Google has launched a Search Ads 360 integration for Google Analytics 4 (GA4), the company announced Monday.
Why we care. Integrating Search Ads 360 data into GA4 enables advertisers to see sessions, users and conversions within the context of dimensions such as source, campaign, medium and default channel grouping. They’ll also be able to see cross-channel attribution data in their Model comparison and Conversion paths reports.
Contextualizing data can enable better decision-making and more efficient campaigns. This integration may also help advertisers save time that might otherwise be spent flipping between GA4 and Search Ads 360.
Data sharing between Analytics and Search Ads 360. Linking your GA4 property to your Search Ads 360 advertiser allows:
GA4 to export conversions to Search Ads 360.
Search Ads 360 to export campaign and cost data to GA4.
GA4 App- and site-engagement metrics to show in Search Ads 360 reports (only for advertisers using the new Search Ads 360 experience).
How to link GA4 and Search Ads 360. Advertisers using the new Search Ads 360 can follow the procedures below to link GA4.
In Analytics, click Admin.
In the Property column, use the menu to select the property you want to link.
Under Product Links, click Search Ads 360.
In the link table, click Link.
If you have the Admin role for one or more Search Ads 360 manager accounts, in the row for Link to Search Ads 360 manager accounts I manage, click Choose accounts to select the manager accounts you want to link your property to.
Click Confirm.
Click Next.
Configure the following settings: – Enable Personalized Advertising: On by default. This setting allows Analytics data to be used to personalize ads. – Enable Campaign Attribution: On by default. Turn this setting on to import Search Ads 360 campaign data. Note: If you turn this setting off, then no conversions are attributed to Search Ads 360 and no conversions are exported from Analytics. – Enable Cost Data Reporting: On by default. Turn this setting on to import Search Ads 360 cost data.
Note: Those not using the new Search Ads 360 will still have to follow the procedure above, in addition to opening their Search Ads 360 interface and creating a link from their Advertiser to their GA4 property in order to also export conversions.
With a number of privacy changes on the horizon, marketers will soon face new challenges with how we track and measure marketing performance. Measuring marketing ROI using native reporting from media platforms like Facebook and Google Ads will become increasingly less accurate and complete, and ultimately not even possible as third-party cookies are phased out.
Frankly, marketers deserve better than what third-party cookies have been able to deliver in terms of measuring marketing value. Third-party cookies are predisposed to inflation and double-counting when it comes to conversions. And conversions, whether tied to an online purchase or a form submission, are what most businesses truly value.
The deprecation of third-party cookies, therefore, presents an opportunity to improve the way we measure and report on campaign performance and ROI. It’s an opportunity to shift our reporting further down the funnel using first-party tracking and data strategies to create a more accurate and sustainable source of truth.
We invite you to learn more about creating a first-party marketing measurement strategy in our session “How to measure marketing value in a cookieless world” at the upcoming MarTech virtual conference on Wednesday, March 30th.
By attending this session, you’ll be able to:
Understand how third-party cookies have led marketers astray
Transition to first-party and even “no-party” marketing measurement
Put a 90-day plan in place to get your measurement strategy future-ready
We’re told hindsight is 20/20. But hindsight doesn’t always tell you where you need to go. In fact, when it comes to research, it only tells you where you’ve been. On the other hand, Foresight research tells you where you need to go, sets you on the right path, and lets you continually check-in to make sure you are always headed in the right direction.
“Foresight research allows you to get quick feedback about what your customers want right now,” Feedback Loop CEO Rob Holland said. “It gives you the ability to rapidly innovate, test a hypothesis, get feedback, make another decision, test that decision, get more feedback, and do that all very, very quickly, so you can stay on track.”
Marketers looking to plan their next ad, message or campaign traditionally have relied on hindsight, or legacy, research. They look at how things have performed in the past to inform what they do in the future. But traditional legacy research is expensive and time-consuming, and it gives you a snapshot of just one moment in time, which by the way, is already behind you. It may have little to do with what’s in front of you.
We know of what we speak. Feedback Loop’s crackerjack marketers recently undertook a revamp of our digital presence. As the effectiveness of agile, iterative, foresight research for marketing became crystal clear, so did the tangible results of our campaigns.
“We would’ve been completely wrong.”
Jolene Pierangeli was surprised. A seasoned marketing consultant, she was charged with overseeing Feedback Loop’s marketing changes. As was her usual practice, she looked at previous ad campaign history and website traffic to gather data to use in planning the company’s website redux.
“Typically, in marketing, you look at how banner ads or display ads perform in other locations,” Pierangeli said. “You take into account personas and the demographics for your audience. Then you use that data to make assumptions and decisions based on what you know — but that legacy data isn’t necessarily right.”
Feedback Loop exists to provide rapid consumer feedback, so Pierangeli didn’t have to rely solely on that legacy data. She did what Feedback Loop tells its clients to do: She tested.
