Advanced UA Techniques For Facebook (Part II)

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:

language country targeting test

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:

advanced UA techniques manipulated values

ROAS Rank

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.

advanced UA techniques audience expansion through Facebook analytics

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.

conversion window

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.

How To Scale Facebook User Acquisition (Part I)

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.

How To Scale Facebook User Acquisition Creative Inspiration

  • 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:

 

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.

Why the Control Video is 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

Scale Facebook User Acquisition Campaign Performance Graph

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.

scale facebook user acquisition 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?

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

IDFA Armageddon Roundup!

In June, Apple announced the depreciation of the iOS Users’ Identifier for Advertisers (IDFA). This is the biggest change in the mobile app advertising ecosystem in the past 10 years.  For some in our industry, IDFA removal will be company-crushing while for others it will create a tremendous opportunity. Learn more about the IDFA Armageddon Roundup!

IDFA Armageddon Roundup

Instead of my normal rants and personal opinion on the direction of the industry, given the magnitude of this change, I thought it would be more helpful to do a roundup and share the thinking of some of our industry’s brightest minds.

What changed?

According to TechCrunch

In iOS 14, users will be asked if they want to be tracked by the app. That’s a major change that will likely have a ripple effect. By allowing users to reject tracking, it will reduce the amount of data that’s collected, preserving user privacy.

Apple also said it will also require app developers to self-report the kinds of permissions that their apps request. This will improve transparency. Allowing the user to know what kind of data they may have to give over in order to use the app. It also will explain how that collected data could be tracked outside of the app.

IDFA Armageddon Roundup Reporting DataIDFA Armageddon Roundup Tracking Data

Gadi Eliashiv, CEO, Singular

Apple hit the reset button on app marketing in iOS.  The IDFA powers basically the entire iOS advertising industry: user tracking, marketing measurement, attribution, ad targeting, ad monetization, programmatic advertising (DSPs, Exchanges, SSPs), device graphs, retargeting, and audiences. Advertisers are faced with a higher level of complexity created by partial IDFA views, fingerprinting, SKAdNetwork, deeplinks, Android, and more.

Paul H. Müller, Co-Founder & CTO Adjust

The biggest challenge of providing IDFA-based attribution under iOS 14 is that you would need the IDFA for every device that installs an advertiser’s app, as soon as the app opens.  There is no logical way to get users’ permission this early on, especially for apps that don’t even show ads and monetize instead through subscriptions and in-app purchases. This is the central problem and one of the main reasons why some in the industry have proclaimed it as the “death of the IDFA”.

John Koetsier, Senior Contributor, Forbes

… iOS users are close to twice as valuable to advertisers and publishers compared to Android users. And that means that iOS accounts for a disproportionate share of that almost $80 billion in user acquisition spend. We are talking tens of billions of dollars here, most of which Facebook and Google hoover up into their ad ecosystems. Now big chunks of those billions are at risk.

… it will be harder to advertise if they don’t believe they can effectively measure the results of their ads. Perhaps most critically, this impacts spend on the two biggest platforms for mobile user acquisition: Google and Facebook … It’s going to be a lot harder for Facebook to run [AEO and VO] campaigns if Apple refuses to let Facebook know what people do in an app after they install it.

tROAS

Similarly, Google Ads can be set to find new users for your mobile app based on “tROAS,” or target return on ad spend. To know that its ads are working, Google needs post-install data from users you acquire via Google Ads: data which will be harder to get now, if not impossible. Google and Facebook are now just like any other ad network: lining up to get a smidgen of privacy-safe information from Apple.

Apple’s Decision

Apple’s decision to essentially kill off the IDFA, while expected, is fundamentally changing how measurement is handled in mobile advertising… It’s impossible to overstate how impactful this change is: almost every large mobile-first business is completely dependent on performance marketing on mobile for revenue growth. Mobile app developers across every vertical are now scrambling to come up with a strategy for continuing operations as iOS 14 rolls out.”

