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The Ultimate Guide to Google Analytics in 2022

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The Ultimate Guide to Google Analytics in 2022

Are you confused — even intimidated — by Google Analytics? Good news: you’re not alone. GA is notoriously complicated, and with the latest release — GA4 — things are just about clear as mud.

In fact, when I first started to delve into GA’s waters, I wondered if I’d ever truly get it. There were so many concepts to learn and reports to run. How did people ever conquer this thing?!

Lots and lots of reading plus some trial and error, it turns out.

I’m not saying I’ve reached total mastery — there’s always something new to pick up — but I’m vastly more comfortable.

And I want you to be, too. So, here’s the cheat sheet for Google Analytics/GA4. This guide might be long, but it’ll take you from zero to hero in ~7,000 words. And if you still have questions, let me know! I’m @ajavuu on Twitter.

Before we move forward, you should know that there are currently two versions of Google Analytics: Universal Analytics and GA4.

What is Universal Analytics?

Universal Analytics is the current version of Google Analytics. If you set up your Google Analytics account before October 2020, you likely have Universal Analytics.

What is GA4?

GA4 is the latest version of Google Analytics. It has a slightly different UI and the reports, tools, and features have been upgraded. If you created a Google Analytics account after October 2020, you likely have GA4.

You’ll know if you have Universal Analytics or GA4 by the way your home screen looks. On the left is Universal Analytics and on the right is GA4.

Google Analytics: Universal Analytics vs. GA4

Other marketing analytics options, such as HubSpot, can give you all the data you need with much less work.

Now, what steps will you need to follow when setting up GA? Good question.

Before you start using Google Analytics, you’ll have to set up a Google account. This means you must have a registered Google Account email address and password.

Once you’ve created a Google account, that doesn’t mean you automatically have access to GA — rather, you have to register for Analytics (which we’ll review how to do in the next section). But the important thing to note as you go to set up GA is that you can only access the tool by using a valid Google account.

Here are the steps on how to use Google Analytics for your website. (I’m using my class reunion website as an example.)

Step 1: Create a Google Analytics account.

First, you’ll have to create a Google Analytics account. Or, sign in to your current account.How to Create a Google Analytics Account: Step 1

Step 2: Add the name, URL, and industry of the website you want to track.

Choose which account you want to add the property to.

How to Create a Google Analytics Account: Step 2a

You should create and name your Property at this point and enter the website’s URL as well as industry and reporting time zone. Then you’ll be able to Create and Finish this step of the process.

How to Create a Google Analytics Account: Step 2b

Step 3: Add a Data Stream or View to your property.

Note: GA4 no longer uses “Views” but it instead has “Data Streams” with similar functionality. Keep this in mind when sollowing these steps using Universal Analytics.

To add a view to your Universal Analytics account, go to the account and property you want to add a view to — use the menu to Create a View, name your view, select the type of view (web or app), and answer a few other questions. Remember, you can add up to 25 views to a property in GA.

How to Create a Google Analytics Account: Step 3a

To add a data stream to your GA4 account, go to the account and property you want to add a data stream to — use the menu to add a Data Stream. Choose or add a stream, and save it.How to Create a Google Analytics Account: Step 3b

Step 4: Add your tracking code directly after the <head> tag of your site.

When you create a property, you’ll have access to a unique ID for tracking and a global site tag (code you need to add to each site page you want to measure). This is how you’ll be able to collect data in your property.

Then, paste your global site tag right after the opening <head> tag on each site page you plan on measuring.

You’ll be asked to choose your type of site (static, dynamic, web hosting, Google Tag Manager) so that you can set up the data collection accurately.

(For more, read our guide to installing the Google Analytics tracking code on your site.)

Step 5: Visit your GA portal and verify the code is working.

Lastly, verify your code is working. You can do this by looking at the Real-Time reports section while clicking around on your site in a different tab or on your phone. The report should show at least one visitor to the site (that’s you!)

How to Create a Google Analytics Account: Step 5

And that’s pretty much it! After that review, you may be wondering the following:

Do you need to add the GA code to every page of your site?

That’s a lot of manual work — especially if your website has more than 50 pages. Plus, what happens when you create new pages? Do you need to add the tag every time?!

The short answer is: no.

The longer answer: you only need to add the tag to every page template. So, if you have one page type on your site (meaning every individual page uses the same header module), you only need to add it to that module — and it’ll be applied to every page.

