If you’ve found your way to this blog post, you’re likely like me in the sense that you’re curious about Google’s audiences and how they’re created. The exact algorithm which Google uses to create audience lists is a heavily guarded secret, much like the recipe of Coca-Cola and Colonel Sander’s Original KFC recipe.
Personally, I’m fascinated by the fact that Google can take audience lists, like custom match lists, for example, and use those to create “similar to” audience lists, allowing us advertisers to reach potential customers who may have never interacted with our brand before.
While I may not be able to provide you with an exact answer as to how Google determines audiences, I hope to provide you with some insights into Google audiences, as well as some actionable takeaways on how to use the audience lists Google provides you.
What is a Google Audience and Where Do You Find Them?
Where to Find Them
Before digging into the ‘behind the scenes’ audience stuff, allow me to take a moment to touch on the different types of Google audiences and where you’ll find them.
In order to effectively use audiences, you need to be aware of how to find them. You’ll find all of your audiences in the Audience Manager in your shared library. In there, even if it’s your first time there, you’ll find your audiences (Google automatically creates some basic remarketing lists for you), as well as the type of audience it is.
Furthermore, you’ll be able to see the size of each list based on which network that list is compatible with. As of now, there are four networks Google allows us to use audiences in. Those networks are Search, Display, YouTube, and Gmail. Not every audience list is compatible with each network, so be sure to check which lists are compatible with which network before you start building out your strategy! If you are ready to begin creating an audience, select the plus sign to get started!
What is an Audience
An audience is just one of the many ways Google allows us to better target our potential customers and our target markets. According to Google, “Audiences are groups of people with specific interests, intents, and demographics, as estimated by Google. You can select from a wide range of categories – such as fans of sport and travel, people shopping for cars, or specific people that have visited your site.”
Audiences are truly a powerful and valuable part of any paid search marketer’s arsenal, allowing us to better navigate the massive Search, Display, YouTube, and Gmail markets. You’ll be able to boost your campaign’s performance by reaching specific audiences that are outlined below.
Types of Audiences
Now, I’ll list the types of audiences that exist, as well as a brief description of each. Please note, this is not an exhaustive list, but these are the most commonly used list types.
- Affinity Audiences: Audiences to reach potential customers based on a holistic picture of their lifestyles, passions, and habits.
- Custom Affinity: Like an affinity audience but can be specifically tailored to better fit your brand.
- In-Market: Designed for advertisers focused on getting conversions from likely buyers. Reach consumers close to completing a purchase.
- Life Events: Reach people around important life milestones, such as marriage, graduation, buying a new home, etc.
- Custom Intent: Define your own audience, using keywords, URLs and/or apps related to products and services your ideal customer may be researching.
- Remarketing: Reach people who have previously engaged with your products/services.
- Website Visitors: A list of those who have previously visited your site. Specific criteria, like visitors of a certain page, can be used.
- YouTube Users: Users who view your video ads can be added to YouTube lists.
- App Users: Users that have installed your app on their device.
- Customer Match: These lists are generated based on user contact info (like email and zip codes) that you may have collected over time. You must manually input this info into Google Ads before they can become an eligible list.
- Custom Combination: Manually combine two or more existing remarketing lists.
- Similar Audiences: Google looks at your existing lists and provided there are at least 1,000 people in that list, creates a brand-new list of people that are similar to that list. This is a personal favorite list to find potential new customers. For example, you could use a customer list audience of previous purchasers emails in order to re-engage them and then use the similar audience created by Google to reach brand new customers.
- Detailed Demographics: Allows you to reach certain segments of the population that share common traits, such as college students, homeowners, or new parents.
It’s important to understand that not all audiences are created equal. Some require a lot of work on the front end to put together, like a custom match list. Some require you to have Google Ads remarketing tags placed on your site. Some audiences are not compatible with Search campaigns, while others are. Whichever audiences you have/are planning on creating, be sure to understand the pros and cons of each. Ready to start creating audiences? Check out this blog on how to create a killer custom affinity audience.
How Does Google Determine Audiences?
Without further ado, I present to you what I believe to be the algorithm which Google uses to determine audiences. Drumroll, please.
y = mx+b
Just kidding, that’s an equation of a line, if my memory serves. My apologies for being such a tease.
All jokes aside, now that we have a foundational understanding of Google’s audiences and the different types there are, I’ll share what I believe Google looks at when building out these audience lists (at least, the lists that we don’t create ourselves). I want to preface this by saying that this is nothing more than speculation on my part, after doing research to satisfy my own curiosity.
I’ll start by stating that Google uses machine learning to analyze what is likely a quadrillion (because a trillion isn’t big enough, probably) different signals and then turns those signals into insights. Those insights could be anything from a user’s purchase intent, user locations, average session duration, past search history, or anything else like that. Honestly, with a quadrillion different signals, this list of potential insights could go on forever. The point is, Google sifts through so much more data than we could ever hope to comprehend on our own, in order to build these audience lists.
