Why You Need Call Tracking for a Full View of the Customer Journey

More and more interactions are shifting online, but for businesses with a longer sales cycle or have high dollar goods and services, phone calls are still an essential part of business. The source of these calls remained a huge blind spot for many marketers until the invention of call tracking. Read below for four reasons why you need call tracking to get a full view of the customer journey.

Get Granular Insights on Paid Media Efforts

Many people understand the benefits of using call tracking numbers on your website to understand how many people called from desktop, as opposed to click to call tracking, which as a general rule of thumb, will only capture mobile phone calls. But what you may not realize is that call tracking software can capture more than just the number of calls you get.

A call tracking software can capture your full URL, including UTM codes. Which means if you’ve put the name of your ad creative or copy in the utm_content field and the keyword in the utm_term field, you can now tell not only which campaigns drove the highest number of calls, but you can also drill down into which specific ads resonated most with your users. Feel free to get creative with your UTM fields for more granular insights on targeting options, ad placements, and ad size (remember that competitors can see your UTM codes, though). The best part a call tracking software that uses UTMs though? They’re platform agnostic, so even if you’re not using Google Analytics, you don’t have to miss out on insights.

Track All Calls from Your Google Listing (Google Business Profile)

Your website isn’t the only place online where people can find your phone number. If you have local businesses, you likely have local online listings, such as with Yelp, Facebook, and especially Google Business Profiles (FKA Google My Business). With its prominence on the search engine results page, your Google listing is the online listing likely to get the most traffic, but did you know their call reporting only reports on mobile calls? Enter your static call tracking number.

By accurately capturing the amount of calls you receive from your Google listings, you can help better understand the ROI of your online profiles. From there, you may decide it’s worth it to engage with a listing management partner to ensure your listing is always up to date and can take advantage of the latest feature releases. Or you may decide that you need to venture into paid search for your local listings.

Take Credit for Offline Conversions

Understanding how traditional mediums impact your bottom line is just as important as understanding how digital mediums impact your bottom line. If you’re running any form of direct mail, billboard advertising (OOH), or even partnering with another company for a sponsorship opportunity, I highly recommend you invest in call tracking.

Without call tracking, you are forced to rely on modeling and third-party attribution tools to help you understand what impact your offline advertising might have had. Instead, take a small part of that budget so you understand definitively what impact you had. Now, here I want to address an elephant in the room – this idea isn’t new, but not many people do it because they assume there is a high cost associated with call tracking. I’m personally partnered with Call Rail, which has plans starting at $45 for 250 minutes. With such a low cost to entry, this is a no-brainer on how to measure the effectiveness of your traditional mediums.

Understand the Full Impact of A/B Testing

Call tracking with A/B testing isn’t new, but most times it’s not used to its fullest extent. If you’re thinking that you should only use call tracking when you’re testing out the placement or color of a phone number, think again. I recommend you implement call tracking whenever you’re testing out any on page element and get the full story of user interactions on the page.

For example, when you changed the color of your button from red to blue, did you really lose any leads, or were people just more inclined to call? When they called, did they convert at a higher percentage? When you implement call tracking with all your A/B tests, you can answer these questions and so many more.

Summary

Getting a holistic view of the customer journey requires you to collect data points about online and offline behavior of potential customers. A call tracking software gives never before seen insight into the ROI of your traditional and digital advertising. By providing insights on how to better allocate your media spend and optimize your creative assets, call tracking software is the one tool in your toolbox that will pay for itself. I’ve used Call Rail for over five years now, and I’m happy to now be a Call Rail affiliate. To sign up for a trial, visit their website here. Please note, by clicking this link, I may get a small commission at no cost to you.

3 Ways to Use First Party Data in Paid Social

With the deprecation of 3^rd party cookies and other tracking technologies, marketers are looking more toward their first party data to optimize ads and create more efficient spend. When it comes to Facebook, here are three ways you can leverage your first party data.

Please consult your legal counsel before you use any first party data in marketing. This is not an article recommending how you should use first party data, rather it is an article outlining the ways that you could use first party data.

Offline Conversions

Offline Conversions for Facebook was a feature released in 2016 as ‘Offline Events’ that allows you to better measure the impact of your Facebook campaigns by importing – you guessed it – offline conversions.

There are two ways to take advantage of offline conversions. First, you can manually upload your first party data as a new data source in the Events Manager section of Facebook.

The second way to employ Offline Conversions is to automate your Offline Conversions through the use of an API or by using a partner integration, such as Square, LiveRamp, Marketo, and Segment/Twilio, among many others. Originally the Offline Conversions API was recommended, however now Facebook recommends using the Conversions API.

Facebook provides slightly more transparency than Google’s Offline Conversions in that Facebook allows you to see the split between online and offline conversions generated by your campaign. In contrast, Google groups both sets of conversions into one bucket.

First Party Data You Can Use:

Email
Phone
First Name
Last Name
External ID
Gender
Birthdate
Geo Information (City, State, Zip Code)

Examples of How to Use This Data:

Understand Offline ROAS
Use data with audience targeting to expand your reach
Use data with audience targeting to exclude existing customers from your campaigns
Use data with audience targeting to upsell or cross-sell existing customers
Optimize ads and campaigns

Source: https://www.facebook.com/business/learn/facebook-offline-conversions

Advanced Matching

In 2016 Facebook released Advanced Matching, a new way to better understand the customers you’re reaching with your Facebook ads. Facebook’s Advanced Matching has some overlap with Offline Conversions in that they both work off first party data to better identify which users converted from your ads, but it’s important to note that they’re not dependent on each other. Whereas offline conversions are additional events you send to Facebook, advanced matching simply augments the events you already send with first party data.

There are two ways to implement Advanced Matching. The first option is manual. This doesn’t require you to adjust any settings in Facebook, rather you modify the pixel itself. When you modify the pixel, you can include any one of Facebook’s first party fields, such as email or phone number, and map user inputs from forms to automatically populate these fields with those values. Manual Advanced Matching is appealing to some marketers because it doesn’t come with the same industry restrictions as Automatic Advanced Matching.

