Measurement and attribution are two of the most important components of digital marketing. Without measurement, you can’t determine how well your campaigns are performing or if they’re even working at all. And without attribution, you can’t know which channels are responsible for driving the most value to your business.

Measurement is the process of analyzing data and interpreting the results. It’s also used to determine if your marketing efforts are working and how effective they are.

For example, if you want to know if someone clicked on your Google AdWords ad, you’d need to set up a tracking pixel on your website so you can track any visitors who click on that ad back to your site and see if they convert into customers or not.

Attribution is what happens after measurement — assigning credit (or blame) for specific actions on your website or social media accounts based on the actions taken by visitors before those actions happened. For example, if you have a new customer who purchased your product after clicking on an ad for it on Facebook, then the attribution model would determine that Facebook was responsible for that sale.

Measurement and Attribution Post-iOS 14

After iOS 14, measurement and attribution have become significantly more difficult.

Imagine you own a sailboat with all of the state of the art navigation tools you need.

This dashboard tells you exactly where you’re going, where you are, how long it’s going to take to get there, etc.

It even has the ability to let you autopilot for perfect wind momentum.
Then, someone comes along with a sledgehammer and knocks out the entire system.
While people have been sailing for thousands of years, not having modern tools can increase the difficulty to to succeed.

Unless you’re Magellan or have a large ship with lot’s of resources/momentum, sailing used to be really hard!

We’re seeing a similar trend in the advertising world.

For many advertisers, not having the state of the art measurement tools we’re used to left them paralyzed.

More critically, smaller advertisers no longer have the proper wind in their sales to keep momentum going.

Unfortunately, the all in-one-silver bullet solution (or close to it) we had is gone and now we have to pull out a bunch of old school tools to triangulate success like a map, compass, quadrant, etc.

In sailing, there was (and still is) a solution to seeing which way the wind is blowing by sticking a wet finger up in the air and seeing which side dries faster to understand which way the wind is blowing.

Without adapting to this new normal, it can kind of feel like that with measuring ad performance.

Thankfully, measurement doesn’t have to be that bad even if we don’t have our ideal solution in place.

Unfortunately, the all in-one-silver bullet solution (or close to it) we had is gone and now we have to pull out a bunch of old school tools to triangulate success like a map, compass, quadrant, etc.

Attribution and Measurement Roadmap

Phase 1: Last-Click Attribution and Post-Purchase Surveys

Source: AppsFlyer

The challenge with this type of attribution is that it doesn’t consider other channels that played a role in converting the customer. For instance, if you had a customer who saw your ad on Facebook and then later visited your website before making a purchase, Facebook would receive 100% credit for driving the sale — even though they only contributed 50%.

The post-purchase survey is a good gut-check to helps you understand the accuracy of your last click model and what influenced them along the way.

Post-purchase survey are also a great way to gather data on why customers made their purchase decisions.

It’s best used when you have multiple products or services that are similar but not exactly alike (e.g., different prices). For example, one option might be more expensive than another but offer better features; understanding which one customer prefers will help you better understand which features drive sales and whether that justifies charging more for it.

Last click measurement and attribution alongside a post-purchase survey answers the following questions:

• What is the last touch point someone interacted with before converting?
• Do how people say they heard about us align with their last click to convert for our brand?

Recommended Last Click Attribution Tools

Measurement tools help you gauge the effectiveness of your campaigns. They also let you see how much traffic is coming from each channel and which channels drive conversions. The most common measurement tools include:

• Google Analytics (GA4): The latest version of Google Analytics allows partners to combine a web + app view of their data and understand their full-funnel customer journey. This is one of the most popular tools for measuring website performance. It provides detailed information about traffic sources, user behavior, and much more. The downside is that it’s not free – if you want to use the full functionality of GA4, you’ll need an enterprise plan or higher. You can also use a free version of GA4 but with limited data reporting capabilities.

Recommended Post-Purchase Survey Tools

• Iterate: Iterate is a suite of research tools that help you learn directly from your visitors, customers, and users. With Iterate you can easily send email surveys, share links to surveys, display surveys directly within your website, or in your mobile app product.
• KnoCommerce: KnoCommerce is a customer survey platform built for eCommerce brands on Shopify. They let you collect the customer insights you want, turn them into the marketing strategy you need, and take action that changes everything.
• Enquire: Enquire is another customer survey platform built for Shopify Merchants similar to KnoCommerce.

Estimated Technical Length to Implement

Last-Click Attribution takes under a week to implement and you may get it up and running in one day.

Post Purchase Survey takes under a week to implement with many companies implementing these solutions in a few days.

Phase 2: Multi-touch attribution (MTA) and Omni-Channel Attribution

Multi-touch attribution (MTA) is an extension of last-click attribution. MTA takes into account multiple touchpoints, or interactions with a brand across various channels and media, to determine the effectiveness of each channel.

Additionally, layering in omnichannel measurement allows these models to understand the relationship between your online media (web and app) and offline media.

You can use MTA with any media, including paid search and display ads, social media marketing, and even direct mail campaigns. By looking at the entire path to purchase and not just one interaction, you can better allocate your budget and measure the impact of your marketing efforts.

You can use MTA to answer the following questions:

• Am I over-crediting specific channels in a last-click model?
• Am I under crediting impression-driven channels in a last-click model?
• What is the path to purchase from a media touch point perspective?
• Do I have the optimal channel mix?
• Should I shift the budget from brand to DR?
• Should I shift the budget from web to app?

Types of MTA

Multi-touch attribution (MTA) can be divided into rule-based and data-driven.

Rule-Based Multi-Touch Attribution

Rule-based MTA is typically used by large enterprises with access to internal data sources such as CRM and eCommerce systems. It uses rules to attribute a conversion to a previous touch point. For example, if a customer converts after visiting your website and then later makes an in-store purchase, the rule would attribute the conversion partially to the website visit and the in-store purchase.

Rule-based systems work by assigning points based on predetermined rules—for example, if someone comes through Facebook Messenger, they get three points; if they come through Google search, they get five points; if they come through an email newsletter link in your blog post, they get 10 points. This system works well when you have limited data and need to make decisions quickly, but it’s not very accurate because it doesn’t consider all the available data.

Data-Driven MTA

Data-driven MTA is open source or self-hosted software that uses web analytics data to perform multi-touch attribution analysis. It uses statistical models trained using historical data from your marketing efforts and website interactions. The model then determines how much weight each interaction should receive regarding contribution towards the final conversion event.

Starting to look at the path to purchase instead of focusing on the last click will allow you to invest more on the channels that are driving sales vs the channels that are taking credit.

Google Analytics Data-Driven Attribution (GA4) is an excellent example of this type of model. It uses advanced algorithms to analyze your website traffic, sessions, and events to determine which actions are most likely responsible for each conversion. You can use GA4 as an alternative to “last touch” attribution models because it can help you identify potential sources of traffic that traditional last touch models would otherwise overlook.

Other data-driven tools include NorthBeam and RockerBox. These use machine learning to analyze all available data and determine what actions result in positive outcomes. For example, if we know that someone who came through a Facebook Messenger ad converted at a rate of 10%, we can infer that sending more Facebook Messenger ads will increase conversion rates. Data-driven tools work better than rule-based ones because they allow us to make more precise predictions about what will happen in the future based on past performance.

The advent of iOS 14.5 and the loss of cookies on the web have made this model harder to use.

This was the gold standard for measuring what people did on your website for a long time. It provided a great way to see where people were coming from, how they interacted with your site, how many people converted, and what happened after they converted.

However, as more people started using ad blockers and privacy tools like Ghostery and uBlock Origin, it became harder for companies to track these events. There are still ways to do it — ecommerce companies can use third-party integrations such as Google Analytics or Shopify — but it’s not as easy as it once was.

Recommended Multi-Touch Attribution Tools

• Google Analytics Data Driven Attribution (GA4): This is the latest version of Google analytics that allows partners to combine a web + app view of their data and understand their full funnel customer journey.
• RockerBox: A tool for measuring user behavior, tracking online marketing campaigns, and optimizing websites for conversions. Rockerbox also works with companies that want to track their offline sales.
• NorthBeam: NorthBeam provides both a web-based and app-based solution to help businesses measure their digital performance and ROI and improve their future strategies. NorthBeam helps with all aspects of digital marketing, including website design, search engine optimization, paid search advertising, social media optimization, and more.
• TripleWhale: TripleWhale is an attribution platform that helps eCommerce businesses grow their sales by connecting with customers across multiple channels, including email, social media, display advertising, and mobile advertising in one place. The platform lets you analyze every single touchpoint between your brand and its customers — so you can optimize every dollar spent on marketing initiatives (and beyond).

Expected Return on Investment

The expected return on investment (ROI) of MTA depends on several factors, including:

• How much you are spending on advertising and marketing
• The number of channels you are using
• How many touch points your customer has with your brand before they convert

Implementation Requirements

Rule-Based MTA:

• Defining rules and looking at multiple model types
• Ingesting data from online and offline sources in a way that will allow you to better understand the path to purchase
• Understanding that rule-based models are limited in scope compared to machine learning models, you should look at the path to purchase in multiple ways vs. with one key solution

Phase 3: Media Mix Modeling (MMM)

Media Mix Modeling (MMM) is a method for forecasting sales using customer data and objective information about the product or service, such as pricing and promotion. It is also known as media mix optimization, and it’s a useful tool for marketers because it shows them how various combinations of different marketing channels can affect sales.

MMM is based on the idea that customers respond to different types of advertising in different ways, and marketers should use this insight to maximize their return on investment in each channel. The best way to do this is by trying different combinations of media types until you find the one that gives you the highest return on investment (ROI).

Questions MMM can answer:

• Is my MTA model calibrated correctly?
• Do I Have The Optimal Channel Mix?
• Should I shift The budget from brand dollars to DR dollars?
• Should I shift the budget from web to app?
• Should I shift the budget from offline to online media?

Potential Tools

• Recast– Recast is a new self-serve MMM tool if you’re looking for something simple. It’s an easy-to-use tool that will give you everything you need without requiring technical knowledge or effort.
• Outpoint– Another self-serve MMM tool that offers several different products, including software and training materials that will help keep your campaigns running smoothly no matter how complex they get!
• Analytic Edge- This tool offers simple, user-friendly interfaces to more advanced features like automated optimization, which allows you to save time while still getting the same results.
• Analytic Partners- The tool offers a wide variety of services designed specifically for marketers and advertisers who want to ensure their campaigns are running smoothly. Their platform gives users access to robust analytics tools that will help them understand where their budget is going to better allocate it in the future.

Expected Return On Investment

Media Mix Modeling Tools can save you up to 64% in CPA on average! (Source: Facebook, “Unlock Business Growth with Incrementality Measurement,” 2018)

It’s easy to see why: Media Mix Modeling Tools are designed to help you find the right media channels for your campaign to deliver the most relevant message to the right audience at the right time.

The result? You get more bang for your buck, which means more sales and conversions.

