowly targeted.

• Gender
• Age

Device Type

Depending on the client, targeting newer vs. older hardware and software could boost performance due to the technological requirements one app may have over the other.

• OS version
• Device
• Device type (phone, tablet, iPod)

Geographies

Countries that run in multiple countries, there are many ways to target locations and performance varies by client.

• Worldwide with/without various country exclusions
• Individual countries
• Groups of countries
• Facebook country groupings
• Continents
• Country tiers

Facebook Placements

Currently, they can subjectively whitelist platform-specific targeting for mobile gaming clients.

• Instagram only
• FAN only
• Messenger only
• Facebook Feed only

Sample language and country targeting test for a gaming client:

Writing Ad Copy for ROI

Ad copy has a significant performance impact. It is also a quick process, so the ROI from time spent is often very strong. Effective ad copy generally tells the story clearly and succinctly. But many different writing styles are effective. When writing ad copy, it is helpful to first outline the themes of ad copy you want to test. Then write variations for each theme with different writing styles. By testing a variety of themes, and then testing a variety of variations of writing styles for each theme, you can quickly learn which themes perform best before optimizing messaging within a theme.

Themes

To identify appropriate themes to test, we can look at the messaging in the conversion funnel (landing pages, app store pages) for inspiration. There are some themes that can generally be applied to all clients, and some themes that will be client-specific. Below is a list of themes that can be generally applied to most clients.

Features

• Gameplay
• How it works

Benefits

• Why use this product?
• Why play this game?

Promotions

• Discounts
• Special events
• Welcome bonuses

U.D.: fear / uncertainty / doubt

• Do not miss out (FoMo)
• Do not take the risk

Specific for a video or image

• Not all ad copy is appropriate for all videos and images. Some images or videos will carry their own themes.

Buzz Words

• Keywords from landing page headlines
• Keywords from the first 100 words of app store descriptions
• Words that appear on app store images or in-app store videos
• Relax (this is a common theme for anyone playing casual games)
• “Best” game ever (within the genre, i.e. “this is the best solitaire game ever!”)

Challenges

• “Only 1% of users can beat this game”
• “Me vs. my grandma/mom/boyfriend/girlfriend/etc.”
• “Can you do better?”

Testimonials

• Taking quotes from actual 5-star reviews and quoting positive reviews for the game
• Fake testimonials and/or fake quotes (if the client is ok with this)

Emojis

• Leveraging popular and/or relevant app genre emojis

“Stacked”

• “Stacking” long ad copy (multiple lines) while mixing in emojis and/or challenges, questions, benefits

 

Variations

For each theme, we should test multiple variations of ad copy, using various writing styles, and spinning out slight variations. The best writing style is dependent on the client/audience, so many styles should be tested for each client.

Writing Styles

We can use one or many ad copy styles for each ad copy variation. These are common styles that perform well.

• Short copy (a few words up to a couple of sentences)
• Long copy (paragraphs)
• Ask questions
• Use emoji
• Use bullet points (with hyphens or emojis)

Slight Variations

• Change 1 word
• Change the order of words/phrases

 

Next Level Custom and Lookalike Audience Creation

Building custom and lookalike audiences are a constant part of audience expansion. By reaching net new users through audience expansion, you can significantly improve CPA / ROAS. Outside of creative testing, this is the most common method of significantly improving CPA / ROAS.

Events

There is no limit to the volume of custom and lookalike audiences you can create. You can create custom audiences based on different events like app starts, purchases, tutorial completions, revenue, etc. For example, for each event you can:

• Create custom audiences based on the top 1% of users, the top 10% of users, the top 25% of users, etc.
• Create different custom audiences for users in the past 7 days, past 30 days, past 60 days, etc.
• For each event and time range, target a different location like worldwide or the United States
• For each custom audience, create lookalikes that are top 1% affinity, top 2% affinity, top 3% affinity, etc.

We see strong performance when creating a highly diverse set of custom audiences and then targeting the top 1% – 3% affinity across the various audiences. For audiences with an overlap above 40%, it can be beneficial to group them in a single ad set, creating a “lookalike stack”. However, for accounts spending > $300,000 per month, the concern with overlapping audiences seems lower. For lookalike audiences without high overlap, they should be tested as individual audiences as well as lookalike stacks.

Location

For the location, worldwide audiences generally perform well whether we’re targeting individual countries, groups of countries or worldwide. Country-specific audiences generally perform well for the countries they were built from, but generally do not perform well when targeting other countries or worldwide.

Value-based audiences work well (i.e. top 25% of purchasers), and you can manipulate purchase values to create different lookalike audiences. The theory is that Facebook has an easier time finding whales when there’s a greater variance between the highest-value and lowest-value purchasers. We manipulate audiences by increasing revenue for top payers, and by decreasing revenue values for low-value payers. Below is an example of 12 different custom audiences that were generated with various revenue increases and decreases for high-value and low-value payers.