Much like Offline Conversions, you’ll control Automatic Advanced Matching by going into Facebook’s Events Manager. Since you’re not adding additional data sources, you’ll click on the name and ID of your existing data sources. Name and ID often refer to the Name and ID of your pixel, since that’s the primary data source your Facebook business account has. From there, visit the settings tab (again, because this is just an additional feature of already collected events) and turn on Automatic Advanced Matching.

Automatic Advanced Matching is appealing to some advertisers because it doesn’t require any code modification. Instead, Facebook automatically detects form fields on your website, such as those associated with log-in, registration, or purchase activity. From there, Facebook collects the field inputs and includes them with the events you’re already sending to Facebook. Facebook promises to not collect sensitive information, such as passwords or health data.

Ultimately Facebook recommends utilizing both methods of advanced matching, as Automatic Advanced Matching can collect more data, but Manual Advanced Matching doesn’t encounter the same limitations with technology (pixels deployed in an iframe or via an IMG pixel) or industries.

First Party Data You Can Use:

Email
Phone
First Name
Last Name
External ID
Gender
Birthdate
Geo Information (City, State, Zip Code)

Examples of How to Use This Data:

Better understand cross-device conversions (if someone sees your ad on one device and fills out a form from another)
Better understand user behavior even when said user is logged out on Facebook.
Optimize ads and campaigns
More accurate targeting – increase your Custom Audience size by matching website visitors to Meta users

Source: https://www.facebook.com/business/help/611774685654668?id=1205376682832142

Audiences

In order to improve targeting, since 2012 Facebook has allowed advertisers to create audiences. Facebook offers three types of audiences: Custom Audiences, Lookalike Audiences, and a Saved Audience.

Custom Audiences include people who have previously interacted with your business in one of many ways. The most common sources of Custom Audiences are below:

Website: People who have interacted with your website, as determined by the Meta pixel installed on your website.
Customer List: A list of your existing customers; this can include first party data as listed below.
Offline Activity: A list of people who have interacted with your business through offline channels (this was mentioned in the above section).
App Activity: People who have interacted with your app
Meta Activity: People who have interacted with your assets on Meta, such as videos, Facebook or Instagram pages, or lead forms.

From any of those Custom Audiences, you can build a Lookalike Audience. Unlike Custom Audiences, Lookalike Audiences include people who aren’t yet your customer, but you want them to be. By mirroring the interests, demographics, and behaviors of people uploaded with your Custom Audience, Facebook can find you people who are more likely to convert. This feature can be amplified with Facebook’s new “Advantage Custom Audience,” “Advantage Lookalikes,” and “Advantage Detailed Targeting.”

Please note, Facebook has contradictory information on which Custom Audiences can be used as source audiences for Lookalike Audiences. In the instructions for creating a Lookalike Audience in Ads Manager, they mention that a Lookalike Audience cannot be created with pixel, app, or Meta engagement data: https://www.facebook.com/business/help/465262276878947?id=401668390442328. In their article detailing what a Source Audience is, they list no such limitation: https://www.facebook.com/business/help/475669712534537.

As with any advertising platform, the more first party data you upload, the better your targeting will be. Facebook mentions that the source audience must contain at least 100 people, but 1,000 to 50,000 is recommended. People in these lists are excluded from being targeted in your Lookalike Audiences.

First Party Data You Can Use:

Email
Phone
Mobile App Identifier (IDFA, AAID)
First Name
Last Name
External ID
Gender
Birthdate
Geo Information (City, State, Zip Code)

Examples of How to Use This Data:

Expand your reach with Lookalike Audiences
Exclude existing customers from your campaigns
Upsell or cross-sell existing customers

Summary

If you’re considering using first party data with Facebook Ads, you can take advantage of Facebook’s Advanced Matching, Offline Conversions, and Audience features.

3 Ways to Use First Party Data in Paid Search

With the deprecation of 3^rd party cookies and other tracking technologies, marketers are looking more toward their first party data to optimize ads and create more efficient spend. Here are three ways you can leverage your first party data with Google Ads advertising.

Please consult your legal counsel before you use any first party data in advertising. This is not an article recommending how you should use first party data in advertising, rather it is an article outlining the ways that you could use first party data in advertising.

Customer Match

Launched in 2015, Google Ads provides Customer Match as a targeting option for advertisers across Google Search, YouTube, Gmail, and Google Display. When you upload your first party data to your Ads account, Google matches the data you provide against Google users. As an advertiser, you then have the opportunity to personalize ads and optimize bidding to increase your conversions. Whenever your customers sign into their Google accounts, they’ll then see any personalization you’ve applied.

Although applying your Customer Match segments used to be a manual process, as of 2022, Google now automatically includes Customer Match lists across any campaign that uses Smart Bidding.

In addition to using Customer Match to retarget your customers, historically you could also use them to target those users who aren’t your customers one of two ways. First, you could (and still can) upload prospects to Customer Match. The second method of using Customer Match to target non-customers – similar audiences – will be deprecated as of 2023.

Similar Audiences, often referred to as Similar Segments, was a feature built off the first party data in your Customer Match list. If your list was eligible, Google automatically created segments of users with shared interests or similar search behavior to the customers you’ve uploaded to the platform.

With the deprecation of similar audiences, Google is recommending advertisers opt for Audience Expansion and Optimized Targeting, released in 2019 and 2021, respectively. While Audience Expansion doesn’t use Customer Match lists, Optimized Targeting does. Optimized Targeting is a targeting feature that looks at attributes of converting customers to create a profile and target additional people based on that profile. While Google mentions they use conversion data and your landing pages to create this profile, an extensive list of the factors Google uses is unavailable. As such, consider this feature another black box of optimizations and targeting brought to you by Google.

First Party Data You Can Use With Customer Match:

Email
Phone
First Name
Last Name
Mailing Address (First Name, Last Name, Country, Zip Code)

Examples of How to Use Your First Party Data With Customer Match:

Re-engage dormant customers.
Exclude existing customers from your campaigns
Upsell or cross-sell existing customers
Target across devices

Learn more: https://support.google.com/google-ads/answer/6379332?hl=en

Offline Conversion Tracking

Offline Conversion Tracking became available in Google Ads in 2013 as a feature that allows you to better measure the impact of your Google Ads campaigns by importing – you guessed it – offline conversions. This feature worked one of two ways.