Estimated Technical Length To Implement

The Estimated Technical Length to Implement (ETLI) for Media Mix Modeling Tools is between four and six weeks for SaaS platforms like Recast and Outpoint. For more consultative engagements, it can take up to a year.
Implementation Requirements

You must meet the following requirements to implement a media max modeling solution effectively:

• Aggregate offline and online media inputs
• Individual/log level user journeys piped into MMM software

Phase 4: Conversion Lift and Geo-Based Measurement

Geo-based measurement is where you measure sales based on location. For example, if your business sells products in New York City and San Francisco but not Chicago, you can use geo-based measurement to see how many customers from Chicago have purchased from you.

Questions lift and geo-based measurement can answer

• Are my MMM models and MTA models calibrated correctly?
• Is the media I’m running incremental to my business?
• Am I paying for users I would have acquired organically without spending on my remarketing campaign?

Tools

Conversion lift: Using conversion lift, you can see at a glance which advertisements are driving conversions and which aren’t. You can also see if your ads are bringing in customers who have already made a purchase or if they’re bringing in new customers from other channels.

Geo-based measurement: The geo-based measurement tool allows you to see how many people have been reached by your ads and where those people are located. If you want to see if your ads are attracting people from different cities or states, this tool lets you do that quickly and easily.

Expected Return on Investment

Conversion lift: The expected return on investment (ROI) for conversion lift is the amount of money you expect to receive back from your efforts in a given period. This is calculated by taking the cost of your campaign and dividing it by the number of conversions.

For example, let’s say that you run a campaign that costs $100 and generates 10 new customers. Your ROI would be $10 (10 customers at $100 each).
Geo-based measurement: The expected return on investment for geo-based measurement varies based on the type of business you are in and your goals. If you’re a retail store, it’s probably best to find ways to increase foot traffic and sales. If you’re a restaurant, it may be more important to have a happy staff that consistently turns customers away.

Estimated Technical Length to Implement

• Conversion lift: None
• Geo-based measurement: None

Implementation Requirements

• Conversion lift (on Facebook): Requires CRM uploads or server-to-server integrations like the conversions API

Attribution vs MMM vs Lift

If you’re still confused about the differences between attribution, MMM and lift, the chart below is a great comparison of how these differ.

Like the sailing analogy above, there’s no silver bullet for measurement and you need all of these tools to succeed in a post-iOS 14 world.

Other: A/B Testing for Channel Specific Attribution and Measurement

Outside of measuring across channels, there’s still a lot of measurement solutions available within the ad platforms themselves.

Split testing is the best example of how to measure within a platform.

Split tests are a great way to answer the following questions:

• Should I shift the budget from brand to DR?
• Should I shift the budget from web to app?
• Does tactic A work better than tactic B?
• Does creative A work better than creative B?

While split testing might be daunting, many platforms have these abilities built in natively to their platform to make this capability easy to implement.

Expected Return on Investment

Split testing ROI can vary depending on the test with some leading to sub-10% improvements to while their are instances of tests driving 100% improvements for in-platform efficiency!

Estimated Technical Length to Implement and Implementation Requirements

Because split-testing is an in-platform solution, there’s no requirement technical implementation.

Final Thoughts

Measurement helps you understand how well your marketing efforts are working. With measurement and attribution, you can figure out which channels are bringing in customers, what they’re doing before they buy, and whether or not they’re converting into sales.

Let’s back up a quick second. We know that iOS 14 was released in 2021, and people updated their iPhones to the newer operating service. Simple, right? However, these changes showed massive implications on advertisers’ ability to market, track, and understand data from potential and current customers due to opt-outs from tracking from iOS (Apple) devices.

Changes to that opted-out user data include shorter attribution windows, eight standard, and custom optimization events, no demographic data in reporting breakdowns, and likely smaller retargeting audiences due to only the highest prioritized events being noted.

What does this mean? It means that in-platform reporting in Facebook Ads Manager isn’t accurate or thorough.

Enter: third-party data solutions.

While Northbeam and TripleWhale are new attribution tools, they are hardly the first to market. There have been other software tools in the multi-touch attribution game for years now — including Visual IQ (2006) and DTC-focused RockerBox (2013).

But before we go any further, let’s first strip the concept of attribution tools down to its barebones.

What are Attribution Tools?

The primary goal of attribution tools (or MTA tools) is to provide marketers with an out-of-the-box or semi-customized attribution tracking & modeling to help marketers understand how much credit should be given to each marketing touchpoint. Put differently, attribution tools give you an insight scoop into your customers’ buyer’s journey to understand what’s working and what’s not.

If you think about marketing attribution as a basketball game, most marketers tend to focus on the person who scored. As in, the channel that directly drove conversions. But this means you’ll be missing out on a lot of data. It takes an average of 5 to 7 touchpoints before a conversion can materialize—just like there are multiple players on each team who assist in a play.

So an attribution tool is critical in giving you a complete picture of all your channels that lead your team to victory.

Attribution Tools vs. In-Platform Reporting: What are the Key Differences?

Using our basketball analogy above, below are two key differences between attribution tools and in-platform reporting.

1. Key KPIs
In-platform reporting makes your conversions the MVP star player.
Attribution tools make your advertising ROI the MVP. This is ultimately what matters for your marketing efforts, and partly why most Facebook Ad Manager users invest in a separate attribution solution.

2. Customer Journeys
In-platform reporting shows your top conversion paths across your Facebook ad campaigns. However, it doesn’t provide user-level data nor ROI, ROAS, or LTV tracking. So you don’t know which paths lead to the highest ROI.
Most attribution tools show each touchpoint that leads to a conversion and let you drill down into individual user paths. So, in addition to aggregate conversion paths, you’ll know who’s on your team roster and the individual stories that led them to your team.

In a nutshell: Attribution tools are a huge upgrade from in-platform reporting as they can show your ROI, the effectiveness of your channels, and the full view of your customer journey.

How Attribution Has Changed Post-ATT

Let’s be honest here.

Attribution is broken — even before the dreaded iOS 14 update. As a reminder, this update prompts and allows users to opt-out of ad tracking.

With more than 50% of smartphone dominance in the US alone and 27.6% of the global market share, it’s no big surprise that the updates led to major changes in ecommerce and the decline in accuracy of tracking in key paid media platforms. Most importantly, Facebook.

Here’s what changed when it comes to Facebook, in short.

• Data: Pre-ATT, Facebook was allowed and was able to connect users’ in-app add views to off-app activity, such as visits to site. Today, however, users are automatically opted out of this.
• Conversion tracking: Even if someone opts in, advertisers are limited to tracking just 8 events on the Facebook pixel (standard and custom). Prior to the update, there wasn’t a limit on the number of events you could track. If the user opts out of sharing their data, you’ll now be limited to tracking just 1 conversion event.
• Attribution window: 28-day and 7-day view-through attribution windows are no longer supported. Before the update, you could track someone who viewed your ad but didn’t purchase until 2 weeks later. The new attribution settings are set to ‘7-day click’, meaning your ad will be optimized for users who are likely to convert within 7 days.

Now with the current iOS 15.4.1 update, you can be dead sure that relying purely on media platforms such as Facebook and Google Analytics for reporting attribution is a fool’s game.
Keeping these issues in mind, it’s clear that for advertisers to stay ahead of the curve, they need to leverage attribution tools like Triplewhale and Northbeam. In other words, they need these tools to help make sense of reporting in a post-ATT world.

Digging further, the first-party pixels of platforms like Northbeam and TripleWhale have tried to replace the biggest change to Facebook and other platforms’ reporting post-ATT: the shift from deterministic to probabilistic data.
Speaking of deterministic and probabilistic data, let’s take a quick detour and examine what these two concepts really mean.

Deterministic vs. Probabilistic Attribution: The Key Differences

By definition, deterministic attribution is an attribution technique that uses device IDs and other personally identifiable information to identify the same user across different devices. Because the device ID is used, the match is 100% accurate for attribution.
Probabilistic attribution, on the other hand, is based on probabilities, not ID or device matching. In other words, this form of attribution relies on machine learning and statistical modelling techniques to identify probable conversions across various marketing touchpoints.

Since probabilistic attribution relies on trying to match device data with a user database, this technique is typically less accurate and is used as a fallback when a deterministic identifier is unavailable.

In addition, the data collected for probabilistic attribution, such as the user’s IP address, changes much more frequently than a device identifier, which means that probabilistic attribution has a shorter attribution lookback period than deterministic attribution. Specifically, the accuracy of correctly identifying a device decays exponentially after 24 hours, so this is the typical cutoff period for the probabilistic attribution lookback window.

Building the Case for Attribution Tools Post-ATT

Prior to ATT, Facebook was really good at deterministic attribution and doing it at scale. With nearly 3 billion people using their real names on the platform, Facebook was (and still is) the de facto identity layer of the internet.

This gave Facebook a competitive advantage in reporting because the data wasn’t only accurate but thorough. If Facebook drove 100 conversions, their level of deterministic data likely matched on 95+ of those conversions. Even if a cookie failed to match a user, the platform’s ability to match someone’s email, name, or some other PII data was high. It also didn’t matter how few conversions you drove.

If you only drove 10 conversions, Facebook could still confidently determine 9 of those conversions via deterministic data.

However, post-ATT, deterministic data has been significantly hampered and practically rendered unusable. As a result, reporting has moved to probabilistic-modeled attribution for all ad platforms.

This means the data shared with ad platforms is less accurate and less thorough. And the less data you have as an advertiser, the worse your modeling is.

For example, in this new world, the advertiser with 100 conversions will likely see more accurate modeling than the advertiser with only 10 conversions.

(It likely also doesn’t help that Facebook is using a mix of deterministic and probabilistic data when they display reporting, further confusing advertisers on their in-platform performance.)

Now, what will an advertiser that’s relied on deterministic data for so long do, especially those with less signal? Find an alternative. This is where attribution tools like TripleWhale and Northbeam realistically come in.

Newer tools like TripleWhale and Northbeam emphasize deterministic data with a view of helping advertisers bring back the good old days of Facebook reporting. No wonder they’ve been largely marketed as post-ATT solutions.

Unlike solutions like Google Analytics, these new tools have pixels that don’t only rely on cookie-based data to help with reporting, but also integrates with your CRM/POS system directly. This integration essentially reconnects your PII data with your ad for insights, similar to what Facebook was able to do pre-ATT with a similar level of accuracy (albeit with less thoroughness). That deterministic data helps with solving some of the teething problems that have emerged post-ATT.

For example, these tools can be very helpful in understanding how a creative is performing without waiting 3 days for Facebook to report back performance. Or to understand the potential path a customer takes before buying.
To further understand how beneficial post-ATT attribution tools truly are, let’s take a look at their capabilities further.

TripleWhale and NorthBeam’s Other Attribution Capabilities

Here are some of the metrics you can see with the TripleWhale and NorthBeam pixels:

1. Your ROAS Data
You can access your ROAS by clicking on the ad platform you want to track. Once you spot the campaigns related to that platform, you can identify the platform’s ROAS and TripleWhale/Northbeam ROAS tied to it. You can even compare different attribution models like First Click vs. Last Click by clicking on the pixel’s icon.
Best of all, the platform allows you to drill down into any given campaign, ad set, or ad. From there, you can see the list of customers or orders that can be traced back to that ad. How cool is that?
To get more information regarding any particular customer’s journey, all you have to do is to click on their name, and their entire journey will be displayed in a popup, such as what ads they clicked on, what pages they visited, and what they purchased.