Manipulated Audience Values:

You can also create additional types of audiences through Facebook Analytics.  This can be useful for 1) new audiences to test that may not be available through other tools and 2) analytics to gain insights into FB-specific traffic quality.

 

Improving CPA and/or ROAS without reducing spend

The simplest way to improve CPA and/or ROAS is to reduce daily spend, as we generally see a correlation between lower daily spend and stronger CPA / ROAS. However, UA teams are generally tasked with improving CPA / ROAS without reducing spend, and the most common ways to do this are by producing new winners through creative testing, audience expansion, changes to targeting, and optimization techniques.

Creative Testing

Testing entirely new concepts with a different look and feel can improve ROAS massively.

Audience Expansion

By reaching net new users from audience expansion, we can significantly improve performance. Outside of creative testing, this is the most common method of improving KPIs. In today’s market, lookalike audiences that are generated from custom audiences commonly outperform interest groups and usually broad targeting (IAP games), and there is no limit to the volume of lookalike audiences we can create.

Audience Expansion Through FB Analytics

In addition to leveraging Facebook Analytics to gather app insights, Facebook also allows for the creation of “non-standard” audiences through Facebook Analytics.  One example of this can be seen through the creation of “rule-based” audiences.  Rule-based audiences can be more defined than standard audiences due to the specific actions one can target on the FB Analytics platform.  The following example shows data for iOS users who launched this app more than 20 times and made a purchase within the last 28 days.

Changes To Targeting

Changes to age, gender, location, placements, and devices can all have positive impacts on CPA / ROAS, and targeting tests should be run early so that future creative testing uses efficient targeting.

Campaign Structure and Optimization Techniques

Facebook has rolled out a variety of new products over the past couple of years. The performance of these products is often inconsistent. Due to performance variance, these items should be tested periodically, but we can generally assume that best practices, on average, should be used as a starting point.

AEO vs VO

App event optimization is used to optimize for the lowest cost per app event, where value optimization is used to optimize for the highest revenue per event. Value optimization generally carries both a higher CPA and a higher ROAS than app event optimization, as Facebook is effective at identifying “whales” (high-revenue purchasers) through value optimization targeting.

Conversion Windows

Currently, Facebook offers advertisers the option to optimize toward a 1 day or 7-day post-click conversion window for AEO and VO campaigns. Generally, 1-day conversion windows net out higher ROAS with higher costs, while 7-day conversion windows typically bring in lower ROAS with higher volume. However, it is important to test both options occasionally to validate account-specific performance.

Min ROAS (VO)

Available only for VO. Min ROAS bids allow the advertiser to “bid” preferred ROAS percentages, according to the selected conversion window selected during adset setup. Bidding here is slightly different as the advertiser is choosing the preferred return on spend instead of “cost per X”. Generally, it is recommended to cast a wide net of bids on each conversion window to optimize toward a “sweet spot” of quality and volume.

DLO

Dynamic language optimization allows us to input ads for various languages in the same ad, and Facebook will dynamically serve ads with the appropriate language to the appropriate audience.

CBO

Campaign budget optimization allows us to use a single campaign budget that governs all ad sets within a campaign. When using a single CBO campaign budget, Facebook then adjusts budgets for each ad set and shifts more spend to ad sets with better performance. This is different from non-CBO where each individual ad set has its own budget that is managed separately.

DCO

Dynamic creative optimization allows us to input multiple variations of each creative element (video, text, headline, etc.) and Facebook will automatically randomize the creative combinations and begin serving more impressions to the creative combinations that perform best.

 

Optimization Techniques

 

Pausing

By pausing underperforming ads, we can shift spend to stronger ads and portfolio CPA / ROAS will generally improve. To offset volume losses by pausing underperforming ads, we need to either increase budgets for active ads or launch new ads.

Budget Management

By adjusting budgets to reduce spend from underperforming ads and shift spend to top-performing ads, we can balance a portfolio of ads to achieve better results. With each budget adjustment, a significant edit is triggered, and the learning phase is reset, so there is some risk to increasing budgets for top ad sets. Making smaller changes in absolute dollars and percentages will help limit the negative impact of triggering a significant edit.

Bid Management

For ads that are using manual bids, we can change bids to improve CPA / ROAS. Bids tend to alter volume more than CPA; for instance, a bid decrease from $12 to $10 may cause spend to fall by 50% where CPA only falls by 10%. We generally recommend running higher bids, to gain access to the highest-quality inventory, and then using budgets to control CPA / ROAS. With a high bid in place, we would expect lower budgets to carry better CPA / ROAS than higher budgets. It’s counter-intuitive, but low bids could reduce access to high-quality impressions and actually hurt CPA / ROAS.