If you have two page types, you’d need to paste the code into the two separate header modules. Three page types? Three header modules.

And if you use a CMS like HubSpot, this task is even easier. These tools come with a separate field where you paste your tracking code just once. HubSpot users can follow these simple instructions for adding GA.

Additionally, to set up GA properly, you’ll want to understand the various layers of the tool — specifically, the hierarchy.

Understanding the Basics of Google Analytics

Google Analytics is made up of many parts, so it’s important to have a clear lay of the land as you begin learning. This section is dedicated to Google Analytics guidelines to help you master the basics of this powerful tool.

Google Analytics Hierarchy

Here’s a look at the GA hierarchy. Remember, Universal Analytics uses “Views” while GA4 uses “Data Streams”, so both are demonstrated in the visual below.

Google Analytics Hierarchy

Let’s dive into each of the sections within the hierarchy.

1. Organization

The organization is the highest level. It represents a company. For example, our organization is HubSpot, Inc. One organization can encompass multiple GA accounts.

Organizations are recommended for larger businesses, but not mandatory.

2. Account(s)

Accounts are not optional. Using Google Analytics requires at least one (sometimes several) accounts.

An account doesn’t mean a user account. I can log into the HubSpot Google Analytics accounts using my Google email ID. HubSpot’s head of technical SEO can also log into the same account using his Google email ID. Our historical optimization specialist can also log into the same account using his Google email ID.

Important details:

  • You can assign one property to each account or multiple properties to one account. Every account can hold up to 50 properties.
  • You can give user permissions for an entire Analytics account, a property in an account, or a view in a property.

You might be wondering, “What’s better: creating a new account for every property or adding every account to the same property?”

It depends on your use case and goals.

For example, suppose you have one website — the Stark Industries corporate site — and five subdirectories, including the Stark Industries blog, careers section, media resources, case studies, and investor relations information.

You want to create separate properties for each subdirectory so the people on each team can look at how their portion of the site is performing, as well as the larger site.

But maybe you have another site that discusses Tony Stark’s work with S.H.I.E.L.D. You want the S.H.I.E.L.D. team to see data for this subdirectory, but you don’t want them to see data for the rest of the website. You create a new account and property for the S.H.I.E.L.D. site.

3. Property

A property is a website or app. Each property can support up to 25 views.

4. View

At the minimum, you need two views per property:

  • One with zero configuration — essentially the “raw” version of the view
  • One with filters set up to exclude any traffic from within your company (i.e. a filter for your IP address) as well as bots and spam traffic

A view only captures the information after your filters and configured settings have been applied. And once you delete a view, that data is gone forever. For those reasons, it’s critical to keep an unfiltered view of your data.

5. Data Stream

A data stream in GA4 is a flow of data that gives you more insights into how your site is performing across different operating systems. There are three preset data streams you can choose from including web, iOS, and Andriod; or you can choose to create a custom data stream.

6. Google Analytics Dimensions and Metrics

To use GA successfully, you need to understand dimensions versus metrics. I’ve found the easiest way to think about it is:

  • Dimensions: categorical variables. Simple examples include names, colors, and places.
  • Metrics: quantitative variables. Basic examples include age, temperature, and population.

Or as my Data Analytics professor put it, “Metrics are what you can do math on.” Not the most eloquent phrasing, but it works.

Dimension Examples

  • Browser
  • Location
  • Landing page
  • Device
  • Customer type

Metric Examples

  • Sessions
  • Pageviews
  • Conversions
  • Bounce rate
  • Session duration

In any GA report, your dimensions are your rows and your metrics are your columns.

Google Analytics Metrics Example

Custom Dimensions and Metrics

GA lets you create custom dimensions and metrics from Analytics data plus non-Analytics data. To give you an idea, suppose you track the membership type of customers who have created an account in your CRM. You could combine this information with page views to see page views by member type.

Or maybe you run a blog. If you want to understand how audience engagement impacts other metrics (like conversions, pages per session, etc.), you could create three custom dimensions for each type of reader:

  • Advocate: user who shared one-plus posts on social media
  • Subscriber: user who signed up for your email list
  • Customer: user who purchased premium access

Using these dimensions will give you invaluable information.

7. Google Analytics Audiences

An audience is a group of users that have something in common. That commonality could be anything: maybe you’re targeting consumers in Australia, so you have an “Australian audience,” or you want to sell to millennials, so you have a “25-34 audience.”