2. Channel Overlap
As you well know, the buyers’ journey will not always be linear. Sometimes customers would have engaged with different ad channels before making a purchase.
This is where a tool like Triplewhale’s Channel Overlap helps out by providing a visual display of the number of customers in multiple touchpoint journeys with the ability to toggle between overlapping different ad channels to guide in decision-making.

3. Live Order Attribution
Nothing is more exciting than seeing sales being made live.
Live Orders keeps you in the know by updating every time a new order is placed in your store where the customer has clicked through an ad at any time before making the purchase. (Tools like Google Analytics typically have a delay in how they report back.)

4. Other Useful Metrics
In addition, you can track tons of other data from the Triplewhale and Northbeam pixels by organizing them into the columns according to your preferences.
Some of the other metrics you can unearth include but are not limited to the following:

• AOV
• Conversion Value
• New Revenue
• CPA

These advanced metrics are available without any complex configuration required. TripleWhale even helps you to predict the future revenue of cohorts based on your data history, and you can segment each cohort by things like first product purchased, discount code, or other important metrics.

For those using the mobile app, Triple Whale is particularly helpful as it allows users to easily monitor their profit levels and gain real-time notifications when their profits or ROAS drops.

See, we’re not kidding when we say Triplewhale and Northbeam have marked a new era in attribution.

Attribution Tools are Indispensable in a Post-ATT Advertising World

Add it all together, and one indisputable takeaway emerges. Attribution tools are of incredible importance, especially if you have less signal to begin with and you’re looking to make sense of reporting in a post-ATT advertising world. Triplewhale and Northbeam, in particular, are cut out for accurate, thorough attribution and can help advertisers understand their performance on a more aggregate level.

However, it’s crucial to note that these tools are no silver bullets but merely another tool in your toolbox (see the sailing analogy in this Twitter thread I shared a while ago). If you don’t understand how they fit into your current measurement and their limitations, you might make potential errors in how you buy media moving forward.

With over 1 billion active global users, TikTok is driving significant growth and opportunities for brands of all sizes across various industries. TikTok’s video-focused content enables brands to connect with mass audiences and be part of the social conversation – it provides powerful tools for advertisers to launch campaigns, monitor data, manage ad resources, and optimize their advertising. 

TikTok recently announced that on April 28, 2022, it will be enabling first- and third-party cookies for all standard developer mode pixels that were created before March 10, 2022. To help you prepare for this launch, this post will walk you through the benefits you can accrue from this change and what impact it will have on ads. 

What Is a Cookie? 

A cookie is a small piece of text sent to your browser upon visiting a website. Cookies help the site remember information about the visit, thereby making it easier for you to revisit a site. They help measure, optimize, and target your campaigns. When first- and third-party cookies are used together with Advanced Matching, performance is boosted significantly. 

By pairing cookies with Advanced Matching, you can further supplement and optimize the matching of visitors on your website with people on TikTok. Some of the benefits of matching include: 

• Boosting campaign optimization: Enhance bidding efficiency to increase conversion events. 
• Expanding your reach: Matching helps you build bigger and better audiences who can reach your website via TikTok. 
• Measuring full-funnel events: Advanced Matching optimizes attribution and reporting of all events with enhanced signals. 

What Changes Will There Be When TikTok Enables 1st and 3rd Party Cookies 

Given the delay in the depreciation of third-party cookies, TikTok has decided to activate the use of first- and third-party cookies. These cookies will supplement the existing event data already shared with TikTok and also optimize both the reliability and quality of event data. 

Cookies typically save a visitor’s conversion events related to an ad campaign. On the flip side, a session ID is generated on the advertiser’s website, which saves information about a visitor’s every single visit, including page views and conversions. 

With the looming update, TikTok will introduce new matchkeys: 

• First-party cookies: These are created and owned by your website via the TikTok pixel. They can be enabled so that you can use them with your pixel. 
• Third-party cookies: These are created and owned by TikTok through TikTok’s server. These cookies will always be on your pixel. 
• Session ID: This is created by the TikTok pixel and records user event information for every visit to an advertiser’s site. Session ID works with cookies to enhance measurement, attribution, and targeting. 

The activation of first- and third-party cookies will allow TikTok to recognize and learn about people who visit your website and the actions they take or don’t take. With this information, TikTok will deliver your ads more efficiently to people who are likely to convert. What’s more, TikTok will enhance the attribution of events to your ads and inform you how effective your events are at converting leads and growing your audience. 

What Is the Effect of TikTok Activating 1st and 3rd Party Cookies for Their Pixel? 

Creativity is super important, but strong ad performance is the solid foundation for a high-performing ad account. This is the reason why Facebook has been significantly impacted post iOS 14 implementation. Even with the best ad creative, performance would still be lower than it was before the implementation. Good creative isn’t as effective if you can’t target the right audience with your ads–even on a platform like TikTok. 

That said, TikTok’s announcement of leveraging first- and third-party cookies is the right step towards improving their signals for advertisers. This will be different from the already existing Events API and Advanced Matching, which are helpful but not impactful from a matching standpoint. 

TikTok’s identity layer is relatively weak compared to that of Facebook. Anecdotally, this is among the reasons why CRM audience uploads on TikTok are still in beta, and the match rates for emails range between 5-10%. 

So what will be the impact of enabling first- and third-party data in the short term? And how will it be in the long term? 

Short-term Impact of 1st and 3rd Party Cookies Activation 

Here is an outline of the short term effects first- and third-party cookies will have on TikTok advertising: 

Larger Retargeting Audience 

One of the main ways of running ads for a client or a business is to build a qualified lead to use for retargeting ads. These audiences are valuable and can help keep the lights on, so to speak, while you are taking the time to look for new customers. Retargeting is an effective way of building your client base, given that it focuses your advertising spend on people who are already familiar with your brand and have recently demonstrated some interest. This is the reason why most advertisers use it to see a higher ROI (return on investment). 

By enabling first- and third-party cookies, TikTok will make it easier track users across the web, enabling a larger audience for retargeting.

First-party cookies (which are created by the website owner) enable website owners to see which users return to their site. These cookies help ensure that the content advertisers display is tailored to their audience preferences and provides them with the best experience. These cookies collect analytics data that assess how users browse around a site. That said, first-party cookies aren’t effective when used on their own, but when used together with third-party cookies and session ID, they make it possible for advertisers to retarget larger audiences on TikTok effectively. 

Needless to say, when you have the ability to retarget a large audience, the chances of converting them into customers are higher. 

Accurate targeting is the backbone of a successful marketing campaign. It helps brands by ensuring that their ad dollars are working hard to reach precise audiences with ads that are relevant to them. 

Better Measurement Across Browsers and Devices 

Activating first- and third-party cookies will also improve measurement across browsers and devices–it will enable TikTok to identify and gather information about users who visit a given site. This includes how long they browse, their interests, the actions they take, and so on. This information will enable TikTok to deliver ads to people who are actually likely to convert. 

Upon activation of the cookies, they will measure the number of unique visitors and track users’ interaction with your website, device specifications, clicks, and search history. This data helps facilitate targeted advertising and also gathers website analytics. 

Simply put, these cookies won’t just help marketers and advertisers to achieve success by tracking user behavior; they will also help them measure and maybe even multiply it. 

Improved Campaign Optimization 

Activation of the cookies will also make the online activities of advertisers on TikTok easier by enabling them to recognize user behavior. Customer targeting is similar to a scenario whereby you walk into your local diner, and the waiter knows that you are a vegan so won’t serve you meat. 

Cookies will improve the effectiveness of your ad campaigns. They enable you to monitor your visitors’ behavior and create content or products that are targeted to their interests and preferences. That way, you will give them a reason to come back to your website. 

Upon enabling the cookies, TikTok will correlate event data to individuals who use your services to support the objectives of your ad campaign, improve the effectiveness of your advertising campaigns, and personalize the ads displayed to your audience. 

In connection to improving the effectiveness of advertising campaigns, TikTok will use event data for delivery optimization only after combining such event data with other information collected from other advertisers to target consumers based on event data. 

According to TikTok, you can expect to see approximately +50% more matched events by using cookies with Advanced Matching, which will result in more conversions and better ad performance. 

Long-term Impact of 1st and 3rd Party Cookie Activation 

So what will be the long-term effect of TikTok enabling their first- and third-party cookies? 

Just like with Facebook (and other platforms), the use of online behavioral advertising gives TikTok the capability to target people based on what actions they undertook on the platform. For instance, if you visit David’s Bridal, TikTok will now know that you are in the market for a wedding dress and can use that intent data to serve you more wedding (dress) related direct response ads. 

While TikTok might not fully leverage online behavioral advertising features immediately, this is a tool that TikTok will likely utilize more by the end of the year as it tries to figure out how to incorporate event data into their ad optimization model. 

While TikTok pixel may not have nearly as many websites as Facebook, it is rapidly building its web footprint. According to BuiltWith, approximately 500,000 websites have the TikTok pixel installed. In contrast, Facebook’s pixel is in over 6.7 million websites. With more websites adopting TikTok, it should be able to target and optimize and become stronger over time with the use of online behavioral advertising.

Getting Ready for the Cookies 

Here are some of the things you should be aware of as you get ready for TikTok’s update regarding the implementation of cookies: 

• You should keep in mind that there won’t be immediate changes in other TikTok ad functions –this is because TikTok is only introducing cookies and updating Pixel. 
• Cookies will only be available for TikTok Pixel, Developer Mode, and Standard Mode. It won’t be available for Events with API or Pixel Partner Integrations. 
• While you can use the cookies without Advance Matching, the performance and matching capabilities will be less effective. To obtain the best matching of users and a more comprehensive understanding of how your ad campaigns are performing, it is recommended that you use Advanced Matching together with first- and third-party cookies. 
• Evaluate the actions you should take to ensure that your website and privacy practices comply with regulations. For instance, this may include obtaining user consent where necessary or updating privacy disclosure where needed. 
• Suppose you are required to obtain user consent for cookie usage; make sure that your website disables the setting and the usage of cookies when a user declines consent either by using a third-party consent manager or a TIkTok’s tag manager. 
• Determine whether it’s a requirement that you obtain opt-in consent before you can transfer data to TikTok via the pixel. 
• You should provide clear, comprehensive information to users in a sufficiently prominent notice regarding access to, collection, sharing, and usage of cookies. Always ensure that your cookie notices and disclosures are compliant with your practices. It is advisable to check with your legal representative for guidance. 

How to Use Cookies with the TikTok Pixel 

Any TikTok Pixels that were created after March 10, 2022, will use first- and third-party cookies by default. On the other hand, those created before this date will have cookies enabled after April 28, 2022. Users will be able to configure cookie usage in their Pixel Settings in the Events Manager. Here are your options: 

• Use both first- and third-party cookies: When you use cookies with the Advanced Matching, you can further enhance and supplement the matching of visitors on your site with TikTok users. This option allows you to maximize your pixel’s attribution and matching capabilities. 
• Use third-party cookies with TikTok Pixel: You can opt to only use third-party cookies by toggling first-party cookies using your TikTok Pixel. If you opt for this option, you won’t be able to maximize your Pixel’s attribution and matching capabilities.