 

Scaling Spend and Maintaining Performance at High Scale

There is a general rule that CPA will increase and/or ROAS will decrease as we scale spend on Facebook. This is true both for aggregate spend over time (audiences get saturated and creative fatigues as overall spend increases), and for scaling spend overnight (Facebook often reaches into lower-quality inventory to fulfill inventory for incremental budgets). We are able to gain efficiencies vs. the market by taking intelligent approaches to scale spend with the goal of protecting CPA / ROAS.

Scaling Spend Overnight

The two most common methods of scaling spend overnight are increasing budgets for existing ads and launching new ads.

Increasing Budgets for Existing Ads

When increasing budgets for existing ads, there are two primary reasons why CPA increases / ROAS decreases. The first reason is that anytime a budget is adjusted, a “significant edit” is triggered and a significant edit causes ads to go into the “learning phase.” When an ad is stuck in the learning phase, backend history is reset, and the ad faces temporary volatility as backend history builds back up. Because of this, our goal is to limit the frequency and volume of significant edits. The second reason is that Facebook will reach into lower-quality inventory to fulfill incremental budgets, so the quality of users generally drops as individual ad set or campaign budgets are increased. We generally see better performance when running a higher volume of ad sets at a lower average budget per ad set.

Optimizing for Significant Edits

While making significant edits can cause performance volatility, it’s a necessary part of scaling and it’s OK to make significant edits as long as you limit the frequency of significant edits and analyzing the impact. We often need to decide whether to make a significant edit for a top ad vs.increasing volume by launching new ads, and the best approach can vary depending on the account. For instance, if we have a relatively low volume of ads that are responsible for most of the account’s strong performance, we may decide not to edit these ads (to protect their performance) and instead focus on launching new ads to capture more volume.

Or, if there is a high volume of ads performing well, there’s less risk to portfolio performance by triggering a significant edit for a single ad, so we would be more willing to trigger a significant edit for a top ad since the risk is lower. When taking a significant edit, the best practice is to not change budgets or bids more than 30% at one time.

Launching New Ads

New ads begin in the learning phase and carry a higher CPM than mature ads, so launching a high-volume of new ads can hurt performance. Launching new ads generally carries more risk than editing existing ads, but the rewards can be much greater for launching new ads versus editing existing ads. For instance, if new ad launches are focused on expanding into new audiences and reaching net-new users, we may see CPA / ROAS improve in aggregate for new ads. Or, if new ad launches are focused on creative testing and we produce a winner, then CPA / ROAS may improve in aggregate for new ads. In general, new ad launches should be focused on doing something different, and most commonly this would be different audiences, different creative, different targeting, or different campaign structure/budget/bid strategies.

Maintaining performance at a high scale has different challenges than scaling spend overnight, but the optimization techniques are similar. Creative fatigue and audience saturation are the main drivers of performance degradation as advertiser spend increases, and these challenges are more pronounced at a high scale than when increasing spend overnight.

Creative Fatigue

Users are repeatedly seeing the same creative over time and click-through rates / general performance declines as the frequency of impressions per user increases. It occurs at a faster rate when spend is higher, and when we have fewer high-performing creative assets. For instance, if we spend $1,000,000 with 3 high-performing video concepts for client A and we spend $1,000,000 with 6 high-performing video concepts for client B, client A’s creative will fatigue roughly twice as fast as client B. Because the impression volume for each of client A’s creative concepts will be double the impressions of creative concepts for client B.

There is a benefit to having a higher volume of high-performing creative and this means that we need to increase the frequency and volume of creative testing as we increase spend. Clients that spend $1,000,000 per month need roughly 10X the creative testing as a client at $100,000 per month, to maintain the same rates of creative fatigue.

Audience Saturation

Users are more likely to click when they see an ad for a product for the first time. So performance is stronger the first time we show ads to a new audience. As the frequency of impressions increases for an audience, users become less likely to click. An increased frequency of impressions causes an audience’s performance to decline. And simultaneously the highest-value users are effectively being removed from our audiences as they convert. So there are multiple reasons why performance drops as audience impression frequency increases.

We combat the performance degradation from audience saturation by continually testing new audiences that are designed to reach net-new users that have not seen our ads. Similar to the benefit of having a higher-volume of strong performing creative assets, we see a slower rate of fatigue for clients that have a higher volume of high-performing audiences. With twice as many high-performing audiences, we would expect the performance degradation to occur at roughly 50% the rate, depending on the amount of overlap that exists between audiences.

 

Diagnosing Performance Fluctuations

With Facebook advertising, the only constant is change. Performance commonly fluctuates as creative fatigues, audiences saturate, marketplace conditions change, and Facebook updates algorithms. When we notice an account’s performance fluctuating, the next step is to determine why performance is fluctuating. While each performance fluctuation is unique, there are four common questions we can ask in the process of attempting to diagnose the cause of performance fluctuation:

What changes did we make that could have caused volatility?