Offline Conversion Tracking via GCLID

The first way to utilize Offline Conversion Tracking is by storing and then importing back into Google Ads the gclid* attached to a website visit after a user completed a conversion. You can import this parameter back through a manual method or use one of Google’s partner integrations, such as Salesforce, Zapier, or Hubspot.

Offline Conversion Tracking via Phone Numbers

The second way to utilize Offline Conversion Tracking is through phone numbers. If you are using a Google forwarding number, Google then allows you to upload a file to capture any offline conversions that occurred via a phone call. This file contains the fields for a caller’s phone number, call start time, and conversion name and time.

First Party Data You Can Use With Offline Conversion Tracking:

Gclid (Google Click ID)
Phone Number

Examples of How to Use Your First Party Data With Offline Conversion Tracking:

Optimize campaign assets, such as ad copy
Optimize keywords
Better understand the impact of your Google Ads campaigns on business goals (with a 90-day lookback window)

Learn more: https://support.google.com/google-ads/answer/2998031?hl=en

Enhanced Conversions

Released in 2022, Enhanced Conversions is an additional feature Google offers that allows you to better measure the impact of your paid media campaigns by enhancing the data you send to Google Ads with first party data via your conversion pixel. Whether your pixel is deployed directly on the website or via a tag management solution, enhanced conversions allows you to scrape form fields to send to Google, where Google will then match the scraped data against one of two sources.

Enhanced Conversions for Leads

Enhanced conversions for leads is most similar to Google Ads’ Offline conversion tracking. When a user submits a lead form on your website, Google records the first party data you configured to be sent with your Google Ads pixel and stores it. Later, when your lead eventually converts, you upload the list of converted leads to Google. This list is referenced against the initial list of first party data collected.

This feature is separate from Google Ads’ Offline Conversion tracking in that it doesn’t require a gclid or a Google forwarding number. Another difference between Enhanced Conversions and Offline Conversion Tracking is the lookback window. While Offline Conversions has a lookback limit of 90 days, Enhanced Conversions doesn’t mention a lookback window at all. As a matter of fact, 30 days after you set up an automated feed of your leads through Enhanced Conversions, Google doesn’t tell you what percentage of your conversions came through your first party data vs. through your tracking pixels.

Enhanced Conversions for Web

Enhanced conversions for web works similarly to enhanced conversions for leads. When a user submits a lead form on your website, Google records the first party data you configured to be sent with your Google Ads pixel and stores it (please note – the word “stores” is used here because Google makes no mention in their documentation about deleting this data). However as opposed to enhanced conversions for leads, which matches this data against your first party data uploaded at a later date, enhanced conversions for web actually matches this data against all Google accounts.

Remember – your Google Ads pixels fire regardless of what traffic source a user comes from. By enabling first party data to be sent when your pixel fires, you’re now able to see users who interacted with your ads on different devices and ultimately converted, regardless of a user’s final traffic source.

First Party Data You Can Use With Enhanced Conversions:

Email
Phone
First Name
Last Name
Mailing Address (Street, City, State, Zip Code)

Examples of How to Use Your First Party Data With Enhanced Conversions:

Optimize campaign assets, such as ad copy
Optimize keywords
Take more credit for the impact your Google Ads campaigns are having on business goals (but less visibility into the customer journey)

Learn more: https://support.google.com/google-ads/answer/9888656?hl=en-GB

Summary

Vidhya Srinivasan, Google’s VP and GM for Ads Buying, Analytics and Measurement said it best – “The future is consented. It’s modeled. It’s first-party.” If you’re considering using first party data in Google Ads advertising, you can take advantage of Google’s Customer Match, Offline Conversion Tracking, and Enhanced Conversions features.

Note: *For those who aren’t familiar – gclid stands for Google Click ID. It’s a query parameter attached to your landing page URL every time a user clicks on your ad. While the exact details of this parameter are unknown, similar to the fbclid, it stores information about who clicked, what was clicked, when it was clicked, and where it was clicked.

Comparing GA3 and GA4 Metrics in Reporting

Google Analytics 4 (GA4) is replacing Universal Analytics (GA3). To prepare everyone for the change, I’ve created a series of blog posts to help users understand the difference between the two versions. Today’s post is a guide to helping you compare your GA3 reporting with your GA4 reporting. You’ll learn how increases and decreases of certain metrics may be more representative of your shift to GA4, rather than optimizations you made as a marketer.

Sessions

Sessions are calculated differently in GA4 than in GA3. Your sessions may be lower in GA4 because:

Session Timeout: While the automatic session duration is defaulted to 30 minutes in both versions of Google Analytics, GA4 allows you to extend that session timeout to 7 hours and 55 minutes. GA3 had a maximum timeout of only 4 hours. That means if you extended your session timeout to GA4’s maximum, someone that stepped away from your website for five hours would be counted as two sessions in GA3, while in GA4 they would be counted as one.
Midnight Restart: In GA3, all sessions break at midnight. In GA4, this isn’t the case. This midnight cutoff is based on the time zone you set within your Google Analytics settings. For example, let’s imagine you’re an ecommerce vendor who gets a lot of traffic from across the U.S. on Thanksgiving. While your fellow East Coast residents have likely gone to bed at midnight, it’s only 9:00 p.m. on the West Coast, and people may still be up shopping. Any session that spans both 8:59 p.m. PST and 9:00 p.m. PST will appear as though you’ve gotten 2 sessions in GA3. In GA4, the session will persist and more accurately reflect the one session.
New Campaign Parameters: If someone encountered two of your campaigns within a short time frame while browsing the web, each of those campaigns would cause a new session to start in GA3. However, in GA4, the session won’t break. For example, if someone first clicks on a paid ad, goes to your website, immediately goes back to Google and clicks on your Google Business Places profile, GA3 would count that as two sessions. In contrast, GA4 would only count that as one session.
Automatic Bot Filter: GA4 has a bot filter that removes known bots and spider traffic from your website. Google populated this list based on their own research and the International Spiders and Bots List. GA3 did not have such a filter.