Paul H. Müller, Co-Founder & CTO Adjust

The first issue is that user-level data is necessary in today’s world of user acquisition – not to profile users or serve them targeted ads – but to analyze how campaigns are performing on a granular level. SKA’s proposed 6-bit of downstream metrics with a fixed 24-hour timer does not offer nearly enough insights to manage the highly competitive performance-focused UA campaigns of today. Marketers will no longer receive retention data, revenue tracking, or granular event tracking, meaning they will no longer be able to run their current campaigns. Indeed, the data tracked with SKA and the granular, in-app events tracked by MMP SDKs cannot be tied together, which limits any kind of campaign analysis to the install metrics only.

The second issue revolves around the level of granularity SKA allows for its aggregated data, where only 100 different campaigns will be visible per network. Looking at our clients who run an average of 15 campaigns per network you might think this isn’t such a big problem. But beneath each of these campaigns, there are often countless sub-levels for different geographies, device types, or creatives. With SKA, using 10 creatives in five countries, for example, would only allow you two distinct campaigns per network. Coupled with the random delay of data from each device, this means making granular real-time decisions is near impossible.

Oren Kaniel, CEO, Appsflyer

While we expect that the specifics of the [iOS 14 privacy-related] announcements will evolve with the beta versions of iOS14, we believe that the announcement in its current form will create meaningful challenges for partners, customers, and the app economy at large. Lack of, or inaccurate attribution also means that app developers can’t monetize their work; iOS developers are generating tens of billions of dollars in revenue from advertising, which are at risk of disappearing without proper measurement. 

Alex Austin, CEO, Branch.io

I can’t imagine a single user that would ever agree to let themselves be “tracked around the internet”. There’s no way this will see anything but accidental adoption. Therefore, we must assume that IDFA is no longer usable… this is a fundamental elimination of basic capabilities like user-level attribution, retargeting audiences, look-alike audiences and so much more.

With Google’s recent announcement to remove 3rd party cookies from Chrome, the writing is on the wall for GAID to follow suit, especially with Apple setting the industry precedent.

…[the] industry is reliant on fingerprinting technology with accuracy rates of 60-70%. We will no longer be able to deliver deterministic matches on iOS without a user logged in across your properties but are confident in this alternative approach.  Apple has proposed usage of the SKAdNetwork framework, which completely anonymizes the attribution and eliminates user-level data. This will certainly break most marketing processes.

Tracking Link Methodology

[The] most likely outcome is the traditional tracking link methodology, which is widely used today outside of the self-attributing networks like Google and Facebook. Generally, the industry-standard method leverages IP-based fingerprinting to attribute in-app actions back to advertising clicks. This is the most tried and true method, since it has existed for some time, and we expect most will shift to this at least in the short term.

Mada Seghete, Head of Strategy, Branch.io

All current MMP and self-attributing ad networks (Google, Facebook, Snap, Twitter, etc.) integrations … use the IDFA to confirm device-level attribution on iOS. This allows us to make the attribution decision at the device level. When the IDFA goes away, all of these integrations will break.

Victor Wang, Product, N3TWORK

Safari was the first to block 3rd-party cookies, but nobody substantially changed how they operated until Chrome with its 60%+ market-share announced it would discontinue support for 3rd-party cookies in 2021. In-app accounts for 87%+ time spent on mobile and nearly 50% of all digital commerce.

Apple clearly has stated its company-level willingness to favor privacy over consumer experience, website security, and other unintended impacts. For example, Apple has explicitly said it would break single sign-on services across multiple websites owned by the same company in favor of privacy.

Ziv Bass Specktor, Head of Knowledge, Appsflyer

Assuming 15% LAT and 90% Ephemeral matching accuracy the best case entails a loss of 1.5% attributions. … on CPI or CPA campaigns, media sources that do not support Ephemeral matching may contribute up to 20% free installs to app owners. Audience-targeted Apple Search ads campaigns don’t serve ads to LAT users … non-targeted campaigns on Apple Search Ads may bring a high percentage of non-attributed users.