GA comes with several built-in audiences (including the two I just mentioned, location and age). You don’t need to do a thing to set these up — once you have the tracking code installed, GA will automatically break down your visitor data into these audience reports.

However, you can also create custom audiences. Perhaps you’re only interested in “Australian millennials”; you’d need to make a custom audience that only includes visitors who are A) in Australia and B) between the ages of 25 and 34.

Creating an audience is fairly easy. Honestly, the hardest part is figuring out what you’re trying to accomplish and then identifying the user characteristics that’ll help you do that.

Once you’ve done that, follow these instructions to create a new audience segment. From there you can import a segment to use as the basis for your Audience Report.

8. Google Analytics Segments

A segment is a subset of your data. I like to picture an entire pizza made up of all different slices — one slice has pesto and mozzarella, another has sausages and spicy peppers, another has ham and pineapple, and so on. Metaphorically speaking, each slice is a segment.

You can create segments based on:

  • Users (e.g. users who have bought something on your site before, users who have signed up for a consultation, etc.)
  • Sessions (e.g. all sessions that were generated from a specific marketing campaign, all sessions where a pricing page was viewed)
  • Hits (e.g. all hits where the purchase exceeded $85, all hits where a specific product was added to the cart)

Like audiences, GA provides you with several segments. I wouldn’t stop there: you can get incredibly granular with your segments.

To give you some inspiration, here are a few of HubSpot’s segments:

  • Users who viewed a specific product page and watched the demo video
  • Users who viewed the same product page and didn’t watch the demo video
  • Users who view a specific Academy course page
  • Users who view a specific Academy lesson page
  • Users who view a blog post and a product page

The sky is your limit — well, that, and GA’s segment cap.

Google Analytics Reports

There are five primary reports available in Google Analytics that can give you insight into your website’s performance. You’ll find these reports on the lefthand size of the screen.

All of these options can be a bit overwhelming. And depending on which version of Google Analytics you have (universal analytics or GA4), you’ll see different reports.

Let’s walk through each report together. First, we’ll start with Universal Analytics reports and then move on to GA4 reports.

Universal Analytics Reports

1. Google Analytics Real-Time Report

Google Analytics Real-Time Report

As the name suggests, the Real-Time report gives you insight into what’s happening on your site at this very moment. You can see how many visitors are on your site, which pages they’re visiting, which social platforms they’re coming from, where they’re located, and more.

While this report is fun to look at occasionally, it’s probably the least valuable. Here are some ways to use Real-Time:

  • See how much traffic you’re getting from a new social or blog post
  • Know immediately if a one-day sale or event is driving views and/or conversions
  • Make sure tracking URLs and custom events that you’ve just set up are working as they should

These are useful, but as you’ll see, the other reports pack a far greater punch.

2. Google Analytics Audience Report

Google Analytics Audience Report

The GA Audience report gives you a high-level overview for the property you’re currently looking at. Check this report once a day to get a sense of how you’re trending overall.

Underneath “Overview,” you’ll see “Audiences,” as well as expandable menus for “Demographics,” “Interests,” “Geo,” “Behavior,” “Technology,” “Mobile,” “Cross-Device,” “Custom,” and “Benchmarking.”

Explore each of these sections to get a sense of what they can tell you about your visitors.

Every section describes an audience.

Active Users

Whoever named this report belongs in the same group as the person who named guinea pigs: “active users” doesn’t refer to users currently on your site — that’s the Real-Time report — and guinea pigs are neither pigs nor from Guinea.

The Active Users report shows you the number of users who visited in the last day (1-day active users), week (7-day active users), two weeks (14-day active users), and four weeks (28-day active users.)

Google Analytics Active Users Report

What’s the value of this report, you ask?

If you have more one-day users than longer-term ones, you’re struggling with retention. People aren’t coming back to your site or app — you need to figure out why.

I’d also recommend looking at this report with various segments; for instance, perhaps you see that users in a certain age bracket have much better retention than the average.

Lifetime Value Report

First things first: do you need a refresher on Customer Lifetime Value (CLV) and how to calculate it? We’ve got you.

The Lifetime Value report gives you a sense of how valuable users are to your company. You can see lifetime value for, say, the users you generated from email marketing versus the ones you acquired from organic search. Armed with this information, you can decide which channel to invest more in.