There’s one reason, however that your GA4 numbers may be higher than your GA3 numbers:

Manual Filters: At the time of writing this post, GA4 does not allow for filters beyond IP filters. This lack of filters means that if you were filtering out hostname traffic in your GA3 property settings instead of configuring your Google Analytics tag within Google Tag Manager, your unwanted hostnames will appear in GA4.

Conversions

GA3 groups all website activity within a session. In doing so, Google also deduplicated any conversions within that session. If someone filled out a form 8 times, Google Analytics counted that as one goal completion. Conversely, GA4 isn’t a data model based on sessions. As a result, in GA4, those 8 form fills would be counted as 8 conversions. This difference means you will likely see an uptick in GA4 conversions compared to GA3, whether or not you make any website optimizations.

It’s also important to point out that because of this lack of deduplication, the transition to GA4 is a great time to test the validation on your forms. If your conversion event fires when you click a “submit” button without ever filling out a form, you likely need to tighten up your event logic to more accurately reflect your lead count.

Average Session Duration

To calculate the average time on page metric in GA3, Google would subtract the time you visited page 1 from the time you visited page 2. The inherent flaw was that Google Analytics had no way of measuring the time someone spent on your exit page because there was no subsequent pageview from which to subtract.

Because Google could never figure out the length of time someone spent on an exit page, GA3 always undercalculated average session duration. Sometimes this was undercounted by mere seconds. Other times, it was under calculated by 10 minutes or more.

GA4 measures time differently by sending a timestamp with every event. I dive deeper into the comparison in another blog post, but the bottom line remains the same. When you compare your average session duration metrics in GA3 to GA4, GA4 will likely have the higher number.

Bounce Rate

GA3’s bounce rate metric was largely influenced by the amount of event tracking you added to your Google Analytics account. The more events you had, the less bounces you likely had. As a result, there is a good chance anyone with lots of event tracking would naturally have a lower bounce rate in GA3. In contrast, account owners that did not implement any custom event tracking would naturally have a higher bounce rate.

In another blog post, I explain why GA4 bounce rate is a little harder to inflate. With GA4, event tracking will only decrease your bounce rate if you count that event as a conversion. The result is that if you had a super low bounce rate in Universal Analytics due to lots of event tracking, your GA4 bounce rate will likely be higher. Inversely, if your bounce rate was incredibly high in Universal Analytics, don’t be surprised if it lowers in GA4.

Summary

Below is an easy takeaway of what you should expect when you compare your GA3 to your GA4 reporting.

Metric	Results in GA4 Reporting
Sessions	Likely lower than GA3, but it depends
Conversions	Likely higher than GA3
Average Session Duration	Likely higher than GA3
Bounce Rate	It depends

Make sure to take this new measurement into consideration when doing analysis so you don’t inadvertently make an optimization decision based on incorrect assumptions. GA4 is continuously rolling out new features. Check back for updates or dive into other comparisons of Universal Analytics and GA4.

Comparing GA3 Behavior Flow and GA4 Path Exploration Reports

Google Analytics 4 (GA4) is replacing Universal Analytics (GA3). To prepare everyone for the change, I’ve created a series of blog posts to help users understand the difference between the two versions. Today I’m diving into the four main differences between the Behavior Flow report in Universal Analytics and the Path Exploration report in GA4.

Background

Released in 2011 under the name “Visitors Flow,” the Behavior Flow report was one of the few opportunities in Universal Analytics to view a customer’s journey throughout one session on your website. Despite the promise for true customer insights, the lack of customization and clumsy aggregation made analysis with this report often difficult. With the introduction of GA4, the Behavior Flow report got a new name – Path Exploration – and new functionality. Below I’ll talk about the four main differences between the Behavior Flow report and the Path Exploration report.

Difference # 1 – Explore More Than Six Paths

One major limitation of the Behavior Flow report in GA3 was that you could only get a detailed look at the top six interactions that occurred after a starting point of your choosing. For these top six interactions, you could see the next page path (or event) and continue to follow along to find the next eleven steps in the user journey. Beyond those top six interactions, though, data was limited. For the top seven interactions and beyond, you could only see the percentage of traffic a subsequent page received and that page’s drop off rate. Unlike the top six interactions, you couldn’t get any insight into the next three, four, or eleven steps in the customer journey.

With GA4 and the Path Exploration report, the limit of events you can explore is much higher. Instead of looking at the top six interactions, you can explore up to nineteen of your top interactions and the steps that followed those interactions.

Difference # 2 – Filtering Out Events

I previously mentioned the lack of configurability within the Behavior Flow report, and filtering out events was no exception to that rule. To provide more insightful analysis, sometimes you needed to exclude events within your customer journey. Even with strategic event tracking, some events only provide thought-provoking data in certain circumstances. For example, if you’re tracking scroll depth, that could be a great way to measure engaged on your blog. However, including this event in the Path Exploration could result in unwanted noise that distracts from a larger goal, such as the key CTAs someone used before submitting a lead form.

With GA4, the pencil icon appears next to each step, allowing you to filter out events you don’t believe are relevant to your particular analysis.

The pencil icon allows you to filter out certain events in the GA4 Behavior Flow report

Difference # 3 – Exploring Paths in Two Different Directions

GA3 limited the Behavior Flow report to one direction – forwards. While you could tell the steps someone took to prior to completing a goal with the aptly named Reserve Goal Path report, this was even more limited than the Behavior Flow report. With the Reserve Goal Path report, you could only see the three previous steps to completing a goal. Furthermore, this was limited to pages that someone visited prior to completing a goal – there was no granular drill down into custom events you may have tracked.

GA4’s Path Exploration report provides greater flexibility, allowing you to analyze different user paths based on either a starting or ending point. This means you can tell the paths someone tool after they visit your landing page, or you can tell what pages or CTAs someone interacted with prior to submitting a form.

GA4 allows you to choose an ending point instead of a starting point within the Behavior Flow report.