As demonstrated, although Apple’s LAT is good for app owners, it has the potential to harm ad networks’ bottom line by forcing them to expose mobile app ads to more users than previously needed. The good news is that when mobile installs are recorded by an attribution company with a good Ephemeral matching solution, the effect of LAT is rather marginal.

Paul Muller, CTO, Adjust

“We will continue to enable our clients to not only view their data for all their campaigns across both iOS and Android but also to ensure that this data is actionable.”

Eric Seufert, Founder, Mobile Dev Memo

Apple is cutting off the flow of data to advertisers at the source.

If between 10 and 20% of the ad impression pool on both Facebook and Google vanishes, the price of attributable impressions will go up. As iOS 10 is adopted en masse (it was released to the public on the 12th), if Facebook and Google (and, surely, others) have stopped exposing ads to users with LAT turned on, then the supply of impressions will consistently shrink until adoption rates level out, as they invariably do. Since the number of advertisers won’t shrink concurrently, inventory prices will increase.

… performance advertising on mobile is on an extinction course, with the meteor impact event being Apple’s presumed inevitable deprecation of its proprietary advertising identifier.

So the deprecation of mobile advertising IDs will really just accelerate a trend that has existed for the past few years: despite clear attribution and unit economic metrics, advertisers can’t trust that their spend on any given channel is totally incremental, and thus their measurement is really only valid at the broadest level of granularity.

The move is Apple’s latest assault against the ad industry. 

As far back as 2015, Apple CEO Tim Cook said its Silicon Valley rivals are “gobbling up everything they can learn about you and trying to monetize it.”

Without being able to attribute revenue to campaigns (because all IDFAs are zeroed, and SKAdNetwork transmits no identifiable user information), the traffic sources of monetizing users are unknowable;– Most of the infrastructure currently supporting mobile advertising will soon become obsolete.

The notion that measurability is exclusively a function of attributable clicks has been evaporating for years, hastening for the aforementioned reasons, and yet user acquisition teams have persisted. The demise of advertising IDs won’t precipitate the demise of user acquisition on mobile since the deprecation of advertising IDs will simply take a trend that already exists — of decreased reliance on click attribution in a shift toward more holistic, macro-level measurement — to its logical conclusion.

Zack Whittaker, Security Editor, TechCrunch

These changes represent a seismic shift in the mobile advertising ecosystem. Mobile advertising, and specifically app install advertising, will fundamentally change with iOS 14. ROAS and CPE campaigns will only be possible via the SANs that are able to do any form of fingerprinting via their proprietary SDK data and the revenue data they collect.

Apps Platforms IDs
Image Source: Victor Wong

 

Overview of top mobile traffic sources
Image Source MobileDevMemo

Patrick Amori, Chief Growth Officer MOBE

With the new limitations of IDFA, it will be much harder for programmatic platforms to build user segments that would be then used to build higher-performing campaigns. We must now rely more on probabilistic data segments, which I believe will cause many companies who use programmatic, to scratch CPA offerings altogether and go back to standard CPI campaigns. Our in-house programmatic platform, for example, already has over 5 billion device IDs/user segments, but going forward we will need to rely more heavily on apps sharing user data than ever before.

Vivek Girotra, Sr. Director of Growth Marketing, Elevate Labs

I think the Apple IDFA change is a seminal event for the entire mobile industry. There are many companies whose business models are going to get completely obliterated, and others whose lofty valuations are going to get severely cut down. Anyone who thinks otherwise is either foolhardy or indulging in wishful thinking… My crystal ball predictions: Retargeting, device graphs, buying models like VO & tROAS, and companies that depend on real-time data are dead in the water. Given the mainstream focus on privacy, Google will likely follow suit and the Android device ID will suffer the same fate soon. While SAN’s and MMP’s have suffered a huge punch to the gut, they are pervasive and resourceful enough and will figure out some solutions that the industry will eventually converge towards.