A few notes: Lifetime Value is capped at 90 days. The Acquisition date range, however — which you can adjust — reflects all the users you acquired in that time frame.

Imagine you’re interested in looking at transactions per user for users you acquired in the week before Black Friday. You’d adjust the date range to that week specifically. Then you’d see the average transactions per user for that cohort over the following 90 days.

Because HubSpot is a SaaS company, not an ecommerce business, I look at goal completions per user, page views per user, and sessions per user by Acquisition Channel.

If my team has recently wrapped up a marketing campaign, I’ll look at the same metrics by Acquisition Campaign.

But if you are in ecommerce and want to see transaction and revenue data, you’ll need to have ecommerce tracking set up.

(By the way, here’s how to track revenue in HubSpot.)

Cohort Analysis

Some people have gone so far as to call Cohort Analysis “the single most powerful report in GA.”

So, how does it work? This report groups users by one characteristic — so far, “Acquisition Date” is the only “Cohort Type” you can use. By the way, Acquisition Date is the day a user first visited your website.

You have several options from there.

  • First, pick your cohort size: day, week, or month.
  • Next, pick your metric, or what you want to explore for this cohort. It can be further broken down into Per user, Retention, and Total.
    • Per user means the total count of that metric divided by the cohort size. So if you choose Transactions per user, for example, you’ll see the average number of transactions per user for that cohort.
    • Retention is simple: user retention, or the number of users who returned that day, week or month (determined by the cohort size you selected) divided by the total number of users in that cohort.
    • Total: the total number of sessions, transactions, etc. that occurred for that cohort size.
  • Choose your date range. GA lets you see up to three months of data.

Now let’s dive into reading the report, because it’s not obvious.

Google Analytics Cohort Analysis Report

The left-hand column shows you the Cohort Type you picked — Acquisition date, by default — broken down by Cohort Size (day, week, or month).

The first row shows you the totals for all the users in that cohort. Each row underneath that represents the activity in that day, week, or month (in this example, we’re looking at month.)

The row outlined in light blue reflects the Cohort Size you’ve chosen. Remember that data only goes back three months at the max.

The row outlined in yellow shows you the values for the metric you chose (in this case, Goal Completions per User). In the eternal words of Calvin Harris: baby, this what you came for.

Look at the first row. This tells you the average goal completions for the entire cohort in the first month after they were acquired was 1.09. Average goal completions for the entire cohort in the second month after they acquired dropped to 0.09. By the last month, it’s 0.02.

Now look at the next three rows. It looks like average goal completions per user in the first month after they were acquired increased slightly from December to January and again from January to February.

This is pretty usual behavior. Let’s imagine that instead, this report tells us average goal completions per user for February 1-28, 2019 (the last row) was 4.07. Woah! That’s nearly four times as high as December and January.

We’d definitely want to investigate further. And to do so, all we have to do is right-click on the cohort we’re interested in.

Make sure you click on the column if you want the entire day, week, or month analyzed. Click on a cell if you want to analyze only the users who, for example, completed a goal three days after they were acquired on February 27, 2019.

Google Analytics Cohort Analysis Report All Users

When you right-click, this box will pop up:

How to create a cohort segment in google analytics

Give this cohort a descriptive name. Change the views to “Any View” if you want to use this segment across your entire property (which I usually recommend), then click “Create.”

Voila — now you can compare this cohort to any other segment in any report you choose.

3. Google Analytics Acquisition Reports

Google Analytics Acquisition Reports

The Acquisition report breaks down your traffic by source: organic, direct, referral, email, social, paid search, display, affiliate, and (Other). (GA uses the (Other) category when it doesn’t know how to categorize a subset of traffic.)

From All Traffic, you can click into Channels.

Google Analytics Acquisition Reports

Click on any category to explore each source in detail.

Depending on the category, you’ll see landing pages (which URLs your visitors entered the site on), source (which website brought them to yours), or keyword (which query took them to your site.)

Google Analytics Acquisition Reports Treemaps

To see this information presented visually, click on All Traffic > Treemaps. This post walks you through how to read and adjust the Treemaps report.

The next report, Source/Medium, breaks down the general category of traffic (which you saw in “Channels”) into the search engine or domain.

It’s useful if you want to get more granular insight into the ways people are coming to your site. For example, you might notice that a whopping 70% of your referral traffic is coming from LinkedIn, while just 5% is coming from Pinterest. Depending on your marketing team’s priorities it may be time to shift focus.