Difference # 4 – Exploring Paths Across Sessions

One of the main selling points of GA4 was a new data model that was event based instead of GA3’s session based data model. This meant that any report you viewed would not be constricted by sessions like it was in GA3. Instead, you could see the full customer journey, regardless of how long it took.

We see this logic applied within the Path Exploration report, which shows the customer journey across sessions. In contrast, GA3’s the Behavior Flow report only included activity within one session.

GA4 allows you to explore user behavior across sessions, as evidenced by the multiple “session_start” events

Summary

The Path Exploration report in GA4 is a huge upgrade from the Behavior Flow report in GA3, with more visible data, no session limitations, filtering, and the ability to analyze events from a starting or ending point. GA4 is continuously rolling out new features. Learn more about GA4 reporting metrics with the blog articles below:

Comparing the Measurement of Time GA3 vs. GA4

Google Analytics 4 (GA4) is replacing Universal Analytics (GA3). To prepare everyone for the change, I’ve created a series of blog posts to help users understand the difference between the two versions. Today I’m going to walk through how time is measured in GA3 with the average session duration and average time on page metrics and compare that to how time is measured in GA4 with the average session duration and average engagement time per session metrics.

Measuring Time in GA3

With Universal Analytics (GA3), the average time on page and average session duration metrics were wildly inaccurate. This is because time measurement in GA3 was inherently flawed. Instead of collecting timestamps intermittently throughout a session, a timestamp was only recorded with a pageview. To calculate the average time on page metric, Google would subtract the time you visited page 1 from the time you visited page 2. The inherent flaw was that Google Analytics had no way of measuring the time someone spent on your exit page because there was no subsequent pageview from which to subtract.

Now, many marketers are big-picture thinkers. Sure, the number may not have been 100% accurate, but it gave us a starting point, right? Wrong. Numerous blog articles go into this in-depth, providing real-life examples, but it took a metaphor before I truly understood how inaccurate and misleading GA3 time metrics could be.

The way Google Analytics measured time in GA3 was similar to measuring the distance it takes to get somewhere using stoplights. While most of us encounter stoplights when we drive, think about the exceptions to the rule. For example, this method doesn’t account for highways, which don’t have stoplights. It also doesn’t account for the time it takes you to drive to a business that’s located right off the highway. It also doesn’t account for the fact that sometimes you’re driving across the state (or across several states for my non-Texan readers). Below is a graphic that measures the time to get from Houston to the DFW airport realistically vs. using GA3’s time measurement logic.

Measuring Time in GA4

Measuring time in GA4 is leaps and bounds more accurate. Not only is a timestamp sent with every pageview, but it’s also sent with each event that occurs. And with GA4’s out-of-the-box events, such as outbound links, file downloads, and form fills, you’re already halfway to a more accurate measure of time.

Even beyond the events you can configure for your GA4 property, Google sends some automatic events. One of these automatic events is a user_engagement event that is dispatched with the page onunload event. This means that Google Analytics understands if a user closes their browser or navigates away from the page, giving us another layer of insight into how users engage with your website that was never available in GA3.

Now let’s compare the two with real numbers. In GA3, if someone opened your webpage, browsed for 1 minute, then spent 30 minutes on another website before coming back to your website and visiting a second page. After 10 minutes on the second page, they exit the website. With GA3, their average session duration and their average time on page would be 31 minutes. In GA4, the session engagement time (the time your website was in the foreground) would be 1 minute and the average session duration would be 11 minutes (1 minute on the first page, plus 10 minutes on the second page).

Below is a comparison of the two time-based metrics in Universal Analytics (GA3) compared to GA4’s time metrics. GA4 released the average session duration metric on November 29, 2022.

Average Time on Page	Average Session Duration	Average Engagement Time	Average Session Duration
GA3	GA3	GA4	GA4
The timestamp of one pageview, subtracted from the timestamp of the subsequent pageview. If there is no subsequent pageview, the time on page will be measured as 0.	The average duration (in seconds) of users’ sessions, regardless if your webpage is in the foreground or background. This excludes the time on page for the last visited webpage.	The average time that your webpage is in the foreground.	The average duration (in seconds) of users’ sessions, measured from the initial session start to the unload event.

Based on the differences in measurement, it is highly likely that your average session duration will increase when you switch to GA4 due to the underreporting in GA3. As such, I highly recommend you don’t compare the two numbers.

Summary

Time measurement is another major upgrade you can expect from GA4, giving you more insight into which content actually engages users. GA4 is continuously rolling out new features. Learn more about GA4 reporting metrics with the blog articles below:

Comparing Engagement Rate in GA4 and Bounce Rate in GA3

How Bounce Rate Was Calculated In Universal Analytics

Every time data was sent to Universal Analytics (GA3), it was called a “hit.” There were different types of hits, such as page hits, event hits, and ecommerce hits. Pageviews (page hits) and events (event hits) by default in GA3 were counted as an “interaction” hit. In contrast to a “non-interaction” hit, an interaction hit signifies to Google that the action occurred was of significance. Whenever a user only has one interaction hit in their session, it’s considered a bounce – someone left your website without completing two significant interactions. As a result, bounce rate is the percentage of sessions that did not have a second interaction hit.

Based on this calculation, any Google Analytics account owner who had a large amount of event tracking on their website would naturally have a lower bounce rate. Account owners that did not implement any custom event tracking would naturally have a higher bounce rate.

How Engagement Rate in GA4 Is Different

When GA4 was first released, bounce rate wasn’t even included as a metric. This was done in an effort to put the focus on customers who actively consuming your website and contributing to your bottom line instead of focusing on users who abandoned your website. In short, GA4’s focus was all about engagement.

Unlike bounces, engagements had stricter rules. A session can only be considered engaged if a user had 2+ pageviews, a conversion, or a time threshold of your choosing, ranging from 10 to 60 seconds. This means that you can’t add event tracking in GA4 to lower your bounce rate, unless you count that event as a conversion.

Engagement rate is the rate of engaged sessions over total sessions. Due to increasing public pressure, Google finally released bounce rate in July 2022. However, the calculation is slightly different from the definition in Universal Analytics. In GA4 bounce rate is now just 1 – engagement rate.