The common changes that drive significant fluctuation are new ad launches and major shifts to traffic from pausing ads or adjusting budgets. To easily identify whether new ad builds are the cause of volatility, we can view ad build performance in advanced reporting and then filter out recent ad builds to determine if performance was “normal” if we ignore the recent ad launches. Outside of understanding the impact of recent ad builds, we can compare different date ranges in our reports for any object to identify major shifts in traffic allocation. For instance, we can compare video performance for yesterday vs. two days ago to quickly determine whether any major shifts in traffic distribution by video have occurred.

What changes occurred within traffic distribution?

If we can’t identify any major shifts in traffic that we caused, the next step is to identify if any shifts in traffic were caused by changes outside of our control. For instance, our top ads may become disapproved and stop spending, which could reduce the volume and hurt performance. The common shifts in contribution % that significantly impact performance are shifts by ad, by creative, and by audience. It’s helpful to use the date comparison function in advanced reporting to compare traffic contribution % over different time periods for ads, creative, audiences, and demographics.

Assuming we can’t point to any major shifts in traffic allocation within Facebook, were there any product or tracking changes on the client-side?

If we can’t find any major shifts in Facebook traffic distribution, and performance is fluctuating for all / most high-volume ads, then we need to understand if anything changed with a client’s product (app or conversion funnel) or if any changes to tracking were made. With apps, we can see the version history in the app store or sites like App Annie. We can determine if any releases correlate with performance fluctuations. There could be changes to an app’s tracking that are not made through an app store release, so app store releases are not a definitive answer for whether changes were made, so it’s appropriate to ask a client if anything changed even if we don’t see any releases that correlate with performance fluctuation.

For web clients, the first step should be to visit the destination URL. Look for any obvious changes to the site or to the Facebook pixel. The Chrome browser extension for Facebook pixel help is very helpful. If we notice app store releases or changes to a website, then we should discuss the changes with the client and determine whether they are the cause of performance fluctuation. Comparing Facebook performance to performance across other high-volume traffic sources can help identify if the issue is global and appears to be caused by a product change.

Assuming we can’t point to any changes on Facebook or the product, performance fluctuation may be caused by macro events that are outside of our control.

If we’re unable to identify shifts in Facebook traffic or a client’s product, then we may be dealing with macro events that are outside of our control. For instance, competition on Facebook may shift end of the month, end of the quarter, and end of the year. We also see major shifts in performance around national holidays and seasonal events like summer when school is out and human behavior changes.

There are also events like the start of NFL season when daily fantasy sports companies disrupt the Facebook marketplace by increasing aggressiveness. We can also look across our portfolio to determine if the fluctuation is client-specific or global. If we see performance fluctuation with most / all clients during the same time frame, then we will have higher confidence that we’re not in control of the fluctuation.

Lastly, the release of competitor apps could also play a large factor in diagnosing performance fluctuations. If we have completed a thorough analysis of Facebook changes and product changes, and we still can’t explain the performance fluctuation, then we need to work with clients to determine whether we should temporarily reduce spend until fluctuation normalizes. Or, determine if we should increase testing in an effort to produce a win that will offset the fluctuation. In most cases, it makes sense to temporarily reduce spend because new ads that are launched during volatile marketplaces often perform poorly, regardless of creative and audiences.

 

Final Thoughts about Advanced UA Techniques

 Artificial intelligence will play a key role (if not THE role) in the future of user acquisition. Even now, AI can run many key components of UA more effectively and more efficiently than humans can.  We recommend UA managers plan to pivot into grow analytics, creative strategy, audience expansion, and A/B testing if they want to keep their jobs.

This can be an exciting shift into UA 2.0 if you’re agile enough to keep pace with all the changes.

It is also an opportunity to elevate your level of advanced UA techniques to become UA masters.

July 2020: How do we profitably scale Facebook user acquisition for mobile apps?  In our two-part series, we’re going to provide a full and detailed explanation for how to achieve ROAS at a sustained scale. For some context on where Facebook user acquisition is today, it’s important to note the major strides made in 2019 and 2020 towards simplifying and streamlining its ad platform. This is overwhelmingly a good thing because it does the following:

• Allows more people to effectively use the platforms, regardless of their advertising skills
• It saves UA managers’ valuable and very limited time
• Gets more consistent results

And, while Facebook has automated some levers, other opportunities are opening up. Creative strategy, development, and efficient A/B testing are now primary drivers of ROAS and scale and they are still best done by humans.  This is where we come in as a trusted advisor in Facebook UA, both in creative and media buying strategy because automation has affected UA performance and management, and what UA managers should do to evolve into this very new environment. It’s an exciting time to be in user acquisition, but it demands a great deal of agility.