I’m not sure about you all but knowing that there’s that many different signals being fed into the audiences makes me feel confident that the people included in those lists are all relevant.
As I mentioned earlier, the algorithm Google uses in order to analyze all of these different signals is a secret, not that I could comprehend the algorithm even if I saw it. So, I’m sorry to say that I cannot give you the algorithm, or anything even close to it, but I do hope that I have at least provided you with a better understanding of Google audiences, how they’re created, and maybe even a new idea or two on how to use them to your advantage. Happy PPC’ing!
Article updated by Brittany Sager (prior post date 9/12/18)
How Automation Hurts Rank, And How to Fix It
Imagine you are offered an opportunity to have control of all the creative, copy, and budget in your Google Ads account (or your paid media platform of choice) put in the hands of an anonymous six-year-old user. Each day, you are allowed to tell them whether they should or should not spend money, how valuable a conversion is, and nothing else. This is billed as a massive gain inefficiency, as they can handle all the tedious tasks while you focus on the bigger picture. Whether or not you get to keep your job depends on this user’s continued success. Do you feel just a hint of hesitation or fear moving forward with this plan? When put in those terms, probably so. Yet somehow, we all seem to have no problem implementing automated bid strategies across our accounts.
There’s no arguing that automated bidding can be an effective tool, but it’s a technology that gets plenty of undeserved credit too. Machines do their best work when they are performing rote, repetitive tasks. Humans do their best work when they’re thinking strategically, and a successful account requires input from both parties. However, there are few, if any, paid media platforms that provide an adequate framework for communication between both parties. In the PPC world, miscommunications can be costly. In this article, we’ll go through the role of automation in paid media, how that influences rank, why rank is relevant, and how to use the tools you have at your disposal to make automation work for you.
Automation’s Role in Paid Media
Saying that automation is ubiquitous in PPC would be an understatement. It’s no secret that as advertisers call for more depth and granularity, advertising platforms answer their demands by developing increasingly complex features that require more automation to use. This is not only pragmatic but wholeheartedly welcome. Letting machines focus on execution means we can spend more time on the activities uniquely suited to humans, like analyzing and strategizing. However, as our focus broadens, we start to lose oversight into the most fundamental aspects of our campaigns, such as our rank.
Why Rank is Relevant
Rank determines where ads are relative to other ads, but it also serves a much more vital function. It determines whether ads show up at all. Even though it serves the most critical function in an account, we still don’t talk about it all that often for a number of reasons:
- The term rank is sometimes thought of as synonymous with the average position. When Google sunsetted the average position metric in 2019, conversations surrounding rank became scarce.
- Increasing complexity in our advertising platforms is causing our attention to become fragmented. We don’t have the bandwidth to manage something that at least appears to be managing itself.
- When efficiency is our main metric of success, how our ads are serving is only as important as our ability to pace our account budget. If spending is fine, no questions are asked.
- We trust automation too much. If a campaign sees low volume, we assume it’s for reasons out of our control. We’re either reluctant to question if automation is relevant, or we’re so entrenched in it that we don’t even think about it in the first place.
How Automation Hurts Rank
There are valid reasons rank may not always be top-of-mind, and it might not need to be if it weren’t for the fact that the automated bidding strategies we use everyday bid far too conservatively, inherently limiting our volume. Then consider the compounding effect of a schedule of continuous optimizations that further limit and refine an account where volume is already limited. Campaigns using automated bidding will end up serving a mere sliver of the potential audience. Without specific attention put towards improving rank and scale, every optimization made on a campaign brings it an inch closer to irrelevance.
Making Automation Work for You
It is clearer than ever that those of us who manage paid accounts need to rethink automation. The question isn’t whether or not to use it. The benefits are clear. What we need to consider is how we can make better use of automation without artificially limiting our audience. There are a number of options to test, ranging from simple to complex:
- Testing unconventional automated bid strategies.
- Testing unconventional combinations of values and goals.
- Test for growth opportunity by (briefly) turning your account into a tessellation of micro-campaigns, then recombine them with updated settings.
Using any of the strategies above has the potential to positively impact rank and help you retain efficiency at scale, but the safest method for doing so is the one that allows for the most manual control. In this case, that involves taking management into your own hands by breaking your campaigns down into smaller components to give yourself the clearest line of sight into growth opportunities. It’s a technique we’ve employed across a variety of clients at Voro, which has helped them achieve impressive results.
If you’re interested in learning more about how to take back control from the machines, employing an effective mechanism for retaining efficiency at scale, how to manage stakeholders’ expectations even when performance doesn’t go according to plan, or how burritos and popcorn relate to paid media management, I’ll be going in-depth on these topics and more at PPC Hero Conference.