Marketing designer Michelle Baker created mock-ups of two new website designs. Option A had a clean white background, using graphics to add some color. Option B had blue elements and a darker blue background. Based on the legacy data they had, as well as the popularity of white-background websites, Pierangeli and Baker were certain their audience would prefer Option A. They did an A/B test using the Feedback Loop platform.
To their surprise, respondents favored Option B.
In addition to testing the color, layout, and user experience for the website, they tested whether to use clickable dropdowns, so all text information would be accessed by clicking buttons.
“Personally, I like to interact with websites that way, and I thought other people would, too,” Baker said. They used the Feedback Loop platform to test their hypothesis that users would prefer a cleaner experience that would encourage them to click through to get the specific information they wanted.
Again, Pierangeli and Baker were surprised. The overwhelming response to the test was that users wanted everything to show at once — no clicks needed — because they didn’t want to have to do any extra work to get information.
“That was not a behavior or a response we thought we were going to have,” Pierangeli said. “I realized you can use as much data as you want from previous campaigns, but you’re going to be missing out on what consumers actually want. In our case, we want product professionals, marketing professionals, research professionals, UX, and innovation teams to find the website useful and easy to navigate.
“All the data we had from other channels and the historical information — from a few months ago to a few years ago — did not tell us this. We didn’t learn this until we tested the different layouts, the different colors, the different ways text was presented. I was surprised. I’ve been doing this for a long time — a really long time — so you’d think I’d have a fairly decent understanding of what consumers would want. I have an understanding of target personas and what’s important to them. That’s why we do persona exercises. But without using Feedback Loop, we would’ve been completely wrong.”
The new blue website launched on February 14, 2022. Since then, Feedback Loop has seen traffic increase — and that doesn’t even include repeat users. The number of new website visitors doubled.
“In a week, we got a month’s worth of lead guarantee.”
On the heels of the website’s success, Feedback Loop launched a content syndication program. The company handling the promotion said it should take a month to reach Feedback Loop’s guaranteed lead goal.
Before launching the promotional ad, Pierangeli and Baker tested the advertising and messaging for their promotion using the Feedback Loop platform. For the ad, targeted at both marketers and product teams, Baker created one version using a cartoon treatment and one using screenshots. The majority of the audience-tested chose the screenshot version. That’s the one they launched.
Feedback Loop got their entire lead guarantee in one week.
“In a week, we got a month’s worth of lead guarantee, which is great because that gave us the entire month to nurture them. We were able to use the time we saved by testing to nurture those leads and then start converting them,” Pierangeli said. They’ve converted 30% of those leads to MQL — so far.
For the next iteration of the ad, targeted at marketers, Baker tested three concepts with the Feedback Loop platform: one with cartoons, one with screenshots, and one with a combination of both. The third option — a combination of styles — was chosen by 46% of respondents, so that’s the ad they’ll be launching.
“As a designer, before I started testing concepts, I never really knew how an audience would react to what I was creating,” Baker said. “Using the Feedback Loop platform lets us target exactly who we want to target, and the more data we have, the better the end results will be. Now, when I create something, going in blind is pointless since I have the ability to know what the audience prefers before we ever launch.”
“You already know going into it what your consumers want.”
The lessons she learned by using Feedback Loop for Feedback Loop changed how Pierangeli looks at marketing campaigns.
“When marketers build out a campaign plan, they typically build out a first draft, revision time, second draft, final draft, then launch date,” she said. “Unfortunately, a lot of times after a launch, the campaign does not perform and deliver the number of leads or MQLs or conversions to opportunities that marketing needs to build the lead funnel and help sales convert. That means marketing will have to go back and add new content to their campaign or do other types of activities. Basically, they’ll have to do more work in order to get to their goal.”
By building rapid consumer research into marketing processes early on, marketers can save themselves and their teams hours of work down the road and, potentially, prevent failure of a launch. Testing in the Feedback Loop platform only takes a couple of hours, and it can de-risk decisions, whether it’s what color to put on your website or what messaging to use for a promotion. You don’t have to wait weeks or months for traditional research methods to produce insights that are already in hindsight when you finally get them. A little foresight goes a long way toward ensuring you launch successfully.
“You need to bring in the voice of your consumers to have true data to make your marketing campaign successful,” Pierangeli said. “Then you can launch with confidence and know you’re going to get what you’re trying to get, whether it’s a number of leads or delighting your customers or a higher conversion rate. For all the things you are being held accountable for, you improve your chances of success by testing in advance.
“It’s OK to be wrong. But you need to understand that, even though you may have been doing this for years, even though you may have the most well-written persona until you actually ask your potential customers what it is they want, you really don’t know.”
Google has released a new home page for Google Analytics 4 properties. Google said “the Home page surfaces information that’s relevant to you based on your behavior in Analytics.” Google added that “you can use the page to monitor traffic, navigate around Analytics, and get insights about your websites and mobile apps.” Google said the new home page “provides more personalized content as you continue to use Analytics.”
How to see it. To access it, click on the “Home” link in the left navigation.