Update (June 20th, 2022): Since writing this post, TikTok rolled out flexible attribution windows. While this won’t help with the system’s optimization, it will help improve measurement in-platform. You can read more about this update here.

Do you need help using first- and third-party cookies on TikTok for your advertising campaigns? Contact us.

But that’s only looking at the update on a comprehensive level. As it turns out, some advertisers are seeing significantly more negative impacts of iOS 14 than others. Especially brands with large average order values (AOV) are struggling more, leading to necessary strategic adjustments to maintain an efficient budget spend and maximize their efforts for ROI.

So, let’s dig in. We’ll start by outlining how advertisers with large AOVs are struggling since the iOS 14 update, before focusing on 5 strategies for Facebook advertising your brand can take should you find yourself in this position.

The Problem: Drops in Facebook Conversions for Brands with High AOV

It’s a problem more than a year in the making. According to research by Varos, weekly Facebook conversions for brands with average order values at or above $200 who invested more than $10,000 per month on Facebook dropped by about 35% in February 2022, when compared to February 2021.

• Conversion rate for these advertisers dropped at about 43%.
• CPM rates for these advertisers increased by about 29%.

Naturally, lower reach on the same budget combined with a lower conversion rate depressed conversions. Interestingly, though, click-through rates did not drop. Instead, they rose 23% year-over-year.

It’s difficult to find a more obvious cause than last year’s iOS 14 update for this. More users opting out of tracking means lowering signal, making it harder to find the right people to convert on your ads or website.

In some ways, volume acts as the great equalizer. Brands that sell hundreds of products on Facebook every day have seen a less significant impact on their conversions. But for advertisers with daily sales below triple digits, Facebook’s models have become less reliable.

Facebook’s Chief Operating Officer Sheryl Sandberg even confirmed this trend in Meta’s 2021 Q4 earnings call, when she discussed that iOS 14 challenges for advertisers are

“…easier to address this with large campaigns and harder with small campaigns, which means that part will take longer.”

So, the question moves to mitigation. How can brands with high AOV adapt, doing “more with less to improve their paid Facebook performance? In our campaigns over the past year, we’ve seen five steps work especially well in this environment:

1. Consolidate Your Individual Campaigns
2. Build Creative that Speaks to Broader Segments
3. Forget About (Or Adjust) Your Retargeting Efforts
4. Use DABA and Other Automated Ad Solutions
5. Leverage On-Platform Signals For Better Data

Let’s explore each of these steps in more detail.

1. Consolidate Your Individual Campaigns

When it comes to Facebook ads, the conventional wisdom has been to build nuanced, specific targeting. That strategy allowed advertisers to speak to specific audience segments in more nuanced ways about their core interests and pain points, increasing sales volume, and lowering CPAs.
We generally use a conversion threshold of 50 per week for a campaign to be fully optimized. A strategy that depends on individual campaigns for specific audience segments becomes a problem when overall conversions are down.

For example, if your Facebook ads were resulting in 100 sales per week targeting men and women separately prior to the iOS 14 update, hitting that 50/week conversion threshold may no longer be possible. A 35% decrease in conversion might mean that both campaigns combined now only result in 65 conversions, or 33 per campaign. Each individual campaign is no longer fully optimized.

Consolidating your campaigns can get you back above that threshold. Now, a single campaign results in 65 conversions, allowing the overall campaign to be more fully optimized.

That’s not just a theoretical exercise. When we tested consolidating campaigns that had previously been separated by gender for one of our higher AOV brands, the results were significant. Simply consolidating genders drove a 24% decrease in CAC in this post-iOS 14 environment. If you get it right, consolidation works.

2. Build Creative That Speaks to Broader Audiences

A natural consequence of consolidating your campaigns, of course, is that your creative has to follow suit. In a pre-iOS 14 world, you might have been able to set up different audiences designed to reach more distinct segments and pockets within your larger target. For example, your testing may have shown different creative working better for different age brackets, allowing for increased customization.

Unfortunately, the necessary consolidation for brands with fewer conversions carrying higher values means this strategy is no longer effective. Ads have to be broader in focus, speaking to the broader audience your ad sets are now targeting.

Ads that may have previously targeted a specific gender now have to include gender-neutral messaging and visuals. Ads once customized for an age bracket should now work across your entire audience’s range. In short, any consolidation you make has to be followed through not just in targeting, but in your creative as well.

Gone are the days of higher AOV brands running two separate campaigns. Unless the baseline of weekly conversions supports it, there is simply not enough signal to make it work as effectively.

The good news: generalizing your creative can help to improve your conversions. In our consolidation experiment mentioned above, creating more general messaging and visuals further improved CPAs beyond the initial 24% reduction.

3. Forget About (or Adjust) Your Retargeting Efforts

This step might just be the most controversial recommendation in this guide to adjust to the new reality of iOS 14. After all, brands with high AOV tend to have a longer purchasing cycle, which makes retargeting a seemingly natural fit to keep your brand and product top of mind in the consideration stage.

Unfortunately, in our experience, real-life campaigns don’t back up that assumption. Let’s say, for example, that you’re getting 30 weekly conversions from prospecting, and 15 weekly conversions in your middle-of-funnel and bottom-of-funnel audiences post-iOS 15. Knowing that Facebook optimizes campaigns at the ad set level and knowing what we know about volume as outlined above wouldn’t those 15 conversions be better captured as part of your prospecting campaign?

Keep that 50/week conversion threshold in mind. With a single ad set, your 45 total weekly conversions are 90% to your weekly conversion goal. Multiply that threshold by three for three separate ad sets, and your 45 weekly conversions are less than one-third of the conversions you need for full optimization.

You might still be able to find a way to integrate retargeting into your overall paid strategy. If you are already getting enough optimized signal and want to reach net new audiences, you can include all MOFU and BOFU audiences from your prospecting campaign and set up a retargeting campaign specifically to audiences who need more than one ad.

But that’s only if your prospecting campaigns are already reaching their optimum signal, which–again–has become less likely post iOS 14. If you want to try it out for yourself, and your budget allows it, try split testing the impact of retargeting on your CPA against a campaign in which all funnel stages are consolidated into a single ad set.

4. Use DABA and Other Automated Ad Solutions

Facebook’s Dynamic Ads for Broad Audiences, or DABA, is a woefully underused self-optimization technique for brands who spend more than $10,000 monthly on the platform. Especially if you have a large enough product catalog, and your AOV is above average, we’ve found it to be the single most effective creative ad unit available today.

The basic concept behind this ad type is simple: Facebook learns about the products you sell based on web and app browsing activity, then leverages that information along with data it generates from other websites to predict which people might be most interested in your products. You can then promote each of your products with unique messaging, without having to create custom creative for each.

You might have heard about or even share the typical reservations about DABA, which is less control over your own creative and weaker creative than custom-created standalone ads. But don’t underestimate the potential effectiveness of this option for your conversion efforts.

DABA automatically shows the right products to users, increasing relevance for each specific users. Ads can show up as collections, carousel ads, or standalone assets, using video or static images. Copy options like “Free Shipping,” “Sale,” and “Popular” get inserted dynamically alongside basic product details.

Add it all together, and a product catalog with 50 products can result in more than 1,000 relevant creative permutations, automatically adjusted to most closely fit audience preferences.

DABA is not the only dynamic creative tool, either. Automated App Ads, for instance, allow app advertisers to run 50 assets in the same ad set dynamically. With less overall signal to test as much creative as was possible before iOS 14, automated tools like DABA and Automated App Ads become foundational components for high AOV products to maximize messaging relevance and succeed.

5. Leverage On-Platform Signals For Better Data

At this point, the impact of iOS 14 reducing Facebook’s off-platform signal is well known. It also means that Facebook advertisers need to gain more, higher-intent first-party signals to continue to reach more relevant users. Two ways are especially successful in help to accomplish that goal:

1. Creating Instagram and Facebook Shops as a potential ad destination
2. Using product tags on all of your ads.

Online storefronts on Facebook and Instagram are beginning to gain popularity for a reason. Facebook Shops now attract more than 250 million monthly visitors, driven at least partially by the platform’s desire to keep its users on platform. It’s only natural, then, for Facebook to offer the option to send traffic to your on-platform storefront instead of your .com website.

Tagging your products in either catalog or video and static ads is another effective tactic to ensure more relevant on-platform signal. As users interact with the tags on your ads, and visit your product pages on-platform or on your website, Facebook can gather relevant information to better catalog and optimize for audience behavior and intent.

The Post-iOS 14 Reality for High AOV Brands: Doing More With Less

After years of best practices that focused on more segmented, specialized, and targeted ads, many of the tactics discussed in this guide may seem counterintuitive. But, as platforms and user behavior continues to adapt to stricter privacy preferences, only brands that adopt their strategies alongside their audience’s preferences have a chance to continue succeeding.

In the world of iOS 14, and especially for brands with high AOV, that means doing more with less. More general ads and creative allow for better optimization possibilities, including the consolidation or even elimination of regargeting efforts. DABA and other automated solutions, which can dynamically create ad permutations based on user intent, as well as new strategies to leverage on-platform signals, can all help to push more signal through for products with lower conversion volume.

Almost a year into the change, we now have data to back up the strategies discussed in this post. As your brand continues to adapt to this new reality, doing more with less will remain a crucial consideration as you set up and optimize your campaigns over time.

There is no doubt the rapid changes to the ads ecosystem make it much harder to measure the true value of marketing. Fortunately, geo experiments offer a great way to quantify ad effectiveness via lift. Facebook purposely created GeoLift, a leading open-source tool that makes it easy to design and run robust geo experiments. This article provides everything you need to know about GeoLift and how it helps measure incrementality.

What Is Incrementality in Marketing?

Incrementality is the lift or increase in the desired outcome provided by marketing activity. The outcome could be profitability, revenues, conversions, web visits, or awareness. Incrementality testing and measurement provide a true incremental contribution of your paid media at the ad, campaign, tactic, or channel level. Incrementality in marketing is specifically necessary for channels where ad impressions are challenging to map and measure, like social channels such as Facebook, Snap, Pinterest, and more. With incrementality measurement, you can determine:

• Waste to eliminate and opportunities to scale, expand, and divert media spend for optimal growth
• The media channels, publisher, campaign contributions to the marketing goals
• Whether to launch new campaigns or ads to increase contribution to conversions at the portfolio level

Why Is Incrementality the Gold Standard of Measurement?

Incrementality-based measurement allows you to determine the actual value of your marketing efforts. Essentially, a campaign’s incremental effect is the difference between what you observed as the results of a specific campaign and what could have happened if it didn’t take place.

What Is GeoLift?

GeoLift is Facebook’s open-source solution designed to measure lift at a geographical level. The tool utilizes advanced synthetic control methods to deliver insights that help you make informed decisions established in incrementality and measure the true value of your marketing campaigns. GeoLift provides unmatched solutions for geo experimentation, spanning data ingestion, power analysis, and market selection to inference. Besides, the tool’s open-source nature makes it transparent and fully reproducible. The tool is highly adaptable to the needs of various businesses, making it ideal for users who want to employ other assessment strategies due to data limitations.