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Ad Fraud Warnings to Look Out for in 2022
Advertisers spend $455 billion in online advertising per year and $42 billion of it lost due to ad fraud in 2019, according to Juniper Research. In addition, the Wall Street Journal reported that 28% of all web traffic likely comes from “non-human” bots.
Why hasn’t ad fraud been stopped yet?
In short, it’s still difficult to detect.
Fraudsters are using far more sophisticated techniques today than in the earlier days of the web and a single advertiser can run millions of ad impressions across hundreds of websites, making it extremely difficult to spot irregularities at such a large scale.
It’s not a secret that ad fraud remains a major problem. But are advertisers aware of it and, if so, are they doing anything about it?
Fraud Blocker, a click fraud protection software, sent out a survey to PPC marketers to help answer some of these questions as they plan for their 2022 campaigns.
What is ad fraud?
Ad fraud is a means to defraud advertisers by using techniques that inflate the total number of ad clicks or views for financial gain.
With click fraud, malicious actors can employ robots or low-wage workers to repeatedly click on ads illegally. Unaware of the fraud, advertisers then pay for the clicks as if they were real humans with actual buyer intent.
Another type of ad fraud, impression fraud, is often done by serving ads in places that are invisible to the human eye. This can be done by stacking ads on top of one another, loading them in tiny iframes, or serving them in the background of a mobile application.
Here are a few of the most common types of ad fraud today:
- Ad Stacking: Multiple ads are stacked on top of one another where only the top ad is visible, however advertisers are charged for the non-viewable ads.
- Pixel Stuffing: A malicious publisher loads ads, or an entire website, inside a 1×1 pixel. The ads are non-visible to the human eye.
- Click Farms: Attackers hire a group of individuals whose job it is simply to click on ads throughout the day. Click farms use techniques that give the impression that each click is from a different user and device.
- Click Bots: One of the most popular methods of click fraud is done by web robots. These bots can be simple programs that click on ads repeatedly or they can be large operations that are installed with malware on user’s devices and click on ads unknowingly in the background.
- Location Fraud: The geographic location of ads are spoofed using a Virtual Private Network (VPN). This makes ads appear to be shown in a more desirable location, such as in the US, despite actually being shown in a less desirable country.
- Video Viewing Fraud: The popularity of video channels can be easily faked to appear more appealing to advertisers, much like social media followers. Advertisers ultimately end up paying based on the view counts which a large portion of may not be from real humans.
- Affiliate Ad Fraud: Fraudsters manipulate the cookies on a user’s device to wrongly credit an associated affiliate as the source of purchase without the user’s knowledge.
- Source Spoofing: The data detailing where an advertisement ran is altered to appear as a more trustworthy publisher or mobile app.
- Domain Spoofing: The domain name is changed to falsely appear as if the ad came from a more premium site, such as changing from junknewssite.com to WSJ.com.
Visit here for more details on the different types of ad fraud.
Ad fraud still remains a large concern for advertisers
In the new survey, PPC managers were asked about their awareness of ad fraud, their overall level of concern and, what role in marketing they held to see if there was any correlation.
The vast majority of all respondents, 70%, stated they were somewhat or very concerned about ad fraud.
The survey also showed that more experienced marketers had a larger concern about fraud. These particular respondents may be able to identify fraud more frequently due to their dedicated marketing and analytics experience relative to more general business owners and consultants.
Ad fraud continues to significantly impact campaign performance
All respondents to the survey had direct experience managing PPC ad campaigns and most of them reported seeing a large amount of fraud.
74% of those respondents experienced more than five percent of fraud in their ad campaigns and an incredible 11% of marketers experienced greater than 25% of fraud. Even a small amount of fraud can have a tremendous impact on an advertiser’s budget and performance.
The types of ad fraud, and their marketing channels, still vary wildly
Historically, click bots were often the most commonly mentioned type of fraud, but today the survey shows “ad stacking” and fraudulent URL sources as the most common problems for PPC managers.
Click bots and “pixel stuffing” were the third and fourth most commonly mentioned and over 10% stated that competitors clicking their ads was a major problem for them.
The respondents also experienced fraud across every channel in nearly the same amount. Even newer technologies, such as over-the-top streaming TV (OTT), reported sizable issues of ad fraud. This could be due to it having less mature ad tech that creates a greater potential for exploits.
Experienced marketers use third-party software to help prevent fraud
Eliminating ad fraud entirely can be very difficult, but advertisers can rely on a few techniques to help save their budgets and improve their performance.
The first is to simply follow best practices to help identify bots, such as adding a “honey pot” to lead forms, or by frequently monitoring data from clicks, views, and leads to find irregularities and then adjusting advertising campaigns accordingly. However, these require experienced marketers to be able to identify the bad data and it can be labor-intensive to frequently monitor and take action.