Because bounce rate was so easy to inflate in Universal Analytics, if you had a super low bounce rate in Universal Analytics, don’t be surprised if it gets higher when you make the transition to GA4. Inversely, if your bounce rate was incredibly high in Universal Analytics, don’t be surprised if it lowers in GA4.

Looking Beyond Engagement Rate – Active Users & Session Engagement Time in GA4

Engagement within GA4 didn’t stop at engagement rate. Google also used this concept to help define two new dimensions – active users and session engagement time. The active users dimension describes those users who have had an engaged session at any point. This metric is helpful if you’re trying to separate out bot traffic from actual users.

Session engagement time sounds very similar to average session duration, but it’s important not to confuse the two metrics. Unlike Universal Analytics, GA4 is able to measure when a user closes their tab, navigates to another website, or reloads the current page. More simply, Google knows when you’re actively engaging with a website versus when that website is in the background. GA4 uses this information to give us session engagement time – the time a user actively had your website in the foreground.

Summary

The shift from bounce rate to engagement rate is a big one, but a good one. Lean into the change and begin focusing on users who are driving your bottom line with engagement related metrics. GA4 is continuously rolling out new features. Learn more about GA4 reporting metrics with the blog articles below:

When to Use Funnels vs. When to Use Path Exploration in GA4

Google Analytics 4 (GA4) is replacing Universal Analytics (GA3). To prepare everyone for the change, I’ve created a series of blog posts to help users understand the difference between the two versions. Today’s post is a brief comparison of the Funnel report and the Path Exploration report in GA4. Both reports provide you with insight into a customer’s journey throughout your website; however, there are times when it’s better to use one report over the other.

9 Reasons to Use the Path Explorations Report

I want to learn more about the customer journey in general, regardless of whether someone completes the action I want them to complete.
I want to see when someone starts and ends a session throughout their journey on my website.
I have a specific ending point I want for a user, but not necessarily a specific starting point.
I want to learn about the wide variety of paths someone took to complete a desired action.
I have a specific starting point I want for a user, but not necessarily a specific ending point.
When looking at how a user progresses through the website, I want to easily switch between visualizations of different breakdowns.
I want to see the count of events, not just active users. If one user completes an event twenty times, that’s okay with me.
I want to compare how the total users progress through the website and compare it to active users. I realize this may include some bot traffic.
I want to see where someone goes on my website whenever they’re not completing the action I want them to.

9 Reasons to Use the Funnel Report

I have a specific list of actions I want a user to complete in a specific order.
I want to quantify drop offs in a multi-step process by pulling metrics such as abandonment rate and completion rate.
The analytics terminology sometimes loses me – I want to change the name of “Step 3” to “Fill Out Name” in my report.
I want to understand how drop off in a multi-step process differs between session campaigns.
I want to understand how drop off in a multi-step process differs between session sources.
I want to understand how drop off in a multi-step process differs between first acquisition campaigns.
I want to see the time elapsed between each step in a multi-step process, even though it may extend beyond one session.
I want to timebox my desired actions – I only care about people that went from Step 1 to Step 2 in a certain about of time.
I want to see an exact date that drop offs in a multi-step process sharply increased or decreased.

Summary

The Path Exploration report and the Funnel report have a lot of overlapping features. Still, it’s important to know why you default to using one report instead of the other. GA4 is continuously rolling out new features. Check back for updates or dive into other comparisons of Universal Analytics and GA4.

4 Reasons You Shouldn’t Use Google Analytics UX Metrics

I heart Google Analytics. Really. There is so much benefit in using it across departments to understand gaps in your content, design, development, and user experience. However, as versatile as Google Analytics may be, in some cases it’s better used as a supplemental diagnostic tool instead of your primary diagnostic tool. Given the overabundance of articles that highlight “X Ways You Can Use Google Analytics to Improve UX,” I wanted to provide a different viewpoint – 4 Reasons You Shouldn’t Use Google Analytics UX Metrics”

You Can’t Accurately Tell Demographics

Google Analytics launched the Demographic and Interests reports in 2013. These reports allowed you to see user demographics by enabling advertising reporting features for your Google Analytics property. Google collects demographic information three ways: Third-party DoubleClick cookie, Android Advertising ID, or IDFA (for users who specifically opt in or haven’t yet upgraded to iOS 14). With the continuing deprecation of third-party cookies and the increasing privacy restrictions around mobile device identifiers, it’s important to note that this reporting only shows a subset of your users. Even in Google’s test Google Analytics property, reporting shows for 40% of all users.

The fact that you’re only receiving a portion of demographic information doesn’t seem completely detrimental to your UX analysis. However, having that extra demographic information can be beneficial. As personalization comes to the forefront of every marketer’s roadmap, understanding how user behavior differs between segments will allow you to provide more meaningful personalization and a more meaningful A/B testing roadmap. A tool specifically designed to measure UX may offer the user the ability to self-identify, giving you that granular segmentation data.

Scroll Depth Doesn’t Allow for Analysis At Scale

Scroll depth is one of GA4’s enhanced measurement metrics. Enhanced measurement means this particular reporting feature can be enabled with just the click of a button. This dimension gives marketers a layer of insight they never had before with no additional coding, but it has limitations. With enhanced measurement, the scroll depth event will only fire when a user has reached 90% of the way down a page. While this doesn’t render the feature completely useless, it doesn’t give you any indication of people actively engaged and willing to scroll 60% or 80% the way through your content.

The 90% limitation also makes UX analysis at scale a little tricky. For example, 90% of a page scrolled on desktop may only be 50% of the same page scrolled through on mobile. Even within mobile devices, content on your iPhone pro-max will look a little different than your iPhone mini. This issue gets compounded with your different content lengths. While some pieces of your content are lengthy 1,000+ word blog articles, other content pieces could be your short and succinct 250 word “Contact” page.

Before I go further, I’d like to demonstrate the context word count adds with a chart. Below are four blog posts and the percentage of users who scrolled 90% of the way down the page.