What it looks like. Here is a screenshot of the new home page:
Broken into five parts. The new home page is broken into these five parts:
Overview shows metrics that are relevant to you based on your behavior in Analytics
Realtime shows activity as it happens using data from the Realtime report
“Recently viewed” provides links to parts of Analytics that you visited most recently
“Because you view frequently” shows cards that you view often
“Insights” shows unusual changes, emerging trends, and other insights about your sites and apps
More details. Google has posted a more detailed help document breaking out how to use this new home page over here.
Why we care. When Google makes changes to the features you use on a daily or hourly basis, it can take some time to adjust. So go check out this new home page in GA4 and acclimate yourself to it. Dig in and hopefully you will find new tidbits and pieces of information that you didn’t see in the previous version.
Google has created a Data Studio sample template called the Web Stories Insights Dashboard. It provides an overview of how your Google Web Stories are performing.
Web Stories are visual, tappable full-screen mobile stories that can be found via Google Search and Discover.
Metrics you can view in Web Stories Insights Dashboard. There are two main tabs: Overview and Story Level. Among the metrics you can view on this dashboard:
Key Metrics: Story starts, page views, time spent and completion rate (number of story completions divided by story starts).
Audience Metrics: Age, gender and device.
Top Stories: Your top 10 stories during a selected time period, sorted by Story Starts.
Traffic Channels: An overview of how users have found and read your Web Stories – organic search, direct (which includes Discover), referral, social, email, or custom.
Story Level Performance: Key metrics and a breakdown of pageviews for a specific story.
For this template to work, you’ll first need to set up tracking for your Web Stories in Google Analytics (or another analytics provider). For whatever editor you use to create Web Stories, you’ll need to provide your UA Tracking ID. After you’ve done this, you can view performance metrics by navigating to Behavior > Events > Overview.
Then you’ll be able to see metrics including story starts, time spent, story pages viewed and story completions:
What the Web Stories Insights Dashboard looks like. This:
Web Stories Insights Dashboard, created in Google Data Studio
How to get the dashboard. You can access the dashboard here.
Once you get there, simply select your analytics account from the dropdown to see your data. Also, you can copy the template and customize it to your liking.
For more information, you can check out this blog post. Google also shared a video that walks you through setting up and using the dashboard.
Why we care. Data is king. This template provides a visual overview to help determine how well your Google Web Stories are performing. You can see how engaging your content is and how people are finding your stories. Tracking the performance of any content you’re investing time and resources into is essential.
Google Analytics 4 (GA4) and Google Search Console (GSC) properties can now be linked, Google announced Wednesday via the Google Search Central Twitter account.
Note: This announcement pertains to GA4. In Universal Analytics, this capability has been available for years.
Why we care
Associating your GA4 and GSC properties expands your ability to analyze search traffic and enables you to directly compare search data with site analytics, which can help you get a better view of the user’s journey and identify correlations.
Universal Analytics and GA4 have existed alongside one another since the latter was launched in October 2020. Google has not announced when it will stop supporting Universal Analytics, but when it does, marketers will be forced on to GA4. Rolling out this association now — well ahead of the eventual deprecation of Universal Analytics (which SEOs overwhelmingly prefer at this point) — will also give us more time to explore GA4’s capabilities, which may help facilitate the transition.
Google Analytics 4 (or GA4) is a completely new version of Google Analytics. The goal of this post is to share the key differences between Google Analytics 4 and the previous version of Google Analytics that you are probably familiar with and highlight the key features that you should be aware of as a Digital Marketer.
What makes Google Analytics 4 unique
If you were around back in 2013 you might remember when the last version of Google Analytics was launched, called “Universal Analytics”. At that time, you needed to update your tracking code to migrate to the new version, but everything else stayed pretty much the same.
Unfortunately, this is not true about the upgrade to Google Analytics 4. Data is collected differently, stored differently, and even visualized differently. All of this change can be frustrating, but GA4 is quite a bit more advanced than the previous version. Those Digital Marketers who migrate to the new version will be rewarded with an Analytics tool that provides a better representation of user behavior, respects user privacy preferences, and allows you to spend less time collecting and aggregating data.
This is possible because of three technologies that Google has been working on for the past few years:
Firebase Analytics
The first is Firebase Analytics. Firebase is a suite of products for developing mobile apps that Google acquired back in 2014. Firebase Analytics uses something called the “event-driven data model” to better describe behavior and measure user engagement. I will speak more about this in a moment, but the important thing to know about Firebase Analytics is that it is the backend for Google Analytics 4. This means that data captured across both websites and native apps now share a single format.
Google Signals
The second technology that GA4 is built on top of is Google Signals. You’ve probably heard about Google Signals before because this is the identity software that uses Google accounts to recognize logged-in users. It is the same method that Google uses behind the scenes to create audiences, and Google Analytics 4 can now use this feature to recognize users instead of relying on first-party cookies.