Scale Facebook User Acquisition (Part I)

When we start working with a new client or creating a new media account, we always begin with a full audit of both the creatives and the media buying account configuration. This includes:

Creative Audit

We review what is a client’s best and worst-performing creative and why. The goal is not to replicate ideas that have won or failed but to go into fresh directions to explore new areas. We always begin with a robust review of competitor ads. Pablo Picasso said it best. He said, “A good artist will borrow but a great artist will steal.” Your competitors are failing at the same rate, between 85% to 95%. That means a vast majority of all new concepts fail to outperform the best creative in a portfolio. If you can’t outperform your best ad, you lose money running it. However, if you can incorporate your competitor’s best concepts and creative trends, it will give you an endless supply of concepts that they’ve tested.

• Special Offer: See your competitors’ top video creative and understand which ads drive their performance. We’re giving away FREE access to over 1 million competitive videos.

We then do a complete review of your assets to understand how easy or complex it will be to take the pieces and recombine them into editing software to create new concepts. 

If you have done market segmentation analysis and produced player profiles, we’ll use that information to refine our calls to action to appeal to your best audiences.

If you are looking for a collaborative approach, we offer a premium service called “Collaborative Creative.” Here, we put together a strategic creative plan with mini briefs that contain concept hypotheses and motivations and then walk you through the document for feedback.

Get killer Facebook creative! Facebook & Google AI are automating media buying making creative the main driver of profitable UA.  In our creative inspiration whitepaper, we layout hundreds of examples of why Creative Trends, Competitive Analysis, Player Profiles, and Creative Testing are critical for UA success. We have developed a new approach to creative testing that solves the adage of “why the control always seems to win” through extensive research, which we reveal in this guide to creative trends for mobile game advertisers.

Check out our other articles on Facebook Creative:

• Creative is King
• Creative Trends For Mobile Gaming
• Player profiles, how do you use for creative ideation
• The Definitive Guide to Facebook Creative Testing

 

Media Buying Audit

Review KPIs (Key Performance Indicators) and Lifetime Data, verify if the client is achieving KPIs. If not, how far off goal.

Does the client have an MMP (Mobile Measurement Partner)? If so, does Facebook data align with their first-party data? If not, how much is it off?

Review creative, campaign, and audience performance – review which components are achieving or near KPI.

Review top-performing campaigns, audiences, and creative and highlight the top performers. What works and why?

Verify if the client has tested CBO (Campaign Budget Optimization) vs Non-CBO campaigns – confirm the performance of each.

• CBO Campaigns allow Facebook’s algorithm to split out a set budget between the different ad sets instead of manually inputting these budgets at the ad set level.

Verify if running DLO ads – confirm performance.  Do particular languages monetize better and how does that map to their geography targeting?

• DLO (Dynamic Language Optimization) allows multiple languages in one ad unit which Facebook dynamically serves users based on their indicated language. 

Review bid types to determine what is working, including VO, MinROAS, AEO, MAI, etc.

• AEO (App Event Optimization): Instructs Facebook to optimize for users most likely to commit the indicated event (Examples could be: level achieved, add to cart, registration completed, purchase). 

• VO (Value Optimization): Tells Facebook to optimize towards users that are most likely to purchase at a great amount over a longer period of time. Typically used for highest LTV (Lifetime Value).

• MinROAS: A function of VO, instructs Facebook to optimize towards users who are likely to generate a specified Return on Ad Spend within a specified timeframe.

• MAI (Mobile App Install): Tells Facebook to optimize towards users that are most likely to install the app.

Review performance by media type and determine if videos or static images, carousels, DCO (Dynamic Creative Optimization) are performing on the account.

Review operating system performance (Android vs iOS).

Uncover new opportunities where the client is under testing or not testing at all.

• Facebook best practices (Structure for Scale or Power 5)
• Ad Copy Headline & Ad text
• Ad set structure testing (one ad per ad set, multiple ads per ad set)
• Validate creative testing process. How much does it deviate from our best practices
  • Video: Funny Facebook Explainer Video On Creative Testing
  • Whitepaper: Creative Testing: Why Is The Control So Hard To Beat?

Prepare to normalize account structures with a balance between Facebook’s best practices of Structure for Scale (S4S) / Power 5 and our proven methodologies to achieve both scale and ROAS.

• Structure For Scale’s main strategy is to streamline/minimize the number of campaigns and ad sets targeting wider reach audiences to allow the algorithm to most efficiently drive ROAS and scale or other desired outcomes. 

• Concentrating spend on fewer adsets allows Facebook to quickly accumulate events and exit the learning phase.