Page Name	Percent of users who 90% Scrolled (Desktop)	Percent of users who 90% Scrolled (Mobile)
/blog-post-1	85%	70%
/blog-post-2	30%	15%
/blog-post-3	40%	20%
/blog-post-4	75%	65%

Based on the data, you’d say that blog posts 2 and 3 are uninteresting to users. This may not necessarily be the case, though. Let’s look at the same data set with a “Word Count” column.

Page Name	Word Count	Percent of users who 90% Scrolled (Desktop)	Percent of users who 90% Scrolled (Mobile)
/blog-post-1	250 words	85%	70%
/blog-post-2	1,000 words	30%	15%
/blog-post-3	900 words	40%	20%
/blog-post-4	400 words	75%	65%

Unlike the first chart, it’s obvious to see that users don’t like to read more than 400 words. The length gave us additional context on the user experience not built into Google Analytics UX metrics.

Now, in order to get around the first two issues with the scroll depth event, it’s possible to build your own custom scroll event (you’d have to anyways if you were using a Single Page Application like react). It’s also possible to add a custom dimension that t-shirt sizes your articles (small, medium, large, etc.). These custom events will get you closer to understanding how people consume your content, but it still leaves a gap when analyzing the UX on your page.

What if people spend 4 minutes on paragraph five and 6 seconds on paragraph two? Where did people stop reading your content? Do they always leave the page when your Newsletter Subscription pop up appears? Are the ads in the middle of your blog article a deterrent? Unfortunately, these aggregated UX insights can’t be built into Google Analytics UX metrics.

Average Time on Page Is Unusable

I wrote another blog post comparing the measurement of time in GA3 vs. GA4. To calculate the average time on page metric in GA3, Google would subtract the time you visited page 1 from the time you visited page 2. The inherent flaw was that Google Analytics had no way of measuring the time someone spent on your exit page because there was no subsequent pageview from which to subtract. This flaw meant average time on page was always underreported or not reported at all.

While GA4 stepped up their game in how they measured time, average time on page, at the time of writing this, is still inaccessible to the average user with the GA4 interface. Advanced users who have knowledge of BigQuery can calculate the difference between pageview timestamps, but those without SQL knowledge have to use built-in GA4 metrics, like engagement rate.

In GA4, engaged users are ones who a) had a visit longer than 10 seconds b) visited 2+ pages or c) completed a conversion event. This metric doesn’t allow for more granular UX analysis, though. You’re either engaged or you’re not. A user who commits to reading your blog for 11 seconds is considered just as “engaged” as a user who reads your blog for 10 minutes. Instead of relying on time metrics in Google Analytics, a heatmapping software can provide more accurate UX metrics.

Unable to Determine Where Clicks Occurred

Within Google Tag Manager, you can fire a Google Analytics event on every single click that happens on your website. This is powerful for those analysts who get the question “How many people clicked this random button on this specific page?”. This feature is supercharged when developers standardize their code and you insert dynamic variables into your event names, such as link text, button classes, IDs, and form elements into those events.

As much data as the All Click Events tag collects, it has limitations when trying to perform aggregated reporting on UX metrics. The main limitation is that users don’t always click within specific buttons or areas of your website. This is best illustrated with an example. If you have a large image on your homepage (your website hero), it could have a standardized class of “class=background.” Because your hero is such a large image though, you won’t be able to tell the difference between a click in the top left of the hero and the bottom right of the hero.

Another example of the limitations faced with click tracking is when users try and click on text on your website. If someone is trying to click on text within a paragraph that isn’t clickable, your event tracking would only show that someone clicked on the paragraph, not that a specific word is misleading someone into thinking it’s a link. In contrast, using a heatmapping tool will give you a better picture of where people are clicking, faster, with less work.

Summary

Google Analytics is a fantastic tool, but take caution when using it to analyze your user experience. The lack of segmentation, aggregation, context, and granular insights can mislead you into thinking something is working, when it’s actually hurting the customer experience and your website conversion rate. I recommend always supplementing your Google Analytics data with a heatmapping tool like Hotjar to resolve the gaps in data mentioned above and help give you a more holistic view of user experience on your website. I’ve used both the free and business plans for over 6 years now, and I’m proud to now be part of their partner program. Sign up for Hotjar using this link and get an extended business trial. By signing up using this link, I may receive a commission at no cost to you.

Why Is An Old Google Analytics Code Bad?

I’ve been in the world of SEO for nearly seven years now, becoming a self-proclaimed expert Googler on everything from Excel formulas to home improvement hacks. However, when faced with the challenge of telling a client why having Classic Google Analytics on their website was not optimal, the only search terms that came to mind were “Why Is An Old Google Analytics Code Bad?”

After my ever-so-sophisticated search, I found myself getting lost in Google Analytics developer documents that were way over my head and articles that hadn’t been updated since Universal Analytics first made its debut out of beta. My loss, however, is your gain.

This guide will give you a high-level look at the different iterations of Google Analytics and why you really should update your Google Analytics code. If you’re looking for a detailed Google Analytics timeline, I highly recommend this Google Analytics article by BCS Blog.

Google Analytics Infancy: Urchin + Google

Google Analytics did not start at Google. It was started by Urchin Software Corp, which Google acquired in 2005. If the name “Urchin” sounds familiar, that’s because it should. Urchin is the “U” in UTM Coding (you can read more about UTM Coding here).

How Can I Tell If I’m Using urchin.js?

The easiest way to see if you’re using Urchin is to search the source code for google-analytics.com/urchin.js– don’t be surprised if you don’t find it, though. BuiltWith estimates that only 7,212websites still use Urchin for their analytics.

Google Analytics Childhood: Classic (Legacy) Google Analytics

In April 2007, Google released the first version of ga.js, a synchronous script that was faster, more secure, and gave users access to more features than its Urchin predecessor. In addition to the library released in 2007, there were two ga.js upgrades released in 2009 and 2012. In 2009, Google updated the tracking script to be asynchronous, preventing it from slowing down other resources on a website. In 2012, Google updated the ga.js script to allow for better targeting of Display ads. All versions of ga.js are now referred to as Classic Google Analytics or Legacy Google Analytics because the library is now a legacy library.