The Global Site Tag
Finally, Google Analytics 4 is also built on top of the global site tag. The benefit of this technology is that it allows you to make changes in the user interface that actually change the code that is deployed to your website. As an example, with GA4 you can flip on a feature to track when users play a YouTube video, and the code to do this will be automatically deployed to your site without a change in the tag manager.
So that’s a pretty high level overview, and there’s a lot we can talk about next. But I’d like to dig a little bit deeper into what I see as the most important of these features: the event-driven data model.
The Event-Driven Data Model
First of all, let’s remember what the home screen looks like when you log into Universal Analytics today.
As you’ll notice, sessions are undeniably the most important metrics in legacy Google Analytics reports. When someone asks the question: “How much traffic did our campaign drive?”, the answer is usually given in the number of sessions. When someone asks the question: “Are users engaged with the site?”, then the answer is usually bounce rate (a subset of sessions), session duration, or pages per session. When someone asks the question: “How well are we converting against a goal?”, the answer is usually the Ecommerce or goal conversion rate (both of which are calculated with sessions).
The problem with sessions
The problem with this is that the concept of a session can be difficult to apply to mobile and single-page apps, because the fact is that apps are more variable than traditional websites, and the assumptions that we make about how users experience the web do not always hold true for how users experience an application.
As an example, if you’re a runner you might open a mobile app to track your speed and let it run in the background for hours. How many sessions should that create? Are bounce rate and pages per session really useful measures of engagement in this situation?
How the event-driven data model solves this problem
The solution to these problems is the event-driven data model, because it eliminates the concept of a hit type (social, pageview, transaction, etc), and replaces it with three simple components: events, event parameters, and user properties.
This may seem like a small change, but it strips away all of the assumptions that we previously made about the data. When something happens, it is tracked with an event (ex. link_clicked). Parameters are just pieces of information that describe the event (link_text). And user properties are simply pieces of information that describe the user who initiated the event (current_customer). That’s it.
Google did not invent the event-driven data model (numerous products have been applying it to mobile apps for years), but with Google’s market share, GA4 will be the first time it has been applied on such a large scale. So it’s a new concept to most marketers.
The impact of these changes is that page views and sessions are no longer the fundamental building blocks that they once were. They still exist, but you are not required to use them where they don’t make sense because the focus has shifted to users and events. As you can see in the new Home screen for GA4 below, the most important metric in the Google Analytics 4 reports has changed from sessions to users.
Google has been talking about emphasizing users over sessions for years, but GA4 really forces this change.
The event-driven data model also enables a series of new dimensions and metrics that can be generated without relying on the concept of a session. There are several examples of this, but the first one I want to share is the move from “goals” to “conversion events”.
From goals to conversion events
As you may recall, a user completes a goal in Google Analytics when they take some action during their session. If the action is taken multiple times during the session, we would still only count that as a single goal completion.
GA4 has eliminated the concept of a goal, and replaced it with conversion events.
A conversion event is simply any event that you’ve marked as important to your business. So this could be an event to indicate a lead form has been submitted, a video has been completed, an element has been clicked on, or anything else. As you send data to Google Analytics 4, the “Configure > Events” report will populate with all of the event names that have been received. You can send up to 500 unique events, and you simply flip the radio button to mark any event as a conversion from here. Once you do that, you will be able to import these conversions into Google Ads just like you would import a goal.
Acquisition vs. re-engagement
When you are evaluating how well your traffic channels are driving conversions, you now have to decide if you are evaluating how well you are acquiring new customers or re-engaging existing customers. If you choose the “User acquisition” report, your conversions will use first-touch attribution. But if you choose the “Traffic acquisition” report your conversions will use last-touch attribution.
There are three important things that Paid Search Managers should know about conversion events:
The user can complete multiple conversions within a session
Each conversion event must have a unique name so that it can be marked as a conversion with the radio buttons I showed a moment ago
Qualifying for an audience can trigger a conversion event
This brings us to our next topic: audiences.
Audiences
You might remember that the old version of Google Analytics allowed you to create user segments (for example: all users who added an item to the shopping cart but did not make a purchase). Then, you could promote that segment to an Audience, and share it with Google Ads for remarketing and identifying look-alikeslook-a-likes.
In Google Analytics 4, the concept of a segment has been merged with the concept of an audience. Instead, you simply create audiences. Audiences can be applied to any report, and they can also be shared with Google Ads.
Another thing that is different about audiences is that once you’ve created one, it is automatically shared with everyone else who uses Google Analytics 4. So you do not need to pass links around to your coworkers so that they can download the audience you are using.
Predictive Audiences
And, lastly, Google has launched a series of predictive audiences that can be automatically generated for you (which are similar to the Smart audiences you might be familiar with). These audiences use Google’s machine learning to score the probability that a user will make a purchase or churn within the next 28 days so that you can invest your remarketing budget in reaching the customers who will have the greatest impact.
Ok, so that’s audiences. Let’s talk about engagement metrics.
New Engagement Metrics
A moment ago I mentioned that all of the metrics that were previously calculated based on sessions have changed.