Lior Barak, Co-Founder, Tale About Data

We were all afraid of the European ePrivacy law that would block the app publishers from collecting User data. Well, Apple did it without a law, giving the Users power to share or not share the IDFA. Apple basically gives all Users the option to opt-out of tracking. The worst enemy of the marketing department is optimizing campaigns in the dark (with no data, or limited data) in hopes they wish to grow.

The new change from Apple regarding the IDFA actually means we will have even more Users deciding not to share their information. This raises serious challenges in setting retargeting campaign budgets and driving decisions on if an Advertiser should (or not) invest more or less money in their campaigns. This change will push more companies to build in-house algorithm solutions to decide how to optimize a campaign. As time goes on, this might increase the costs of acquiring and retaining each User. Get Your Copy of “Data is Like a Plate of Hummus” by Lior Barak.

Michael Brooks, SVP Revenue, WeatherBug

The IDFA “opt-in” functionality in iOS 14 represents the biggest shift in the mobile advertising industry to date. It’s going to fundamentally change how apps spend money and make money. The future belongs to apps and companies that provide real value to users because the value exchange we’ve always talked about with consumers will now be front and center.

Jesse Lempiainen, Co-Founder & Chairman, Geeklab

I do believe that the most significant fundamental change will be about how we think of user acquisition as it is. It has shifted (especially with some niche genres) to really targeted Whale hunting, where the importance of event optimization has a lot more in play than it used to. Now that the whole industry loses the opportunity to do this, and practically all the retargeting, it will lead to advertisers focusing more on the only thing they can still effectively optimize creatives. The whole industry will shift back from the payback curve, data-focused marketing towards more creative advertisements with the actual creatives.

Paul Muller, CTO, Adjust

Apple has given the ecosystem two choices for attribution in the future. The AppTrackingTransparency Framework and SKAdNetwork. ATT allows for user-level attribution and post-install analytics per campaign. Thanks to the opportunity of on-device attribution, MMPs will still be able to offer accurate and actionable data for the modern marketer. Our industry is innovative and has adapted to a lot of changes over the past years without losing steam. Adopting ATT is the next logical step to continue building on this unprecedented growth while respecting user privacy.  Read Adjust’s Article on The Future of the Ad Ecosystem on iOS 14

Jennifer Burrington, SVP Sales, TrafficGuard

Opacity has always been a characteristic of the digital advertising ecosystem. Unfortunately, the removal of IDFA is going to exacerbate that, to the detriment of the advertiser. Attribution is going to be harder, obviously frequency capping, and retargeting as we know them will be off the table. With less transparency, fraud is likely to flourish, compounding the challenges of attribution and optimization… The removal of IDFA is going to take this to the next level.

Pau Quevedo, Lead Programmatic Trader, Goodgame Studios

We were already expecting something in this direction since the rumors were increasing a lot in the last few weeks. IDFAs being the oil of the user acquisition industry, it’s clear it will have a severe impact on the Ad Tech industry.  In a return to a probabilistic marketing approach, maybe with PMP Deals and Contextual targeting coming back. From a publisher’s perspective, it means we have to work on our 1st party data more so we can leverage it effectively. For UA the big players will probably find some way to remain as deterministic as possible. But for the smaller players, it will probably get tougher. Retargeting remains an open question, we hope to see some solutions coming up in the near future.

Gabe Kwakyi, CEO, Incipia

Assuming Apple firmly polices the spirit of its new stance, rooting out workarounds like fingerprinting or hard app usage gates forcing IDFA permissions, it’s back to targeting basics for mobile marketing and adieu addiction to incredible ROAS as ad networks’ hyper-efficient algorithmic targeting is nerfed by the sudden loss of massive user-ROI map. While abrupt and overwhelming change is scary, let us also realize that this time inevitably will prove once more the ingenuity of the mobile marketing industry and yield a wealth of innovation – in advertising and beyond.