Importance of Lift Analysis

Lift analysis is a method for determining how a marketing campaign affects specified indicators. It is a technique for tracking variations in metrics such as clicks, conversions, engagement, impressions, and more. It measures the percentage rise or decrease in each metric for users who were the recipient of the campaign against a control group. Ideally, the control group that was not presented with the campaign is called the lift.

Credit: https://mackgrenfell.com/blog/conversion-lift-tests-are-dead-transitioning-to-geo-experiments

• Determine whether your ad campaign is triggering short-term spikes in engagement or long-term increases in loyalty and app usage.
• Determine the ROI of each marketing strategy,
• Save valuable time and avoid targeting your campaigns in areas with high clicks but low sales.
• Track repeat conversions instead of one-time conversions

What Does GeoLift Do?

GeoLift comes with a pretest dataset included in the package. The package, which consists of the simulated data of 40 cities in the USA across a period of 90 days, is comprised of three variables, namely:

• Location (city)
• Y (total number of conversions each day per location)
• Date (in “yyyy-mm-dd” format).

GeoLift can effectively perform the following tasks:

Handle Data Processing

After you feed the dataset into GeoDataRead, it will assess the dataset and perform other related tasks. These include handling locations with missing data by configuring the ideal method of imputation. It will also transform the dates in the date column to timestamps. Since it is a data-backed process, the time series would indicate a similar pattern being shared across multiple locations.

Carry Out Power Analysis

Power analysis is the vigorous and comprehensive statistical analysis to extract crucial information from data. Power analysis is an essential step to a successful test. It helps you find vital parameters such as:

An optimum number of test locations: To find out the optimum number of locations where running your test would deliver the best results, you have to leverage the NumberLocations() functions in the GeoLift package. The function is designed to simulate many tests and evaluate their outcomes.

Insights: The plot of Average Power vs. Number of Locations helps you observe how to get a well-powered test even from a few locations. It delivers a perfect start to your analysis so that you run the test in a few preferred locations and still get optimum inferences from it.

Probable action: You can choose two to three locations to run the test in and obtain the best results with minimal effort.

Determine the ideal test and control location: Once you determine the number of locations, you can move ahead to work out the best combination of test locations to run the test. To achieve this, leverage the GeoLiftPower.Search() function. The function helps provide you with a ranking of various location combinations. This helps you achieve the following:

• Insight: You can see the ideal test locations combinations to focus your campaigns
• Probable action: You can implement your test in one of the top-ranking markets while putting all the other cities in the dataset in the holdout group.

Determine the minimum detectable effect to get substantial results: Although GeoLiftPower.Search() reveals the test and control markets that can reliably produce well-powered results in synthetic control tests, it can’t calculate the lift you need to obtain a powerful test. Consequently, you must now employ the GeoLiftPowerFinder() method designed to investigate the Minimum Detectable Effect that a combination of test and control groups can produce.

Find out a suitable test duration and ballpark for the budget required to do a test: Once you have determined all your obtained parameters, you can now find out the ideal duration for the test and the reasonable budget you need to run it. To do this, find out the Cost Per Incremental Conversion (CPIC) to use the GeoLiftPower() function. The CPIC enables you to obtain the approximate value of the budget you need for a test.

Determine Actual Lift

GeoLift can also help you compute the actual lift that your marketing campaign has achieved. You will leverage the GeoLift_Test dataset included in the GeoLift package for this purpose. The database comes with resultant sales after the 15-day test period. Ideally, the GeoLift() function takes the GeoLift data frame as an input along with information about the test. For example, the cities in the treatment group, the time the test began, and the time it concluded.

Benefits of Using GeoLift to Measure Incrementality

Help Brands Invest in Data-Based Decision-Making

Advertisers often face myriads of issues with their measurement frameworks. One of these challenges is a tendency for brands to spend a bulk of their budgets on one or two digital channels, making it more challenging to determine the ROI of investing in new approaches. GeoLift can be a valuable tool to overcome these challenges. A data-based approach of ad performance can be achieved because ads are served to a subset of people and not a similar subset. By controlling other factors that could influence performance, lift tests enable advertisers to measure instrumentality and determine the direct impact caused by marketing activity.

Evaluate Media Mix More Effectively

When it comes to lift studies, brands often have a tendency to focus their spending on one or two approaches only. This means that advertisers get only the picture of specific channel performance rather than overall performance. GeoLift tools help advertisers maximize the benefits of lift testing by delivering a comprehensive view. Because geo lifts allow you to apply geographical boundaries to test and control groups, you gain the flexibility needed to understand better the performance of different media sizes rather than just individual channels.

Help Understand the Incremental Impact

GeoLift can help marketers understand the incremental impact of their campaigns vs. the budgets allocated. Determining the ROI of investing in new marketing approaches can be challenging for most advertisers using traditional techniques. Lift tests help brands understand relative channel performance and effectively compare diverse media mixes.

How to Implement a Geo Experiment

Follow the steps outlined below to set up a geo experiment:

Step 1: Geo Identification

Your first step is to determine the coverage area you would like to run your test at. For example, if you are targeting the entire US market, it may make sense to have states as your geos. If you are targeting a specific state market, consider leveraging DMAs or zip codes further to scale down your region of interest to smaller units. To achieve robust statistical measurement, avoid the temptations to select geos that are too small such as the zip codes. Ideally, people tend to move across geo-boundaries meaning the volumes of conversions will be too low with small geos.

Step 2: Geo Randomization and Assignment

After defining the market and its partitioning, the next step is randomly assigning each geo to the treatment or control group. Robust randomization ensures minimal differences between two groups aside from the ad serving. This function prevents possible biases. This step ensures the treatment and control groups are as similar as possible before you launch your campaign. Unfortunately, differences in geos may be prevalent, and if not controlled, it can hurt the design and accuracy of the test. It is advisable to do a preliminary analysis to determine the best geos to incorporate in the treatment and control group to avoid this.

Step 3: Measuring a Geo Experiment

After you set up the experiment, use econometrics methodologies such as Difference in Differences or Synthetic Controls to quantify the ad incrementality. This is basically a critical step in determining the number of conversions that would not have happened if the ad was not serviced.

GeoLift: FAQs

Do I Require User-Level Tracking to Run Activity in Specific Locations?

GeoLift works by dividing the country geographically to make a control/test split. This means that it won’t require user-level tracking. All you need to run GeoLift successfully is the ability to only include activity in specific locations. After running the test, you will determine whether your spending resulted in a significant uplift in the areas you spend compared to your control geos.

How Does GeoLift Testing Differ from the Standard Lift Tests?

A crucial question that most marketers ask is how GeoLift testing is different from standard lift tests that ran in the past with Facebook. Ideally, with the old way of making conversation, lift tests on Facebook compared control vs. exposed at that moment in time. With GeoLift, we evaluate what we anticipate would happen had ads not occurred using pre-test period data relative to the control. We then calculate the difference between what would not have happened and what actually happened during the test. Another key difference is that lift tests are more comprehensive and complicated than the standard lifts tests you may have run in the past.

What Are Facebook’s Brand Lift Tests?

Brand lift is a type of lift test where you utilize brand polling and other brand awareness measurements to gain an understanding of the true value of your Facebook advertising and how well it performs independently of your other marketing approaches. Brand lift works by choosing a representative sample of people who qualify to see your adverts. The sample group is further subdivided into the test and holding groups to allow for causal inference techniques to be used to measure the impact of advertising activity.

Key Takeaway

GeoLift is a powerful lift measurement solution and an ideal tool to analyze your advertising strategies. It can help businesses gain greater insights and data-backed decisions while course-correcting their marketing methods. GeoLift is designed to be relevant in the ever-changing advertising ecosystem, thereby helping brands correctly quantify the effect of their marketing. In a nutshell, GeoLift delivers an effective way for brands to move ahead into the future and overcome typical marketing challenges common with traditional techniques

If you have further questions or need help to get started with this, too, don’t hesitate to contact us.

When Apple released its new iOS 14 mobile operating system last year, many predicted a comprehensive change in the advertising industry. And they were quickly proven right; less than one month after its release, one report showed that 96% of U.S. users opted out of having their actions tracked by apps like Facebook.

In the age of data privacy (and the way Apple designed the opt-in prompt), that’s not necessarily surprising. But it proved to be a major challenge for advertisers, who could no longer rely on data from ad networks like Facebook to learn about their users, track conversions, and optimize their campaigns accordingly.

The most commonly cited adjustment seemed simple: brands and advertisers would have to rely on other means to gain that much-valued first-party audience data. But of course, a step like that is easier said than done. Paid ads had always been the best way to acquire this type of data at scale. Nearly one year into the App Tracking Transparency feature, here’s where we stand–and how brands can continue to mitigate the impact of iOS 14.

The Challenges With First-Party Data Strategies

Hypothetically, shifting your focus to more first-party data from your own online presence, like your website and app, makes intuitive sense. Brands that can gain information from their audiences first-hands don’t need to rely on third-party app tracking for insights and optimizations.

Unfortunately, that approach also comes with a significant limitation: Relying on your own first-party data only works with audiences who are already invested in your brand. In other words, they will only navigate to your app and website, and provide information to you, if you have built a significant amount of credibility with them already.

While that solution might work for brands with significant cache and brand recognition, it’s a more difficult proposition for niche businesses and industries in which awareness is just as crucial as the final conversion to customer.

The resulting suggestion, then, was to shift to more organic marketing efforts like content marketing, SEO, and organic social. After all, the argument goes, these more value-based means of gaining awareness and attention can effectively build credibility, making audiences want to provide you their information in the process.

Organic and content-based digital marketing tactics certainly play a significant role in almost any comprehensive strategy. At the same time, they fall short in replacing one important piece of the paid ad equation: the halo effect of paid digital when it comes to reach.

The Halo Effect of Paid Social on Other Digital Tactics

Here’s an important truth: digital marketing never exists in a vacuum. Every tactic has at least some secondary effect on others, all working together to reach your marketing goals.

Facebook, for example, found that in addition to driving their own traffic and conversions, digital ads resulted in a 19% comparative lift in organic search traffic and 10% lift in paid search traffic.

But that’s not all. Another study on ad lift found that Facebook ads drove more quality traffic, and lower-cost leads, through channels like search even when the results didn’t come directly from its ads. The same study estimated that as a result, last-click attribution undervalued the effectiveness of Facebook ads by almost 50%.

The takeaway: running paid social ads doesn’t just come with its own benefits. When used the right way, it can lift the effectiveness of other channels as well, even when its own results don’t show it. The more qualified users a Facebook ad reaches, the more qualified search volume those ads drive.

Take that away by focusing on organic channels only to gain credibility and first-party data, and the results could be devastating. It doesn’t help, of course, that thanks to the iOS 14 update, the cost of CPM has been steadily rising while reach has been steadily falling at the same time.

Why Better Creative Is Not Enough to Mitigate Lost Reach

In the months since the initial release of the iOS update, one thing has become clear: especially on social media, reaching your audience has become more expensive.

Largely because of shrinking audiences due to users who have opted out of data tracking and the comparatively higher value of in-feed apps due to a shrinking audience network, CPMs have risen steadily and across industries. One study saw a 30% CPM increase, while another study of 350 Facebook advertisers spending more than $10,000 monthly saw a 29% year-over-year increase.