Another option is to rely on anti-fraud services provided by ad networks, such as Google Ads. This can be effective; however, there is often a conflict of interest the ad networks generate revenue from each click or impression regardless if it’s fraudulent. Reducing their fraud clicks thus reduces their revenue. Some ad networks also provide very little transparency of invalid activity in their reports and then the burden can be up to the advertiser to request reimbursements if fraud is discovered.
Some ad networks, such as Google Ads, provide “invalid clicks” in their campaign reports for advertisers, but one man sued Google after allegedly discovering his invalid clicks were far greater than what the Google reports were showing.
When the survey respondents were asked if they believed Google Ads blocked click fraud, only half of the respondents, 49%, believed Google did. This should be a major consideration for advertising in 2022.
The final option is to use a dedicated, independent ad fraud detection software. There are several players on the market that can help advertisers detect, block fraud in real-time and the survey showed that about 50% of advertisers use these services, or have considered using one.
Overall, the results of this survey indicate the prevalence of fraud in advertising campaigns today is still very high. As marketers plan for 2022 they should consider taking action against this fraud to improve their ad performance and extend their ad budgets.
The Fraud Blocker survey was conducted by Pollfish and concluded on December 1, 2021. It was sent to a randomized group of PPC marketers and media buyers in the US and UK who purchased digital advertising in the prior 24 months. 200 respondents completed the survey. Pollfish is a leading survey company with a pool of over 480 million mobile audience members worldwide that participate in their surveys.
Use Customer Lifetime Value to Find More Clients
With new privacy rules continually changing the landscape of third-party data, brands are increasingly becoming more focused on understanding their current customers in order to make more sophisticated marketing decisions. One approach to this is utilizing customer lifetime value (LTV) to segment your best customers and ultimately find more of them. In this article, we’ll provide a brief outline of LTV but you’ll want to attend Hero Conf 2022 in Austin, Texas for a more in-depth breakdown with key takeaways.
What is customer lifetime value?
The lifetime value of a customer, or customer lifetime value (LTV), represents the total amount of money a customer is expected to spend in your business, or on your products, during their lifetime.
*Note on calculating LTV*
Now to be fair, there are a number of varying ways to calculate LTV going from relatively simple, to complex and complicated. This article will not be focused on evaluating the best approach or even how to calculate LTV. I do have some preferred tools which I’ll share at Hero Conf- but ultimately finding the best tool that works for your brand is important.
Large brands like Amazon and Starbucks have documented how their understanding of LTV has influenced their marketing and overall business decisions. Smaller brands who often have limited resources in their pursuit of growth often overlook LTV or don’t truly appreciate how helpful it can be to their overall growth.
Which campaign is performing better?
Take a look at the chart below – at a glance – which campaign appears to be performing better?
|Campaign A||Campaign B|
|Cost / Acquisition (CPA)||$10.00||$16.67|
Most digital marketers, including myself, would say campaign A. More purchases (revenue), lower CPC, and lower CPA. Seems pretty obvious.
But a question that’s worth asking is – what if campaign B focused on acquiring a better quality customer? Someone who purchased a higher average amount bought more frequently, and stayed, is a customer of the brand for a longer period of time. Ultimately, a customer with a higher LTV. The question of which campaign is performing better looks a lot different when LTV is factored as a metric and could lead to very different marketing approaches.
Looking beyond CPCs & CPAs
These are conversations that more brands should be having. Looking at CPCs, CPAs and the revenue from the first purchase are all very common KPIs, but they can be misleading and myopic. Factoring in LTV provides a more holistic approach to making marketing and overall business decisions.
Going a step further, brands that decide to utilize LTV often come across the hurdle of how to efficiently segment their best from worst customers. In the workshop, I’ll share the most effective analysis that we’ve found. For brands on Shopify, we’ll take it a step further and offer a valuable app that will both help solve LTV and segment your customers as well. There are a number of apps in the Shopify App Store that can help calculate your LTV and effectively segment your customers for you, but there’s one that we’ve found to be leaps and bounds ahead of the rest.
Finally, once you’ve segmented your customers, you now have the ability to supercharge your marketing efforts to find more of your best customers, while also excluding targeting anyone who you believe might be exclusively bargain hunters or cherry pickers.
If you’re interested in scaling your brand, you’ll want to attend this workshop. Understanding LTV and how to find more of your best customers will be an invaluable tool that will help move the needle for your brand in 2022. Key takeaways will be:
- How LTV has shaped the decisions of large brands we all know
- How LTV provides a more holistic picture of success within paid search
- How we’ve helped a women’s apparel and homeware brand find more of their ideal customers
- Tactical insights (including apps/tools) on how to implement an LTV strategy within paid search
Hope to see you there!
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