Expect innovations in statistical modeling to maintain advertising ROI like organic uplift, channel incrementality, and optimal channel mixes (which were already needed due to the proliferation of impression attribution, LAT, and Google’s new untrackable iOS UAC search inventory), more thoughtful creative strategy (rather than the current modus operandi of churning creative out which doesn’t work with Apple’s 100 campaign limitations), and growth in organic, referral and influencer marketing.

Here’s My Take on IDFA Armageddon Roundup!

ConsumerAcquisition shares Apple’s values when it comes to protecting user privacy. As an industry, we must embrace the new rules of iOS14. We also must create a sustainable future for both app developers and advertisers.  I believe we can all agree that user consent is important for any app that monetizes through advertising. Also, there are options to provide user-level attribution and necessary data for performance advertising within Apple’s acceptable framework.

If I had to guess about the future:

IDFA Armageddon Roundup: Short Term

We encourage all publishers to talk to Apple and seek clarification on process and end-user consent along with the use of IDFVs & SKAdNetwork product road map, etc.

We believe publishers will aggressively move to optimize their sign-up funnels to maximize consent. Or live with campaign-only level metrics and lose end-user targeting.   If you would like to continue to optimize towards ROAS, we encourage you to think of privacy consent as a step in the UA conversion funnel necessary to show targeted ads to consumers.

Here’s Twitter’s Solution:

We believe phase 1 of the iOS 14 rollout could look like this:
  • In the first month of its rollout, the supply chain for performance advertising will experience a short-term hit. Especially for remarketing.
  • 1st Step: Publishers optimize user consent flows
  • 2nd Step: User “opt-in” sharing increases
  • 3rd Step: Fingerprinting users rapidly expands in an attempt to maintain the status quo.
    • A publisher’s internal fingerprinting, IDFV, (which or may not leverage fingerprinting) may not create a privacy problem if it is not used for re-marketing/re-targeting. If abused, Apple is sure to shut it down quickly.
    • While fingerprinting is outside of Apple’s control, it appears highly likely to fragment the ecosystem. It will create more barriers for entry to building competitive measurement solutions.
    • If a publisher or MMP sends their fingerprinting to a 3rd party network, this may be a violation of Apple’s policy. This may result in getting an app rejected by Apple’s App Store.
    • Open questions remain on how audiences comprised of apps + down funnel user actions (purchases, etc) without user consent will be created/used.
IDFA Armageddon Roundup: Mid Term
  • Fingerprinting will be an 18-24 month solution and entered into everyone’s internal algorithm/optimization black box. As SKAdNetwork matures, Apple is likely to shut down fingerprinting or reject apps that violate its App Store policy.
  • There will be sustained challenges for programmatic/exchanges / DSP solutions.
  • SKAdNetwork must be enhanced with Campaign/AdSet/Ad level information to keep the mobile ad network functioning.
IDFA Armageddon Roundup: Long Term
  • User consent optimization becomes a core competency.
  • Human-driven, creative ideation, and optimization are the primary lever for user acquisition profitability across networks.
  • Incrementality and optimal channel mix become critical.

We’re all in this boat together. We are looking forward to working with our clients, Apple, Facebook, Google, and MMPs. In order to participate in shaping the future of our mobile app industry.

Look out for more updates from us regarding IDFA changes.

Oh yeah, and now a word from our lawyers: Nothing stated here is legal advice. Please work closely with legal and other professional advisors. Determine how IDFA changes, GDPR, CCPA, or any other laws may or may not apply to you.

Sources for IDFA Armageddon Roundup:

 

See Mobile App Industry Benchmarks — FREE!

Ever wonder how your mobile game or app KPIs perform vs industry benchmarks? We’ve released our “Mobile App Industry Benchmarks” dashboard and it is 100% FREE.  You can uncover your performance vs competitors and see KPIs like CTR, CPM, CPC, CPI, IPM, Conv%, country breakdowns, and much more.