This is important to Digital Marketers because these include all three of the tools that we previously had for measuring the quality of a click: bounce rate, pages/session, and average session duration.
These have been replaced by a new and very important metric that is automatically recorded in GA4 called “engagement time”, which is the amount of time that the user actively viewed your content. If the user is on a mobile app, this is the time that the app was in the foreground. And on a website, this would be the time that the browser tab was active.
Google Analytics 4 then uses this metric to calculate: engaged sessions.
Engaged Sessions & Engagement Rate
An engaged session is a session with greater than 10 seconds of engagement time. You can divide the number of engaged sessions that you had during a time period by the total number of sessions to calculate another new metric “engagement rate”. This is the metric that you will use instead of bounce rate in GA4 (read more about engagement rate here).
Engagement Rate is a much more useful metric for measuring user engagement, especially with sites like blogs and news outlets where a successful session may only include a single pageview.
Active Users
Now I do want to point out that engaged sessions and engagement rate are both session-based metrics. Sessions have not gone away with GA4, despite the greater emphasis on users. But, we also have a new metric called Active Users.
An active user is someone who has had at least 1 engaged session during the date range that you’ve selected.
If you pull up either of the Acquisition reports you can see how these new metrics are front and center. I expect that a lot of Paid Search Managers are going to struggle to let go of the old metrics, but I actually think that this is a big step forward, and I hope that you’ll find these tools to be useful once you become familiar with them.
Before we move on, there’s one more thing that I want to point out about these new engagement metrics.
Improved data import
None of these are impacted when you import external data. The details on this are a bit technical, but this solves a really big problem with Universal Analytics. If you ever tried to upload offline transactions, for example, you created a bunch of single hit sessions in Google Analytics, which drove up your bounce rate and reduced your pages/session and avg. session duration.
This was very frustrating for a lot of analysts, but since those events do not contribute to engagement time in GA4, they do not have any impact on your engagement metrics. This makes the integrations with Salesforce or call tracking tools much more seamless than they were before.
Okay, I have three more items to discuss, and all of them circle around User Privacy.
New privacy controls
First of all, Google Analytics 4 provides a long list of new privacy controls that marketers can use to ensure they are compliant with the latest regulations.
Disable ads personalization
The first is the option to disable Ads Personalization. This is useful for marketers who would like to use Google Analytics to understand user behavior, but who do not plan to build audiences for remarketing. In this case, a user with “Edit” permissions can completely disable audiences for remarketing so that no one in the company can flip it on.
However, Digital Marketers (such as yourself) also have the freedom to flip this on only within specific geographies. So, for example, it’s now possible to disable Ads Personalization within the EU, but continue to use this feature for all other users.
Not for personalization
But even within a geographical region where you are using Ads Personalization, you can exclude specific events that may be private in nature so that they cannot be used to generate audiences.
Websites and apps that collect medical information are a good use case for this. If you have an event that identifies that the user has generated an appointment with a doctor, you may choose to mark this event as “NPA” (not for personalization) so that no one on your team can create an audience that considers this data point.
So those are the most important new privacy controls that Digital Marketers should be aware of, but I should mention that there are also several others.
How Google Analytics 4 is embracing user privacy
I think that it is important to point out that enabling many of these privacy controls will create gaps in your data. And historically, most Analytics tools have worked very hard to eliminate data gaps like this (for example: we use to write code to detect when users are running an ad blocker, we’ve deployed tricks to help recognize users across domains, or when they log in with a different device, etc.). These new privacy features in Google Analytics 4 actually move in the other direction — they give you more controls to embrace privacy when the user requests it.
And the reason is that Google is taking the first steps to transition us into a world of incomplete data, where we do not rely so strongly on cookies.
Over the past 3 years or so, Safari and Firefox have taken large steps to limit how long a cookie can exist, and eliminate cookies that are used for tracking users across sites. Most marketers don’t realize that the impact of this is already showing up in your data.
For example, most websites are right now showing a higher number of users in Safari than they two years ago. This isn’t because you’re driving more traffic, it’s because the cookies that we use to identify a person are being deleted between sessions if those sessions are more than 7 days apart.
So, Google Analytics has to help marketers prepare for regulatory restrictions that are coming from GDPR and CCPA, but they also have the new burden of helping marketers prepare for technical restrictions that are being imposed by browsers.
In response, Google has announced two features that are coming soon to GA4: Reporting Identity and Conversion Modeling. So I’d like to wrap up with a quick overview of what we know about these features and how they will work once they are released.
Reporting identity
Traditionally, Google Analytics has identified a user on the web by setting a cookie (called the Client ID), or by using something called the App Instance ID in a mobile app.
If you’re lucky enough to have logged-in users on your site, you have the ability to set your own unique identifier for users (called the User ID). The benefit of doing this is that you could see how frequently users log in to your site from different devices.
Right now, if you go to your property settings and click on “Reporting Identity” you’ll see two options: “By device only” (which means that you are only using the Client ID and do not have Logged-in users), or “By User-ID, Google Signals, and then device”.