As long as budgets stay the same, a higher CPM naturally leads to a lower reach. One common solution has been to improve creative and landing pages, which is certainly commendable. But while that might lower acquisition cost and somewhat lessen the negative effects on direct conversions, it doesn’t completely eliminate the issue.

One problem still remains: losing ad reach has a knock-on effect on the rest of your strategy. We saw this for a couple of our ecommerce clients, who were able to improve their cost-per-acquisition and maintain a positive performance on their paid Facebook strategy–but, because of the above-mentioned halo effect, saw a decrease in their organic search and social reach at the same time.

Better creative, and better landing pages, can only go so far. As CPM continues to increase a search for more sustainable solutions to mitigate the negative effects of iOS 14 will only become more imperative.

Two Solutions to Acquire More Qualified First-Party Data at Scale

Especially for marketers hit hard by the data lost in the wake of the iOS 14 update, adjustments should be a part of the daily agenda. Unfortunately, no adjustments can fully replace the data benefits that had become such an important benefit of paid social. However, two potential solutions can at least mitigate the impact, keeping savvy marketers on the continued path to success.

1. The Potential Promise of Facebook Conversion Leads Optimization

The first option is a Facebook Ads optimization for conversion leads. As described by the platform, this setting “optimizes to increase the quality of your leads” by showing ads to Facebook users most likely to become customers.

Think of it as the next step from conversion optimization. Marketers can install a conversion pixel in their CRM, which allows Facebook to analyze common characteristics of leads who keep an active funnel trajectory. The marketer determines the stage at which a lead is considered to be “high quality,” and the platform delivers the ads to the people most likely to reach that stage.

Crucially, this is a different set-up from Facebook’s Offline Conversions API, which only tracks actual conversions. Conversion Leads Optimization doesn’t just measure these conversions, but actively optimizes the campaign’s targeting and bidding to maximize the chances of reaching them.

This potential solution is especially relevant for lead ads, which drive new contacts into your CRM. Through the Conversion Leads Optimization, your campaign self-optimizes for a purchase or sales-qualified lead down the line, ultimately leading to more qualified first-party data, cheaper campaigns, and more relevant results.

The downside, of course, is that this approach is an alternative for the lost reach due to iOS 14, but not a direct replacement. Similarly to improving your creative and landing pages, your reach will still be lower, mitigating the halo effect described above.

It’s also important to point out that this option lends itself to long sales cycles. If your lead funnel is short or operates at a high velocity, Facebook will have fewer touch points and qualifications to optimize for.

2. Leveraging Emerging Channels Like TikTok for Greater Reach

To replace that lost reach, the other alternative is branching out. That means finding an alternative ad channel with a lower CPM, similar scale, and well-qualified traffic. One year in, one potential option stands out above the others: TikTok.

You likely know TikTok as one of the fastest-growing social media networks in the world, surpassing 1 billion global monthly active users earlier this year. Its advertising model and adoption rate are still developing, leading to potentially significant opportunities for brands and marketers willing to jump on board.

In our experience, CPMs on TikTok campaigns have been up to 50% lower than similar campaigns run through Facebook, making this an increasingly relevant alternative for many industries. Conversion rates have also been slightly lower, but without decreasing at the same rate as CPM.

In other words, a more active paid presence on TikTok allows brands to reach more people at comparatively cheaper rate, with conversion drops that are insignificant enough to sufficiently fill the funnel and increase first-party data more efficiently.

Of course, TikTok also comes with some limitations that are important for advertisers to keep in mind. While the platform has found increased adoption across demographics in the past two years, its core audience still skews relatively young, with 60% of its U.S. users between the ages of 16 and 24.  B2B brands looking to reach seasoned business professionals may struggle more to do so on TikTok than they would through their traditional Facebook campaigns.

Still, this solution is a viable alternative to at least partially replace the halo effect and reach lost by Facebook campaigns over the past years. And, like TikTok, other emerging channels may lead to similar effects in the near future.

Will New Tactics to Gain First-Party Data Negate the Negative Effects of iOS 14?

To summarize, both lead conversion optimization and newer channels like TikTok can at least mitigate some concerns that the new tracking restrictions of iOS 14 have brought with them. It’s important to note, though, that neither can completely offset the impact of iOS 14 on both organic halo effects and first-party data collection.

While better and more efficient than most optimization alternatives, Facebook’s conversion leads optimization is not likely to outperform a conversion campaign from pre-iOS 14 times in terms of effectiveness. Meanwhile, TikTok has been impacted by Apple’s update as well, and may never reach the same level of efficiency, or same broad audience potential, that Facebook had before the iOS update.

Neither of these solutions are antidotes to Apple’s App Tracking Transparency update. Marketers who expect them to be are doomed to fail. But that doesn’t make them inefficient. Their ability to mitigate at least some of these negative effects still make them worth implementing in any comprehensive digital campaign.

The reach, user time on platform, and user behavior make TikTok well worth a test within your larger efforts. Given more time to grow, it may even approach Facebook’s scale and efficiency at some point in the long-term future.  For now, though, it’s an intriguing channel with the potential for decent current results that may position you as an early adopter as the platform continues to grow.

Facebook conversion leads optimization, meanwhile, works well as a late-funnel complement to other, more reach-based campaigns. If nothing else, it allows you to reach more qualified users at scale, with the needed efficiency to improve your first-party data collection. 

The Future of Digital Marketing in the Age of iOS 14

Nearly a year into iOS 14 and the first-party data collection challenges it brought with it, we’re beginning to see which of the many recommendations from last year are actually starting to bear fruit. Ultimately, though, one thing remains clear: digital ads are not likely to approach the effectiveness of previous years anytime soon.

That, in turn, makes one truth of digital marketing even more important: continuous testing and an ongoing improvement mindset is absolutely vital. As Facebook ads continue to see reach challenges and users continue to opt out of data collection, marketers who are willing to continue examining their channels, tracking their effectiveness, and pivoting or experimenting when necessary will see a major advantage in the long run.

That might lead to testing out emerging channels like TikTok, or new conversion opportunities within established channels like Facebook. In isolation, neither is likely to simply replace the effectiveness of campaigns from early 2021 or before. But, when backed by data and as part of a larger strategy, they continue to ensure that advertising dollars are spent efficiently and towards optimum ROI.

There are many debates when it comes to Facebook bidding. Should you use manual bidding or the lowest cost? What is the difference? And, most importantly, which one is better for your business?   

In this post, we will discuss what manual bidding is and how it helps maximize your conversions at your CPA on unlimited spend. We will also look at what lowest cost/auto bidding does, as well as give examples of the two types of bidding.  

What Is Bid Cap and How Can It Help Your Ad Strategy?   

A bid cap or manual bidding is a bidding strategy that allows you to set a maximum cost-per-acquisition (CPA) threshold  for your ad campaign. With this setting in place, Facebook will never bid more than the amount you specify, even if it means your ads don’t show as often. This is a great way to make sure you never go over your desired target.   

When using bid cap, Facebook will automatically adjust your bids up and down to get as many conversions as possible while still staying within your set bid. This way, you’re guaranteed to get the most bang for your buck and never go over target. So, in short, bid caps help maximize your conversions at your CPA on unlimited spend. 

What Is Lowest Cost and Can It Help Your Ad Strategy? 

In contrast to manual bidding, lowest cost/auto-bidding is a bidding strategy that automatically sets your bids to get you as many conversions as it can while spending your budget in full and as evenly as possible throughout the day. While this may seem like the best option at first, it’s important to note that auto bidding doesn’t always get you the most conversions. Sometimes, it can lower your conversion rate due to how Facebook calculates its bids.   

Example on Lowest Cost Ad Strategy   

If you have a budget of $28K/day, Facebook would need to make sure that it can invest that money throughout the day, even in the later hours. This is because no one wants to see their budget used up by 8 pm. You might be skeptical still because if you graphed out your spend by hour on the lowest cost, you might see a chart like below.  

For example, you might be seeing a massive number of low-cost conversions coming in at 10 am. You may want to capitalize that, but Facebook can’t continue to invest simply because they need to make sure there’s enough budget to go around for the remaining 14 hours of the day. 

Example on Bid Cap Ad Strategy   

If you now decide to shift to a bid cap campaign and increase your budget to $40K/day. Facebook will still want to spend $40K/day in full. But our bid cap will put a constraint on its ability to spend all of that money. The bid cap will limit Facebook from spending if you’re not hitting your CPA target. 

In the prior example, your 10 am slot was limited by your budget. Now, your non-10 slots are limited by your bids. At 9 am, for example, Facebook might want to be spending 2X more, but it can’t. Your bid is constraining delivery.

Facebook has more room to spend and still wants to spend the full budget. Unlike 9 am, it can deliver more ads within your bid now. So it’s going to spend as much as it can without going over that bid. With bids, your conversion volume and pacing might look like this for the day.   

What Is an Inflated Budget?  

An inflated budget is a budget that’s higher than your average daily budget. This could be because you’ve increased the amount you’re spending on Facebook ads or because you’ve switched to manual bidding with a bid cap. Whichever way you choose to do it, an inflated budget will help ensure that you’re getting the most out of your ad spend. It can be 10X more. Whatever you’re comfortable with. The idea is you should never spend your full budget on any given day.   

Benefits of Running Bid Cap Campaigns with Inflated Budgets   

When you run a bid cap campaign with an inflated budget, Facebook will automatically adjust your bids up and down to get as many conversions as possible while still staying within your set CPA. This way, you’re guaranteed to get the most bang for your buck and never go over budget.   

Additionally, since auto bidding doesn’t always result in the most conversions, running a bid cap campaign with an inflated budget will help to maximize your conversion rate and get you more leads for the same price.   

If you’re running bid cap campaigns with the same budget you’d invest on the lowest cost, you’re using them wrong.     

Can You Move from a Daily to a Weekly or Even Monthly Budget Using a Bid Cap?  

Bid caps help ensure that you’re getting the most out of your ad spend, so it’s advisable to use them if you can. But as much as you be on a tight budget and may want to consider moving to a weekly or monthly budget instead, it’s not quite advisable. It may make sense to use them that way, but it is hard to execute in practice. Reasons being: 

1. Advertisers need to use lifetime budgets to achieve weekly/monthly budgets.   
2. Most DR advertisers use daily budgets because they need more control over performance if things are good or go off the rails. 
3. Since most people don’t know how to use bid caps, they adjust their daily budgets with the lowest cost bidding based on performance instead.   
4. It will be harder to adjust a lifetime budget on the fly if you see good/bad performance. These adjustments have become even more important post-iOS 14.6, where Facebook reporting isn’t as reliable. You’d also likely need to constantly make new campaigns to make this work.   
5. Also, theoretically, a daily budget is a weekly budget because it can spend +/- 25% of the budget any given day and will average out to your daily budget, correct?   

Should You Replace the Bid Cap with the Lowest Bid?   

No, you shouldn’t replace the lowest bid with a bid cap. The bid cap is for scaling and should be run in conjunction with the lowest bid, not in replacement. In reality, it’s hard to find an equilibrium of bid that leads to proper CPA, especially post iOS 14.6. However, in general, using a bid cap can be more effective than relying on the lowest cost bidding alone.   