Mobile App Industry Benchmarks

Week of July 6th Highlights:

  • CCPA Compliance Impact
    • Starting July 1, 2020, for some networks, there was a significant global reduction in % of installs being tracked from California from 5.32% contribution down to 0.24% and a corresponding drop in the % of spend from California from 6.43% down to 0.24%.  Do these CCPA compliance challenges foreshadow an IDFA Armageddon with iOS 14?
  • Q2 CPMs Spike To Close The Quarter
    • If you broaden the time frame on the graphs below, you’ll see that March CPMs have almost fully recovered from a high back in February of $18.04, they plunged down due to COVID-19 impacts to $10.17 and now back up to $16.97 as we close out Q2, 2020.
  • CTR
    • 97% Pre-COVID levels
    • 68% Dropping to a low during the first two weeks of April
    • 93% Recovered up to end Q2
    • 87%, Down a bit as we start July, we believe this may be related to CCPA compliance hurdles
  • CPI
    • $15.71 blended CPI as of March 8, 2020
    • $7.20 dropped to a low around March 22, 2020
    • $11.81 CPIs have recovered but still has a ways to go to fully get back to pre-COVID number

 

CCPA Impact – California vs Total US

Mobile App Industry Benchmarks CCPA Impact - California vs Total US

Across Gaming Categories – CPM

Mobile App Industry Benchmarks Across Gaming Categories - CPM

CPM

CPM

Across Country Tiers – CPM

Mobile App Industry Benchmarks CPM Across Country Tiers

CTR

CTR

CPI

CPI

 

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Please reach out to sales@ConsumerAcquisition.com if we can help with creative strategy or media buying on Facebook and Google.

 

Killer Facebook Creative

Get killer Facebook creative! Facebook & Google AI are automating media buying making creative the main driver of profitable UA.

In our newest 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.

We have all seen big changes in user acquisition (UA) advertising in the last few years. Artificial intelligence (AI) is automating more and more media buying, as the best practices outlined in Facebook’s Structure for Scale framework demonstrate. As a result, the AI optimizing the ad platforms has gotten better and better, it’s leveled the playing field for advertisers big and small. Now, creative is the key differentiator driving profitable UA.

And, while quality and volume of creative are key factors in success, we’ve discovered that they’re not the only factors that drive UA success. Monitoring creative trends and doing in-depth competitive analysis is a must within any UA or creative team today. We’ll share the latest trends and best practices we’re seeing.

The Definitive Guide to Killer Facebook Creative for Mobile Game Advertisers

Table of Contents

 

Section 1: Why Killer Facebook Creative is So Important

  • Creative Tunnel Vision and How to Overcome It
  • Creative Trends and Recommendations
  • Word Games Trends
  • Card Games Trends
  • Social Casino App Trends
  • Casual RPG Trends
  • Entertainment App Trends
  • Puzzle and Hidden Object Games Trends
  • Match 3 Puzzle Games Trends
  • Simulation Role Playing Games Trends
  • Simulation Lifestyle App Trends
  • Sports Games Trends

 

Section 2: Using Player Profiles for Market Segmentation

  • Pasta Sauce, Pickles, and Howard Moskowitz
  • People Can’t Always Tell You What They Really Want
  • Player Profiles as UA Creative Strategy 2.0

 

Section 3: Creative Testing and Why the Control is So Hard to Beat

  • Statistical Significance vs Cost-Effective Approach
  • How We’ve Been Testing Creative Until Now
  • Creative Testing 2.0

 

Download Killer Facebook Creative Today!

We have worked with many of the large mobile games on Facebook and Google. Contact Sales@ConsumerAcquisition if you would like to discuss our Creative Studio or Media Buying.

killer facebook creative

whitepapers

Read Our
Whitepapers

Creative & UA Best Practices For Facebook, Google, TikTok & Snap ads.

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