• Maximize audience reach so Facebook algorithms find the most qualified users while minimizing the audience overlap. 

• We minimize changes to campaign/adset settings to avoid a “significant edit” and returning to the learning phase. We’ll often launch a new campaign with the desired changes to avoid affecting the original campaign. 

• We tend to follow 4 out of the 5 “Power of Five” Best Practices and those would be
  • Auto Advanced Matching (typically set by the client if they want to sync customer data)
  • Account Simplification / Structure For Scale
  • Campaign Budget Optimization
  • Automatic Placements (Allows Facebook to choose where the ad will most efficiently be displayed across their ad networks)
  • Dynamic Ads – Infrequently used, but powerful for personalized product retargeting campaigns primarily for e-commerce clients.

Note, if there is not a lot of campaign data or it’s a brand new account with no history, typically we kick off with MAI and AEO campaigns to determine audience performance and creative performance.

• Once top-performing audience groups and creatives are determined we move into VO bidding and MinROAS bidding.

• Once bid strategies are determined, we start to review the performance of different demographics:
  • Age/Gender
  • Geos
  • OS Version

• We will test CBO vs Non-CBO if we haven’t in the initial phase.

• We’ll test DLO on top-performing audiences.

• For MinROAS bidding, we start testing out different bidding levels to determine the top performer.

• We will test new audiences based on top-performing audiences (higher lookalike %, similar lookalike events, etc.).

The Process of Media Buying

The simplest way to improve performance is to reduce daily spend. We generally see a correlation between lower daily spend and stronger ROAS. However, we’re generally tasked with improving ROAS without reducing spend, and the most common ways to do this are by producing new winners through creative testing (described above), audience expansion, changes to targeting, and optimization techniques.

Starting Point

To start, we use the client’s strongest elements (videos, images, ad copy, audiences) to establish a baseline performance while using our preferred campaign structure.  While benchmarking, we write a new copy and our creative studio will begin ideation following the above creative process.

Our Preferred Audience Structure for Scale:

Testing Client’s Available Geos. Usually WW, T1, and the US on Broad, Interest Groups and Lookalike Audiences.

Lookalike Audiences

We initially test narrower (higher quality) 1%, 3%, 5% audiences, analyze performance and then expand to wider (less expensive) 10%, 15%, 20% audiences in an effort to balance cost vs. Return on Ad Spend:

• Lookalike audiences can range from 1-20% (Typically use 1,3,5,7,10,12,15,20%) 
• Lookalike audiences are based on seed audiences of spend (value) or events committed that drive client KPIs (monetization, retention, LTV)
• Seed Audience Examples
  • Purchase
  • Registration
  • Purchase greater than certain $ amount
  • Top 1% Purchasers
  • Most Active users
  • Top 10% Users
  • Most App Launch Users
  • Users who have reached a particular milestone
• We create “MegaStacks”, a group of lookalikes that consist of similar lookalike audiences in the same percentage range. This allows us to create an expanded audience that is similar in intent. This can include:
  • Similar audiences (purchases vs top purchasers vs purchases>9.99) 
  • Different lookback windows (7D,30D,90D, etc.)
  • Different Geos (if the audience is WW) 
• Early Whales, use a revenue value that is relevant to the particular game. Then create lists of users that meet those criteria. Values below are placeholders but the idea is that the highest amount (in this case $10) may not be achievable for 1 day or even 2-day users but only 7-day users.  Once these are built, they are uploaded to Facebook for lookalike audience creation.
• Ex: 
  • On day 1 all users with at least $2 of revenue
  • Day 2 all users with at least $2 of revenue
  • By day 7 all users with at least $2 of revenue
• Increase $ amounts
  • On day 1 all users with at least $5 of revenue
  • Day 2 all users with at least $5 of revenue
  • By day 7 all users with at least $5 of revenue
• Then:
  • On day 1 all users with at least $10 of revenue
  • Day 2 all users with at least $10 of revenue
  • By day 7 all users with at least $10 of revenue

Value-Based Manipulated Audiences

The client generates a list of users sorted by revenue that is then manipulated to go much higher and much lower based on their place along with the average. This creates a profile of users who are “high value” and “low value” for Facebook.