As I mentioned before, this feature will be available for users who are logged in to a Google account on their device and have opted-in to ads personalization (so not everyone). If you enable this feature, GA4 will still use the user ID if it is available since it is the most accurate way to identify a user. But, if the user ID is not available and Google Signals is, then GA4 will use Google Signals to identify the user.
As a result, you will be able to identify a portion of your users across devices, even if they are not logged in. This is important because it means that you will generate very complete data for the small subset of your users who are logged in to Google, using Chrome, and have enabled ads personalization.
Having good information about this small subset of your users will help you fill the data gaps that exist in the rest of the user population. And this is called “Conversion Modeling”.
Conversion modeling
Conversion modeling is different from Attribution modeling. The idea is that Google uses machine learning to fill the gaps that we know exist in our data. So, for example, if we know that Safari is reporting 100 users on the site last month, we could estimate that you probably only had 80 because 20 of those were the same user with deleted cookies.
The downside of this approach is that we are going to become more reliant on black-box algorithms and estimated data. But the benefit is that we can respect a user’s privacy request without the concern that it will cause our data to be less useful for making marketing decisions.
How to get started with Google Analytics 4
If you’ve made it this far, then hopefully I’ve convinced you to get started with Google Analytics 4. My recommendation is to start today, but take it slow. If you’re running an old version of Google Analytics, you can add Google Analytics 4 tags to a website without impacting the existing Google Analytics implementation.
My recommendation is to dual-tag your site, so that data is sent to both versions of Google Analytics for 6 months or so. This allows you to continue using the old version of Google Analytics for your day-to-day reporting, and spend an hour or so a week looking at the new metrics and pulling reports from GA4. Plan to fully switch over to GA4 entirely in 2022 by removing those old tags.
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Good morning, Marketers, and Google Ads (the artist formerly known as AdWords) turned 21 this weekend.
Did you know that Google Ads started as a service where marketers paid a monthly fee and Google would set up and manage the ad campaigns? Quite ironic considering many advertisers today are lamenting the loss of control and data as Google Ads invests in more automation and is managing many elements of ad campaigns itself. The company offered a self-service option for small businesses “who wanted to manage their own campaigns,” which is the foundation of our current Ads platform today.
What was AdWords like when it first ramped up? “The AdWords program provides low-cost exposure on one of the industry’s leading search engines with CPMs from $15 or 1.5 cents an impression, $12 or 1.2 cents an impression, and $10 or 1 cent an impression, for the top, middle, and bottom ad unit positions, respectively,” stated the announcement.
Not only that but, “Google’s quick-loading AdWords text ads appear to the right of the Google search results and are highlighted as sponsored links, clearly separate from the search results.” It seems a lot has changed with Google’s ads product since inception, and yet we’re almost coming full circle in many ways too.
Carolyn Lyden, Director of Search Content
How to solve the marketing reporting conundrum without being a magician
In a recent survey shared at SMX Report, C-Suite executives said sales and leads are top performance indicators for marketing teams. If sales and leads are what our leadership teams, whether internally or externally, care most about, what does this mean for PPC marketers?
Such preferences can lead to a level of expectation that marketers are going to create a magic sales faucet, a fountain of fortune, or that there’s a secret Google leads button hidden in our toolbox somewhere.
And hey, PPC marketers can help make some pretty magical things happen, but we’re definitely not magicians, at least not in the Gandalf the Grey or Albus Dumbledore kind of way. In this how-to, SMX speaker and Aimclear’s VP of Growth, Amanda Farley deep-dives into the steps to get reporting and measurement right for your audience.
Navigating Google’s title changes: The rollout, what’s happening now and what you can do about it
In August, Google introduced a new system for generating title links (the title of a search result in Google Search). “This is because we think our new system is producing titles that work better for documents overall, to describe what they are about, regardless of the particular query,” the company explained.
However, during the new system’s initial rollout, SEOs provided example after example after example of titles that not only failed to describe what the page was about, but may also confuse users and deter them from clicking through. Fortunately, the situation has since improved, but placing blind faith in Google’s new system can mean that you’re ceding control over a crucial aspect of your content, which could ultimately affect your business. Below, you’ll find a synopsis of how Google’s title changes have evolved, how you can verify whether your titles have been changed and what you can do to regain control over them.
In this in-depth analysis, editor George Nguyen covers what’s changed, what to do if you suspect Google is changing your titles, what to do if you dislike the changes Google is making, and what these changes mean for the future.
How to fix the SEO issues that keep you from achieving your goals
At this year’s SMX Report, JR Oakes, Senior Director of Technical SEO at Locomotive, provided an overview of SEO Issues that hold us back from achieving our goals. He took a holistic look at resources, communication and mental constructs around SEO that often hinder progress. Oftentimes we look for the quick fixes that drive major ranking improvements. These still exist, but the relationships involved with connecting us to clients, and the website to users are where the most sustained value can be found.