How Can I Tell If I’m Using Classic Analytics?

The easiest way to see if you’re using Legacy Google Analytics is to search the source code for google-analytics.com/ga.js. BuiltWith estimates that 6.8 millionwebsites still use Legacy Google Analytics. If you deploy your Google Analytics code through Tag Manager, check inside Tag Manager for a Classic Google Analytics tag.

Classic Google Analytics in Google Tag Manager

Why Should I Change from urchin.js to ga.js?

Note: MoreVisibility wrote an excellent article on this that goes into this in much more detail. I highly recommend checking it out here:

Faster.When implementing your tracking script, you’ll notice that both the Urchin and Google Analytics code reference a script source. When you load urchin.js (https://www.google-analytics.com/urchin.js), you’ll notice the code is near 700 lines. In contrast, when you load ga.js (https://www.google-analytics.com/ga.js), you’ll notice the code is much smaller – only 84 lines!
More Secure.Google Analytics doesn’t store any PII (Personally Identifiable Information). However, it does need to distinguish User 1 from User 2. By increasing the safety of their namespace, this means it’s harder for Google to tell that you’re you.
Access to More Features. Event tracking, real-time reporting, multi-channel funnels, flow visualizations, data import, custom alerts, custom reports, and data import are a few of the new features that came with the migration to Classic Google Analytics.

Google Analytics Adolescence: Universal Analytics

Although we may use Universal Analytics and Google Analytics interchangeably now, Universal Analytics was considered very different from previous iterations of Google Analytics when it was first announced in October 2012 and then released from Beta in March 2013. Universal Analytics opened up a world of possibilities for website owners and allowed for more customization of our website analytics than ever before.

How Can I Tell If I’m Using Universal Analytics?

Much like Classic Google Analytics, there are two ways to check if you’re using Universal Analytics. The first way is to see if you’re using Universal Analytics is to check if you have the Universal Analytics tag within Google Tag Manager.

Universal Analytics in Google Tag Manager

If your Analytics account is more than six years old, there’s a strong chance you started with Classic Google Analytics. If this is the case, your transition would be a two-step process, and you’ll want to check more than your website code. When you open up the Google Analytics interface, you want to make sure it doesn’t give you a warning that the Universal Analytics transfer hasn’t started. If you don’t see the warning below, you’re good to go!

Why Should I Change from ga.js to analytics.js?

With the release of analytics.js, Google not only allowed users more configuration of existing elements but also more advanced integrations with offline data.

Adjust Your Time Outs & Exclusions: With analytics.js, you get to customize the time outs for your campaigns and your sessions. By default, a campaign times out at six months, and a session times out at 30 minutes. You also get to add search engines so it doesn’t appear as referral traffic, exclude search terms so the traffic is attributed as direct instead, and exclude referral sources so you can better prevent self-referrals.
Better Tracking Across Domains: In Classic Google Analytics, tracking across subdomains required additional configuration because the Google Analytics cookie wasn’t set to the highest possible level. Google fixed this with the automatic cookie domain configuration in Universal Analytics, and now users can track across subdomains without any extra work.
Better Tracking Across Devices: The introduction of the User-ID allowed any website with a login to start tracking users across devices. While you still needed a developer to implement this feature, this gave a better picture of the customer journey.
More Custom Data, Less Manual Work: Classic Google Analytics gave us data import and custom variables. Universal Analytics gave us V2 of those features. With the measurement protocol, we wouldn’t have to manually import outside data anymore, it could be done via a feed. Universal Analytics also transformed custom variables into custom metrics and custom dimensions, which meant easier reporting and easier implementation for website owners (a nice custom variables vs. custom dimensions list can be found here).

Google Analytics Awkward Post-Grad Years: Global Site Tag

A history of Google Analytics wouldn’t be complete without talking about gtag.js, the fourth library used by Google Analytics. This library was released in beta in August 2017 and is now the recommended Google Analytics library, unless you’re deploying Google Analytics through Google Tag Manager.

Admitted author bias: When I discovered the benefits of deploying my tags through Google Tag Manager, I ignored any new Google Analytics libraries. After looking at BuiltWith trends, however, it appears I am in the minority. 12.2 million websites use the Global Site Tag, while only 9.3 million websites use Google Tag Manager. If you are hesitant to switch over to Google Tag Manager, I totally understand, but I implore you to dig deep within and find the courage to make the switch. Even Google recommends Google Tag Manager over the Global Site Tag. I have an introductory piece on GTM here that should make it a little less scary.

How Can I Tell If I’m Using the Global Site Tag?

The easiest way to see if you’re using the Global Site Tag is to search the source code for www.googletagmanager.com/gtag/js.

Why Should I Change from analytics.js to gtag.js?

Again, I really recommend you go straight to Google Tag Manager, but if you insist on carrying on without it, I can’t stop you. Here’s why gtag.js could be a nice upgrade from analytics.js for you:

One Tag For All Google Products: Unlike previous libraries, gtag.js isn’t a library solely for Google Analytics. Rather it’s a wrapper library that contains your analytics.js (Google Analytics) script, your conversion.js (Google Ads) script, and other Google scripts. This helps consolidate tracking because now all Google products will use the same event command.
Grouping For Multiple Websites: The Global Site Tag makes it easier to send analytics hits to multiple accounts at once without duplicating tags, by providing a feature called “grouping.” Grouping is exactly what it sounds like – you can place multiple accounts into one group and can instruct Google to send hits to everyone in that group.
DataLayer: The dataLayer was previously only used by one Google product – Google Tag Manager. With the introduction of the Global Site Tag, the vendor-agnostic data container is now available to a wider audience, and developers can now avoid the DOM scraping that was previously needed to achieve the same results.

Summary

Outdated code is not only less reliable, but it often means you miss out on new and cool features. If you’re still currently using urchin.js, ga.js, or analytics.js, there’s reason to upgrade to the latest implementation of Google Analytics, whether its through the use of the Global Site Tag or Google Tag Manager. Both will help better prepare you for the next iteration of Google Analytics, Google Analytics 4, which was released in beta in October 2020.