• Interest Groups – programmatically generated groupings of Facebook interest categories, games, products, pages, etc.
• Broad Targeting – Unrestricted targeting of all users in the geographic area. This allows Facebook the most reach in identifying quality users but maybe too wide to control costs.
• Testing AEO and VO against the above audiences to determine which bidding strategy produces the best results based on client KPIs.
• VO+MinROAS. We follow a set of best practices
  • Start with 1% bids (unless the client has a very high ROAS goal) or a range of bids
  • Adjust the bid higher or lower depending on audience performance
  • Increase the bid, if we see that quality is too low
  • Decrease the bid, if we see that scale is too low
  • If performance is very high, decrease the bid to increase the scale
• Our preferred audience/campaign structure allows us to quickly determine which Geos/Audiences and Bidding optimizations will achieve client goals from both a cost and scalability perspective. 
• Our structure delivers more precise results with fewer variables within each campaign. Other agencies/media buyers may change bid strategy or audiences on the fly within campaigns as a quick fix, where we split out variables to identify true performance. 
  • Split audiences based on similar events (purchases, top purchases) 
  • Separate broad and interest campaigns
  • Divide out campaigns from VO/AEO/MinROAS/MAI
  • Split out different country targeting
  • Separate out different conversion window targeting
  • All so that we can understand specifically what causes a campaign to perform well

Tips for Additional Audience Expansion

By reaching net new users from audience expansion, we are able to significantly improve performance. Outside of creative testing, this is the most common method of improving KPIs. In today’s market, lookalike audiences that are generated from custom audiences commonly outperform interest groups and usually broad targeting (IAP games), and there is no limit to the volume of lookalike audiences we can create. For instance, we can create custom and lookalike audiences based on different events like app starts, purchases, tutorial completions, and revenue, etc.

Also, for each event, we can create custom and lookalike audiences based on the top 1% of users, the top 10% of users, the top 25% of users, etc. In addition, for each custom audience, we can create different lookalikes for users in the past 7 days, past 30 days, past 60 days, etc. And finally, for each custom audience, we can create lookalikes that are top 1% affinity, top 2% affinity, top 3% affinity, etc. We see strong performance when creating a highly diverse set of custom audiences and then targeting the top 1% affinity across associated lookalike audiences.

Audience Expansion Through FB Analytics

In addition to leveraging Facebook Analytics to gather app insights, Facebook also allows for the creation of “non-standard” audiences through Facebook Analytics.  One example of this can be seen through the creation of “rule-based” audiences.  Rule-based audiences can be more defined than standard audiences. This is due to the specific actions one can target on the FB Analytics platform.  The following example shows data for iOS users who launched this app more than 20 times. Also, those users who made a purchase within the last 28 days.  Currently, AdRules does not support all of the options listed in Facebook Analytics. So there could be large performance boosts by getting creative with these types of audiences.

Then, we optimize the media buying account for its monetization strategy – based on Ads (IAA) vs Purchases (IAP):

IAA

(IAA) apps monetize with in-app ads. Generally, the longer a user remains in the game, the more revenue (ad views) they generate. The goal is to find high retention users for the lowest acquisition cost possible. Targeting is designed to be low cost / low CPM – usually App Install optimization with broad, wide lookalikes (10%-20%) and interest groups. Basic App Install campaigns lack optimization levers (AEO, VO, etc), we instead optimize on top-performing age, gender, geo, language, device/Android/iOS, and platform placement (IG, FB, FAN, etc).

IAP

(IAP) apps monetize with in-app purchases. The goal is to acquire high-ROAS users, typically achieved with AEO & VO optimizations. Typically for IAP campaigns, we seek high ROAS campaigns that are driven by AEO and VO bidding, tied to lookalike campaigns.

Testing Structure For Scale Campaigns

• We kick off campaign creation by testing broad campaigns and lookalike campaigns for initial testing
  • Initially, if there is enough data we tend to test AEO and VO against each other to see a better performer
  • If we start to see strong performance in VO we start testing MinROAS Bidding
• We kick off testing with WW and US campaigns. Typically this is because the US has always been a consistent performer, and WW campaigns give us data on the other countries for further testing.
• As we continue to run campaigns and identify top-performing countries, we create lookalikes for those specific countries and test them against worldwide.

Testing Structure For Scale Optimization

• Once we get a winning bidding strategy and audience, we test different levers of optimization like:
  • DLO vs Non-DLO
  • CBO vs Non-CBO
  • Multiple Ads per Ad Set vs One Ad Per Ad Set
  • Different MinROAS Bidding Levels
  • D1 Conversion Window vs D7 Conversion Window 
• We also review different breakdowns to determine if top-performing breakdowns could be specifically targeted on a new campaign. These breakdowns include:
  • Age
  • Gender
  • Geographies 
  • OS Version
  • Publisher Platform
• While we are testing different campaign builds and audiences, we are consistently putting new creative through Phase1, Phase 2, and Phase 3 testing. Once we have determined winners we introduce these ads into our top-performing campaigns to determine their performance against control creative. 

Fluctuations in ROAS and Scale

You’ve tried everything but are still having issues! Check out how we diagnose performance fluctuations.

With Facebook advertising, the only constant is change. Performance commonly fluctuates as creative fatigues, audiences saturate, marketplace conditions change, and Facebook updates algorithms. When we notice an account’s performance fluctuating, the next step is to determine why performance is fluctuating.