In this article, Oakes goes through the 8 ways that SEO teams can break through major issues and reach their KPIs and achieve their goals:
Companies need the team buy-in and resources to succeed with SEO.
SEO teams should focus on clarity of communication and efficient prioritization.
The key areas to consider in SEO strategies are Links, Content (page satisfaction), Experience, and Relevance.
GIGO is a real thing. Taking time to go slow with accurate XML sitemaps, custom metrics, user feedback mechanisms, etc can make your life easier and give you data to inform growth.
Spend some time watching user sessions. You will thank me.
Work hard to ensure your pages solve a problem or provide the right answer.
Look at how your page’s content aligns with user searches provided by Google.
Write to support and build your site’s subject matter expertise. Credibility is key.
Support our existing and future Clients with Ecommerce consultancy services – concentrating (albeit not exclusively) on new product development, roadmap definition, experience management and support in the creation of business cases
Have in depth experience of eCommerce tactics and how to influence the physical and digital journeys.
Develop a master content plan that meets lead generation and strategic brand goals; translate this plan into an editorial calendar and manage the workflow to execute
Create short-form and mid-length direct response content for lead generation including native articles, landing page copy, and paid social advertisements
What We’re Reading: What does “shoppable” content mean for the upcoming holiday season?
We’ve mentioned it a thousand times, probably, but the pandemic has accelerated e-commerce and online retail. It’s a fact of life that the trends of COVID shopping have just become a way of life now. People expect to find what they need online (whether they plan to have it delivered or pick it up on location), and that reality coupled with advances in AI and VR technology means retailers are looking to “shoppable content” to boost their online sales this holiday season.
Pinterest. “Pinterest recently rolled out the ability for merchants to automatically create videos from the products they’ve already displayed on their accounts. This creates a slideshow that taps into the demand around videos and stories-based formats in social feeds. The videos also link directly to merchant check-out pages.” Along with this Pinterest Creators can tag brands in their pins which is a useful addition for affiliate and influencer marketers.
TikTok. TikTok is continuing to expand their shopping options, ads, and integrations. One example is TikTok Shopping, which “lets Shopify merchants that have a TikTok For Business account add a ‘Shopping’ tab to their TikTok profiles. That in turn lets them sync product catalogs and create mini storefronts on TikTok. So the integrations continue to expand in several directions,” said Boland.
YouTube. “YouTube just this week announced that it’s expanding its program for live shopping. This gives merchants a QVC-like format to stream product demonstrations. It’s rolling the whole thing out through a week-long event called ‘YouTube Holiday Stream and Shop’ starting November 15.” The move elevates YouTube to a shopping destination.
Shopping doesn’t just happen on e-commerce sites anymore. Social and streaming platforms are becoming retail destinations (especially as we near the holiday season) with shoppable content. Retailers will have to take advantage of the integrations to ensure they’re found across channels this shopping season.
If sales and leads are what our leadership teams, whether internally or externally, care most about, what does this mean for PPC marketers?
Such preferences can lead to a level of expectation that marketers are going to create a magic sales faucet, a fountain of fortune, or that there’s a secret Google leads button hidden in our toolbox somewhere. And hey, PPC marketers can help make some pretty magical things happen, but we’re definitely not magicians, at least not in the Gandalf the Grey or Albus Dumbledore kind of way.
There can also be a limiting belief that there is no value in marketing components and efforts that don’t have a direct correlation to exact dollars, and by some sort of wizardry, it’s somewhat expected a visitor will just magically appear on a business’s website and then (poof) become a customer. Such bloated misunderstandings of reality are great examples of expectations that may lead to conversations where we hear things like “Facebook doesn’t work”, “We just want the most bang for our buck”, and “Focus only on low funnel”.
Expectation vs. Reality
Most consummate marketers know consumers are not mythological robots that you one simply puppet into a magical fountain of money. In actuality, consumer behavior is the farthest thing from linear, constantly evolving.
Understanding customer journey for reporting
We know customer journey is a complex process but getting a baseline understanding can positively impact our marketing reporting and data measurement. Start by asking: What data do we know? What is assumed based on what we know?
Why customer journey matters: When as marketers and their stakeholders we scrutinize reporting needs we can then attribute longer timelines to different types of attribution models. Studying marketing reports does not need to be perfect math but conceptual understanding can improve cross-channel performance attribution and understanding of what marketing tactics are driving success within your reporting funnel.
What is our customer lifetime value?
What is our average customer retention?
What is the typical sales timeline to close?
What is our average order value?
Additional Considerations: Emotional investment of the purchase, the purchase price (average buyer’s journey for purchasing a yacht is 5 years), timeliness (is this something that is urgent they need to purchase or is there risk in making a quick purchase).
The more your customer value and complexity of purchase is, the more challenging it can be for reporting and data management. More fastidious use of data can also help teams overcome the “Facebook doesn’t work for sales” argument when you can show longer attribution and the multiple touchpoints of your customers.
Set up reporting and get to what matters
Here is a list of questions you can ask to get started on wrangling the data and reports that are going to matter most for your teams and stakeholders.