Which Bid Caps Bring Success?  

There’s no one perfect bid cap that will work for every advertiser. It depends on several factors, including your average order value (AOV), your ad spending, and your goals. However, in general, you should aim for a bid cap that’s around double your AOV. If you want to play it safe, start with a low bid and adjust up. The worst-case in this instance is no delivery to start.   

Generally speaking, to get the maximum result, you can aim to run an auto bid campaign and use that CPA you’re getting + 20% as your initial bid.   

Will Facebook Delayed Reporting Affect Your Strategy?  

Facebook says it needs 3 days to get data from iOS, which can make you question how the system will know when to spend at 9 am if it doesn’t get data for 72 hours.   

What you should note is that bid delivery isn’t based on conversions that have happened, but on expected outcomes. If there are a lot of cheaper users available who might convert at a certain time of day, there will be more delivery. On lowest cost, Facebook is just conserving delivery to ensure even pacing.   

Prior conversion data only helps calibrate the model.   

An inflated budget with a manual high bid cap removes the pacing component to help you enter more auctions.   

The cost control using bid cap allows the system to better understand your willingness to pay for a particular auction transaction and therefore has more freedom to partake in auctions that meet your actual, realized CAC requirements rather than an artificial pace.   

What To Do When You Use Bid Cap Campaigns but Get No Impressions  

If you get no impression unless you increase the CPA to an unprofitable level, consider: 

• Slowly creeping up CPA by 10% – 15% each day until you get delivery.   
• Be patient.   
• Using bid cap just as a way to scale beyond your lowest cost campaigns. They should be a complementary, not a replacement.   

Which Strategy Is Right for You?  

Manual bidding gives you more control over your ad spending, but it can be difficult to find the right balance of bids that leads to proper CPA. On the other hand, the lowest cost bidding is more automated and easier to use, but it can be less effective in terms of overall ROI.   

If you’re not sure which bidding strategy to choose, or if you want to get the most out of your Facebook advertising budget, consider using a bid cap campaign. With a bid cap campaign, you set a maximum bid that you’re willing to pay for each click or conversion, and the Facebook algorithm will automatically adjust your bids to stay within that budget. Your bid is what controls the slots. This ensures that you never spend more than you want to while still giving you the chance to compete in high-value auctions. 

Bid cap campaigns can be especially useful if you have a limited budget. You can get more impressions and clicks for your buck by bidding high enough to enter into more auctions. And, because you’re only paying the maximum bid if someone converts, you can often get a lower CPA than with lowest cost bidding.   

If you’re thinking of using a bid cap campaign, there are a few things to keep in mind:   

• First, make sure you set your bid cap high enough to enter into the auctions you want to compete in. If you set it too low, you may not get any impressions at all.   
• Second, be patient. The Facebook algorithm can take a few days or even weeks to learn your new bidding strategy and start delivering results.   
• Finally, keep in mind that bid cap campaigns should be used as a way to scale beyond your lowest cost campaigns. So if you’re getting good results with your current bidding strategy, don’t switch to bid caps just yet – try using them in addition to what you’re already doing.   

Manual Bidding vs. Lowest Cost Bidding: The Takeaway 

Both manual bidding and lowest cost bidding have their pros and cons, but bid cap campaigns can be a great way to get the most out of your Facebook advertising budget. By entering into more high-value auctions, you can get more impressions and clicks for your money. And, because you’re only paying the maximum bid if someone converts, you can often get a lower CPA than with lowest cost bidding. So if you’re thinking of using bid caps, give them a try – you may be pleasantly surprised by the results. 

Not sure how to get started with bid cap campaigns? We can help. Contact us today, and one of our experts will be happy to walk you through the process. We are sure you will find it a valuable addition to your Facebook advertising strategy.   

Facebook’s Dynamic Catalog Ads have long been a much-loved and regularly-used tool for advertisers on the platform, especially in the eCommerce industry.

The ability to auto-generate and optimize ads thanks to Facebook’s incredible machine learning and algorithms was exceptional; you could create a single ad template, connect your catalog, and have Facebook drop images and text into the template that individual users will be most responsive to.

Now, though, there’s a catch. Catalog ads unfortunately are no longer what they once were.

If you’ve been plugging ahead with catalog ads in the past few months and seen a sudden downturn, there’s a reason: iOS 14.6.

Right now, we firmly believe that brands shouldn’t be running facebook catalog ads since the update has rolled out, and in this post we’ll explain why and talk about what you can do to adapt your campaigns moving forward.

The iOS 14.6 Impact on Facebook’s Dynamic Product Ads & Dynamic Ads for Broad Audiences

Facebook has two commonly-used types of dynamic ads: Dynamic Product Ads and Dynamic Ads for Broad Audiences.

Facebook’s Dynamic Ads allow you to create ads from your product catalog, automatically plugging a product’s image and information into an ad template. Different users will see different products based on their past in-app activity, off-platform activity, and their past interactions with your business.

The Dynamic Product Ads (DPA) utilize remarketing audiences to show relevant ads to people who have visited your site or your app, previously engaged with your ad, or interacted with your business in some way.

Dynamic Ads for Broad Audiences (DABA), on the other hand, you can show dynamic ads to users who align with your customer profile even if they’ve never visited your site or app.

Both use product catalogs, and both require the pixel to be installed. You can choose product categories within the catalog and choose what ad formats you want to use.

Facebook was extremely clear that Dynamic Product Ads would be impacted by iOS 14.6, which made sense given the heavy retargeting focus of the campaigns. They failed to warn advertisers about the impact on DABA, however.

How DPA Was Impacted

We knew early on that DPA would become a fast casualty, as Facebook’s remarketing pools were largely focused around content and page viewers. The ability to track off-Facebook, on-site activity like page views and adds to cart would be significantly reduced from Apple opt-outs.

There were no surprises there, though this was deeply disappointing since DPA had previously been such a valuable resource for brands with large product inventories who were letting Facebook’s algorithm do what it does best: Take charge and drive conversions.

How DABA Was Impacted

Facebook had initially recommended moving your campaigns more towards using dynamic ads with broad audiences, so at first it seemed like DABA would be just fine.

enough users and data to offset challenges and lack of data resulting from Apple’s update. We were wrong, however; these campaigns were impacted, too, as enormous numbers of Apple’s users downloaded the update and large portions of the data got knocked off kilter.

What This Means for Your Campaigns

We know from experience that things started shifting rapidly once the updates rolled out. Product catalog ads, to say the least, were no longer the powerhouse they once were.

When iOS 14.6 launched, our catalog ads started to shift ad spend from our clients’ best selling products to cheaper products almost overnight.

At the same time, we couldn’t see catalog breakdowns. This meant we couldn’t check to see if Facebook was actually driving sales to these products from the ads.

The backend data was showing that sales were tied to lower cost products, and we wanted to course-correct, showing customers more profitable SKus.

We ultimately removed lower price point items from the ad campaigns, focusing exclusively on our top-performing and high-value SKUs.

Performance did improve slightly, but it was still lower than expected… and significantly less than before. Product delivery still felt random as opposed to targeted, and that’s because it was.

What Brands Should Be Doing Instead

The first thing that advertisers should do right away is to steer more of your ad campaigns away from dynamic product ads. You can create ad campaigns for high-value and best-selling products manually if needed to ensure they’re getting the reach that they need to.

And if you are going to continue using product catalog ads, make sure that you’re grouping items carefully. Organize them into categories determined by price points and qualities like “best sellers” so you can run more niche campaigns and ensure that you’re getting eyes on your high-value products.

Additionally, the more Facebook can get on-platform signal about your products, the more likely the system will be able to find the right target consumers. Make sure that all of your product ads leverage the Instagram product tags so people can checkout in the Instagram app. Even if a consumer ends up buying on your website instead, all of the interactions on Instagram leading up to that sale (such as viewing the product or adding it to their Instagram cart) are valuable for the ad system to know what someone is interested in buying.

Final Thoughts

The iOS 14.5 and 14.6 updates have been anything but fun for advertisers, and it’s definitely impacted our campaigns and our clients in a major way while we adjust and find workable long-term solutions.

And the good news is that there are new solutions; you just need to know how to find them and how to adapt your existing campaigns to stay profitable in the new advertising environment.

Need help adapting your brand’s campaign and strategies to the post iOS 14.6 world? Get custom help revamping your strategy here. 

Bidding is sometimes treated like an afterthought when it comes to Facebook Ads, but it can actually have a significant impact on your campaign’s success.

Small decisions like whether or not you want to implement cost or bid caps can affect your ad deliverability and the quality of the audiences you’re seeing and how much you’re paying. These all can directly affect your bottom line and your ad’s potential long-term scalability.

If you’re not sure where to start, don’t worry. In this post, we’re going to break down the 6 different Facebook Ad bidding strategies you can choose from, including when to use each and what you need to know post-iOS 14.5.

Why Bidding Strategies Directly Impact the Performance of Your Campaigns

Selecting your bidding options requires you to actively click to view options aside from the default. This may make it seem like a niche feature, it can directly determine how Facebook will serve your ads considering the budget that you have and, in some ways, how aggressive or conservative you want to be with your bids.

Some bid types, for example, prioritize the cheapest conversions possible. While this can seem appealing at first, it can mean that low cost is prioritized over high quality. This can ultimately backfire, depending on your goals, your audience, and other contributing factors.

Knowing which bidding strategy is crucial to help you get the results you want at your target costs without sacrificing quality. Let’s take a look at the six different Facebook Ad bidding strategies and when to use them.

1. Cost Cap 

The Cost Cap is a Facebook Ad bidding strategy that focuses on hitting your average target acquisition cost.

Here’s how it works: In the example below, I set a cost cap of $5.

If I have an unlimited budget, Facebook will acquire all of the users in the purple box. All of the conversions below the dotted line are $5 or lower, the total CPA is less than $5. Some will be $5 even, but some may be $2 or $3.

Since some CPAs will be $3, this means that Facebook can then bid $7 for higher quality placements, as long as it all evens out to around $5. This allows you to acquire more customers while still hitting your goal.

Because this option gives you the chance to acquire higher quality leads while still sticking to an overall CPA goal to keep your costs on track.

It’s best to use this Facebook Ad bidding strategy in the following circumstances:

• You have a certain acquisition cost target and no concerns about going slightly over once in a while as long as performance averages out; this is common for many brands
• You’re running catalog ads with little or no price variation between products
• You are sending people to landing pages where the average order value is relatively predictable (like a product page, or a landing page with a single product)

2. Lowest Cost 

The Lowest Cost Facebook Ad bidding strategy prioritizes acquiring as many cheap conversions first as possible within your budget. Then, once all of those cheap conversions are obtained, they’ll then go after more expensive conversions.

In the graph below, they’ll start with acquisitions in the green area of the map before moving to yellow and then to red.

If your budget was unlimited, there would actually have no upper limit for what you’d be paying per CPA. Budgets, however, do limit delivery. As a result, Facebook uses optimal bids in the background to get you the lowest cost conversions possible while hitting your budget.