While each performance fluctuation is unique, there are four common questions we can ask in the process of attempting to diagnose the cause of performance fluctuation:

1. What changes did we make that could have caused volatility?

When performance fluctuates, the first question we need to answer is whether we made any changes that would cause performance fluctuation. The common changes that drive significant fluctuation are new ad launches and major shifts to traffic from pausing ads or adjusting budgets.

To easily identify whether new ad builds are the cause of volatility, we can view ad build performance in advanced reporting and then filter out recent ad builds to determine if performance was “normal” if we ignore the recent ad launches. Outside of understanding the impact of recent ad builds, we can compare different date ranges in our reports for any object to identify major shifts in traffic allocation. For instance, we can compare video performance for yesterday vs. two days ago to quickly determine whether any major shifts in traffic distribution by video have occurred. 

2. What changes occurred within traffic distribution?

If we can’t identify any major shifts in traffic that we caused, the next step is to identify if any shifts in traffic were caused by changes outside of our control. For instance, our top ads may become disapproved and stop spending, which could reduce the volume and hurt performance. The common shifts in contribution % that significantly impact performance are shifts by the ad, by creative, and by the audience. It’s helpful to use the date comparison function in advanced reporting to compare traffic contribution % over different time periods for ads, creative, audiences, and demographics.

3. Assuming we can’t point to any major shifts in traffic allocation within Facebook, were there any product or tracking changes on the client-side?

If we can’t find any major shifts in Facebook traffic distribution, and performance is fluctuating for all / most high-volume ads, then we need to understand if anything changed with a client’s product (app or conversion funnel) or if any changes to tracking were made. With apps, we can see the version history in the app store or sites like App Annie and we can determine if any releases correlate with performance fluctuations. There could be changes to an app’s tracking that are not made through an app store release, so app store releases are not a definitive answer for whether changes were made, so it’s appropriate to ask a client if anything changed even if we don’t see any releases that correlate with performance fluctuation.

For web clients, the first step should be to visit the destination URL and look for any obvious changes to the site or to the Facebook pixel. The Chrome browser extension for Facebook pixel help is very helpful. If we notice app store releases or changes to a website, then we should discuss the changes with the client and determine whether they are the cause of performance fluctuation. Comparing Facebook performance to performance across other high-volume traffic sources can help identify if the issue is global and appears to be caused by a product change.

4. Assuming we cannot point to any changes on Facebook or the product, is performance fluctuation caused by macro events that are outside of our control?

If we’re unable to identify shifts in Facebook traffic or a client’s product, then we may be dealing with macro events that are outside of our control. For instance, competition on Facebook may shift to the end of the month, end of the quarter, and the end of the year. We also see major shifts in performance around national holidays and seasonal events like summer, when school is out and human behavior changes. There are also events like the start of NFL season when daily fantasy sports companies disrupt the Facebook marketplace by increasing aggressiveness. We can also look across our portfolio to determine if the fluctuation is client-specific or global. If we see performance fluctuation with most / all clients during the same time frame, then we will have higher confidence that we’re not in control of the fluctuation.

Lastly, the release of competitor apps could also play a large factor in diagnosing performance fluctuations. For example, performance for an FPS mobile game dips due to the release of a new major title. If we have completed a thorough analysis of Facebook changes and product changes, and we still can’t explain the performance fluctuation, then we need to work with clients to determine whether we should temporarily reduce spend until fluctuation normalizes. Or if we should increase testing in an effort to produce a win that will offset the fluctuation. In most cases, it makes sense to temporarily reduce spend. Because new ads that are launched during volatile marketplaces often perform poorly, regardless of creative and audiences.

Still looking for more Facebook & Google user acquisition info?

• Check out our 2020 Facebook Gaming Playbook for more tips and tricks.   
• Ever wonder how your mobile game or app KPIs perform vs industry benchmarks?  Check out our “Mobile App Industry Benchmarks” dashboard and it is 100% FREE.  Uncover your performance vs competitors and see KPIs like CTR, CPM, CPC, CPI, IPM, Conv%, and much more.
• Is the UA Department Shrinking? The Future of Growth Teams in 2020. 
• Don’t Fear the Machines: Why Letting Google and Facebook’s Algorithms Manage Your User Acquisition Advertising Is a Good Idea
• Facebook Power 5
• Facebook’s Structure for Scale
• How to Apply Facebook’s Significant Edits
• Facebook Simplified Campaign Structure
• Google Universal App Campaigns: Best Practices Playbook
• Google App Campaigns: 8 New Tools & Tricks You Need to Succeed
• A Guide to Google App Campaign Best Practices

To learn more about how Consumer Acquisition can support your creative and media buying needs, contact us: https://www.consumeracquisition.com/contact-us/