Who are the reporting stakeholders? Who needs to see the data? What do they care about?
There is so much time wasted when you are constantly making changes to marketing reports. One classic method to limit marketing report spew and churn is to segment who your key reporting audiences are and create dashboard views and reporting formats specifically tailored for reporting consumer segments. Think of it like audience targeting with summary data.
Understanding what matters to each stakeholder’s role involved in the reporting process is crucial for success: marketers, executives, sales teams and other stakeholders.
What business questions do we want to try to get answers to?
What questions do you need to answer for executives?
What reports are you using today? Where do they come from?
What questions are you asking today that you wish you had answers to?
Are there any dots you are hoping to connect?
As we start mapping reporting frameworks it’s important to determine success criteria, and what success looks like at each level of the organization: business, brand, and marketing. This will help define not only what’s desired but also what is actually possible.
Define some common vocabulary:
Creating a report glossary can be especially important working with sales teams. Make sure said teams are on the same page with the what and how behind the categorization of certain labels. Below is an example of a sales/marketing vocabulary list:
Contacts – New contacts added into the system (First name, last name, email address)
Subscriber – A contact who has opted in to receive content
Lead – Someone who has provided you more information about themselves
Marketing Qualified Lead (MQL) – A lead that is deemed ready for marketing messages, they have deemed themselves interested in our product and services, given additional information and opted into marketing
Sales Qualified Lead – A lead that is deemed ready for sales messages
Opportunity – Someone who is ready to buy and ready to receive sales correspondence
Customer – Someone who has purchased a product or service
Brand advocate – Someone who is actively advocating for your brand
Data integration and KPIs, setting up for success
As PPC marketers, we love to talk about our access to data, reporting tools, and everything shiny that’s new in the industry. But, the reality for most geeky marketers is, in our day-to-day working lives we manage data with our hands tied behind our back in a box. Yikes!
Managing the data can be complex and most of the time dots are not easily connected. Data ambiguity exists at all levels of sizes of companies. The larger, more complex, the messier it can be. Often we have to answer questions like why and how Facebook calculates views vs. YouTube, versus what analytics is reporting, and then define if there is ROI attached.
And, the reality is this is extremely difficult at times because our foundations are sometimes flawed, nearly every system, channel etc. can be calculating things a bit differently, and the systems don’t always connect and talk to each other properly. So, what the heck do we show what we need to and report on it? Similar to getting on the same page for vocabulary, we have to determine common ground for tracking.
These are some questions to ask to get you started:
What can we actually track (example if we can’t actually track sales, we can’t optimize for it)
What data do we truly have? What is currently being tracked? What else is possible to track?
What tools do we have?
What is trackable?
What is not trackable?
What blind spots do we have? Are there any ways we can remove those?
What third-party constraints exist?
Is there anything we should not track? (Example: exclusion list for IPs, etc.)
If it’s a missed opportunity that cannot be changed, note the disconnect, put it in your reporting framework. Determine what is going to be counted as what and for where and why. If there’s a way to integrate data not currently integrated, note the omission, see if it’s possible to integrate missing data and if it’s not possible, note the short circuit and make sure all stakeholders are on the same page.
Drilling down: What to actually put in reports
What marketing process questions do we seek answers to? What is the most valuable information? What campaigns, KPIs and overall metrics do we want to measure? And does this look different for any of the below?
What benchmarks do we have and wish to use?
What marketing questions do we want to summarize? What elements do we need to track and measure?
Campaigns
Websites
Third Party Channels or Websites
Social Channels
Audiences
What else?
Once we’ve put lists together, summarize key talking points in a brief and share findings with teams and stakeholders. Give other players a chance to contribute ideas and ask questions to help alleviate changes and challenging conversations once you start generating reports.
Presentation; it doesn’t have to be fancy
Presentation is important but the style doesn’t need to be fancy. It needs to be in a way that those using the data can understand. Sometimes the best reports are simple spreadsheets or diagrams, or for C-suite, it may just be a funnel image with a few bullets on performance that make it easy to digest.
Here are some examples:
Below is a funnel report that shows an increase in lead to MQL and SQL performance for a C-Suite leadership team.
Below is a simple spreadsheet report that was created in collaboration with a sales team leader to report to a sales team using a Google Spreadsheet (the team’s preference).
Or when you need to explain digital marketing reports to someone who hasn’t done digital marketing before:
Or when you have an engaged ecommerce team who wants to report on ongoing ROAs performance. This is an automated Power BI report.
Or an ecommerce team evaluating website performance to Revenue. This is also an automated report using Power BI.
A creative team report who is running CRO testing on a website landing page that needs to improve FTD Rate (conversion rate – free trials and demo submission rate).
Iterate and test your reporting effectiveness
Find the formats that work best for you and your team. It is also a good idea to treat the first round of reports as a draft, this is a good time to get feedback or make adjustments. Once you’ve done this, it will make the ongoing reporting process easier.