This bidding strategy can work well in either of the two following scenarios:

• You’re running catalog ads that have very little to no price variance between the products you’re advertising
• You’re promoting landing pages where the average value is relatively predictable (like a product page, a landing page with one product, or a page with a singular offer)
• When you have a budgeted amount planned with no intended acquisition cost target

3. Bid Cap

When you implement the Bid Cap bidding option, Facebook will deliver your ads to users who are most likely to convert as long as they cost less than or equal to the bid cap that you’ve set.

Facebook will not show your ad to any users that will cost above your bid cap to acquire, even if you have plenty of acquisition opportunities that come in far under the bid cap. They’ll start with the cheapest customers, and then expand to more expensive opportunities, but they’ll all fall at or under your bid cap.

In this bidding strategy, Facebook will always keep your average CPA below your bid cap. In the example above, I set a bid cap for $5 and an unlimited budget. Facebook acquired all of the users in the purple box under the bid cap, keeping my CPA under $5.

4. Highest Value 

The Highest Value Facebook bidding option isn’t about bidding in the highest ROAS; it’s actually about connecting you with the biggest spenders within your budget, and to then go after lower spenders (or lower value audiences).

In this graph, the high value spenders are going to be in the green, which is where Facebook starts for this bidding option. They’ll move to the left through yellow, orange, and then red.

Bidding for the highest value isn’t about ROAS, but it can actually correlate closely with a stronger ROAS. While these customers often cost more to acquire, they are more likely to purchase and to purchase more than the average customer. This often far outweighs the increased conversion cost.

The biggest challenge you’ll want to consider with this bidding option is that there are typically fewer users in this segment available, making them much more competitive in the auction. It’s possible that you could reach a smaller segment of your target audience here.

That being said, this is a great Facebook Ad bidding strategy to use in the following scenarios:

• You’re running catalog ads with products that have a large variance in pricing
• You’re sending users to landing pages where people have the option to spend a various value based on what they choose to convert on, like if they choose to purchase a more expensive item or package; this may include product pages with the ability to upsell
• You have a budget you planned to spend with the goal of driving as much revenue as possible per customer without regard to acquisition cost

5. Minimum ROAS 

The Minimum ROAS bidding option allows you to set a minimum Return On Ad Spend level that you want to achieve. Facebook will then limit ad delivery to the people who are more likely to generate revenue at those levels in order to reach that minimum ROAS goal.

A higher ROAS target is going to be tied to higher conversion values, which can mean lower delivery. A lower ROAS can give Facebook more leeway to scale the delivery.

In the example here, I set a 2x ROAS target. Facebook gives me the conversions in the purple box, which you can see are in the green and yellow zones. While there were additional conversions that will be above the ROAS target, Facebook is less likely to deliver to these users because they’re not sure if the value will get me to my target ROAS.

Even though Facebook is aiming to hit a minimum ROAS, it’s basing the ROAS estimates on conversion value… not on conversion value relatively to the CPA. If, therefore, I want to acquire more of these conversions, lowering my minimum ROAS below a 2x would be helpful. Adjusting your minimum ROAS is going to be a key part of the results you get here.

Here’s when I recommend using this bidding strategy:

• You’ve got a set budget you’d like to spend with the goal of driving as much revenue as possible per individual customer above a certain threshold and without regard to customer acquisition cost
• When you’re running catalog ads that have large price variance between products
• If you are using landing pages where the average order value can vary significantly, like a home page or a product page with the ability to upsell

6. Highest Value with iOS 14.5 Opted-Out Users Taken Into Account  

This is the Highest Value bidding strategy with iOS 14.5 users opted out. There are plenty of reasons why iOS 14.5 is a concern for advertisers, which can include reduced tracking and optimization abilities.

With this option, Facebook is now limited to looking at ROAS in 4 large value sets (which can be expanded to eight) instead of the actual value spent per customer because of the tracking concerns

In the past, Facebook could distinguish between these 7 different orders all with different values of $1, $2, $3, $6, $8, $12, and $14. Now, though, Facebook can’t distinguish order values granularly anymore.

These same values would be sorted into estimated buckets; three $2 orders, two $7 orders, and one $13 order. It’s important to be aware of this so you can ensure that your campaigns are still profitable.

Final Thoughts

As you can see, the Facebook bidding strategy that you choose can directly impact what types of users you’re attracting, the success of your campaigns, and your overall ROAS.

Do you want the most number of clicks possible? Great. Would you rather focus on investing in higher value users? This can work exceptionally well, though you’ll be paying more.

You can always split test bidding strategies just as you’d test any other aspect of your ad campaigns, but knowing which will be best for your specific campaigns and goals is always a good start.

As digital advertisers, the reality is that we are all at the mercy of the larger technology landscape. Apple is one of the organizations whose moves can make a big impact on the overall advertising ecosystem. They rolled out the new App Tracking Transparency prompt on April 25th, 2021, as part of the release of iOS 14.5. Facebook advertisers everywhere had questions about their future – but were ultimately relieved. The effects of iOS 14.5 felt cosmetically different but performance overall was relatively stable.

In the beginning it only looked like Facebook’s reporting shifted dramatically. For example, measurement was attributed differently: to the day of conversion instead of the day of impression. Additionally, the ability to look back at longer windows was removed as well as breakdowns for demographics like age and gender.

Even with these reporting changes, though, performance remained relatively consistent and some advertisers breathed a sigh of relief. Maybe these new changes would offer only a mild inconvenience?

iOS 14.6 is a Different Story for Advertisers

Anyone who was hoping for the same outcome following the iOS 14.6 release is likely to be disappointed. The release happened on June 3rd, 2021, and we began to notice Facebook advertising performance degrade seriously across our client accounts. Some of our clients saw their customer acquisition costs increase by as much as 50% overnight!

What made the release of iOS 14.6 so dramatically different and why did it impact Facebook this time in a way that the release of iOS 14.5 did not?

Although Apple typically releases an automatic update when they roll out a new version of iOS, that wasn’t the case for version 14.5. Instead, Apple slowly rolled out 14.5. It took Apple nearly one month for 20% of their user base to upgrade to iOS 14.5 as a result.

However, when Apple rolled out iOS 14.6, they triggered an automatic update for users. In less than a week, adoption for iOS 14.6 and it’s privacy features was logged at over 60%.

At a 20% adoption rate, Facebook auction was less impacted by these changes to how they could track users. However, when Apple flipped the switch on pushing people to iOS 14.6, Facebook tracking started to degrade immediately.

We performed an analysis for our Facebook advertising clients, in order to understand what percentage of their backend sales Facebook tracked before iOS 14.6 launched, and after iOS 14.6 launched. Here’s what we found:

– Prior to iOS 14.6, our advertisers saw Facebook’s pixel capture roughly 80% to 95% of all backend sales.

–  After iOS 14.6 launched, our advertisers saw Facebook’s pixel capture only 60% to 70% of all backend sales.

In a best case scenario, that means Facebook saw a 12.5% signal loss.

In a worst case scenario, that means Facebook saw a 37% signal loss.

This signal loss stems from Facebook no longer being able to track a large portion of their iPhone users. (Any iPhone user who chooses to not be tracked by Facebook will no longer be accounted for in reporting or conversion optimization efforts).

Even though this tracking gap first occurred with iOS 14.5 users, the performance impact was far greater with iOS 14.6. Simply put: up to three times as many people are no longer trackable on iPhones. This is particularly impactful since Facebook uses these signals to help find more users that are likely to be interested in your product or service, creating more efficient campaigns over time.

Every data point matters to how Facebook optimizes your campaign, not just the ones attributed to ads.

Expect Massive Changes to Facebook’s Conversion Optimization Model

Regardless of the size of the advertiser – unlike with the initial 20% adoption of iOS 14.5 – this 60% adoption rate of iOS 14.6 for iPhone users resulted in a significant change to Facebook’s conversion optimization model.

Here’s the bottom line: with less signal, Facebook struggles to find people that are likely to convert. And the more signal lost, the more likely Facebook will struggle to drive cost efficient performance.

What does that mean for you?

If you are an advertiser who drives a higher volume of transactions – even if your reporting is 37% lower – the impacts on your efficiency will be less exaggerated than an advertiser who generates a lower volume of transactions with less signal loss.

For example, if you reported 1,000 backend transactions in one week, and Facebook now only sees 630 transactions, your Facebook reporting will look worse. Keep in mind, though, that actual performance will be less impacted because Facebook still has sufficient data to optimize efficiently.

However, if you have 60 backend transactions in a week but now Facebook only sees 48 transactions (a 20% loss), this could impact Facebook’s ability to optimize in the way that it used to. Besides underreporting, you might also be less efficient.

We suggest you measure this yourself as you continue to evaluate these new changes. Here’s a video recording to show you how to pull this data in Facebook:

For example, if you pull purchase data from June 1 through June 15th on Facebook, pull the same purchase data in your backend system for the exact same dates.

Divide your Facebook data by your backend system data to understand your fire rate. For example, if you had 50 purchases in Facebook and 100 purchases in your backend, your fire rate would be 50%.

Additionally, these changes are also causing greater discrepancy between Facebook ROAS and Shopify ROAS. We compared week over week data and there has been a jump in the difference between Facebook and Shopify ROAS since iOS 14.6 launched. Prior to June 4, there was a ~30% difference between Facebook and Shopify ROAS. That has since increased to 50%.

All of this information may seem alarming at first, but we do know that these numbers are inline with Facebook’s predictions of how performance was expected to play out.

While Facebook stated they “do not expect immediate noticeable impact to iOS event reporting on the day of enforcement,” they also stated that users “may see greater impact to delivery and reporting, resulting in performance fluctuations and increased CPAs” as adoption of iOS 14.5 increased.

Not the New Normal for Facebook Advertising

As Facebook updates their auction models to adjust for this new privacy centric approach to advertising, things will start to level out.

In the interim, there are some steps you can take to adjust how you approach both media buying and measurement, including:

– Optimizing campaigns over longer time horizons. For example, we are now using a weekly testing cadence to ensure we have enough sales volume to make more informed decisions around creative iteration.

– Implementing additional measurement techniques. Along with using Facebook’s reporting, we are starting to use Shopify data and post-purchase survey data to better help inform Facebook’s influence in the buy-flow.

– Consolidating even more. With every data point being more crucial for an ad set to optimize, we’re shifted how we think about targeting and campaign build-outs. For example, we’ve shut off retargeting for clients with smaller site visitor audiences and instead let our prospecting campaigns target these site visitors instead.

– Shifting from value optimization to conversion optimization. While Facebook used to understand the value of a customer from infinity all the way down to a penny, now they only have at most 8 inputs based on value ranges instead (and many advertisers are only using 4 inputs!). Transitioning from infinite value signals to 4 value sets is a big change to an optimization lever meaning the ability to drive higher return on ad spend has been significantly diminished.

While it’s still unknown how long this volatility will last (and things might get worse before they get better), we don’t expect this to be the new normal for Facebook performance.

Already, Facebook has made updates and improvements to their conversion focused campaigns, including:

– Relaxing the 72-hour reset period for ad campaigns when an advertiser makes changes to their event configuration so that ad sets will not be paused.

– Expanding their use of conversion modeling to include it in the 7-day click default attribution setting.

– Providing additional flexibility for Website Conversion campaigns, allowing optimization beyond the top 8 prioritized events in Events Manager.