The last report, Referrals, reveals the specific URLs that sent people to your site, e.g. your referral traffic.

Google Analytics Acquisition Reports for referral traffic

I like to add “Landing page” as a secondary dimension so you can see which pages on your site are receiving the referral traffic.

4. Google Analytics Behavior Reports

Google Analytics Behavior Reports

Out of all the reports in GA, I use the Behavior ones the most.

Site Content

This report gives you a review all of the blog posts, landing pages, web pages on your site.

All Pages

Let’s start with Site Content > All Pages. This shows the top-trafficked pages for your current view and/or segment. It’s useful in and of itself — you should always keep a careful eye on your most viewed URLs — but I especially like it when I’m analyzing traffic growth or declines.

To give you an idea, maybe total traffic to my website has dropped 10% month over month. I’d navigate to Site Content > All Pages and change the date range to this month compared to the last month (making sure the days of the week match up).

Google Analytics Behavior Reports: All Pages

Then I can see the differences in page views by URL:

Google Analytics Acquisition Reports for all pages

This helps me identify which pages received less traffic and contributed to that decline.

Helpful tip: I like to change the “Sort Type” from “Default” to “Absolute Change” so I see the results sorted by the greatest differences in percentage rather than total views.

Google Analytics Behavior Reports: Absolute Change

I also add Page Title as a secondary dimension so I can see the name of each page alongside its URL.

Content Drilldown

This report breaks down the structure of your site by subdomain and then subfolder. To give you an idea, for HubSpot we can see data for each of our subdomains, including:

  • blog.hubspot.com
  • developers.hubspot.com
  • community.hubspot.com

And so on. If I clicked into blog.hubspot.com, I could then see aggregated data for:

  • blog.hubspot.com/sales
  • blog.hubspot.com/marketing
  • blog.hubspot.com/service

You get the drift. This report is probably most valuable for those managing highly complex properties.

Landing Pages

Landing pages is another one of my favorite reports. GA defines a landing page as the first page in a session — in other words, the visitor’s first interaction with your website.

There are a few ways to slice and dice this report.

First, if you’re interested in the sources (organic, paid social, direct, etc.) driving users to the landing page, you can add Source/Medium as a secondary dimension.

This is basically the opposite version of the report we added earlier.

Second, if you only want to see which landing pages users visited from a specific source, on a specific platform, or within a specific category, you can add the appropriate system segment:

Google Analytics Behavior Reports: landing pages

Maybe you’re most interested in the landing pages that mobile and tablet users see — so you choose the Mobile and Tablet Traffic.

Or perhaps you’re curious about users who ended up buying something, so you choose the “Made a Purchase” segment. There are lots of possibilities here.

Exit Pages

This report shows the last pages users visited in their sessions before they left your site.

That’s a little confusing, so let’s use an example.

I want to find a place to grab dinner with my friends so I search, “Mediterranean restaurants near me.” A place that looks good pops up, so I click on it. First, I check out the menu. They have a hummus sampler — yum. Then I click on their press page. It links to a recent article on Eater, so I leave the site to read it. The reviewer loved the food. I’m sold.

The Press page would be my exit page.

You may hear that you should analyze your exit pages to understand why users are leaving your site — I think this example reveals why that strategy doesn’t always make sense. Just because someone has left doesn’t mean anything is wrong with the content.

Check this report out but take the data with a grain of salt.

Site Speed

This report is pretty self-explanatory: it tells you how quickly your site is loading for users. Obviously, the faster the better — not only do faster pages correlate with higher revenue, but Google’s algorithm takes page load time into account.

Site Speed report example

Site Speed Page Timings

This report delves into the average page load times for each URL. I use it to identify the slowest-loading pages on HubSpot’s site with the ultimate goal of figuring out why they’re taking their sweet, sweet time and how to speed them up.

Google Analytics Acquisition Reports for site speed

The default metrics are page views and average page load time, but I also recommend looking at:

  • Avg. page load time and bounce rate
    • Change the Sort Type to “Weighted” so you see the blog posts with the highest page views first
  • Avg. page load time and page value

Site Search

First things first: if users can search your website, make sure you’ve set up Site Search in GA. You must enable it for every view separately (here are the step-by-step instructions).

Usage

I typically start with the “Usage” report, which tells me how many sessions occurred with and without one-plus searches. In other words, I learn how frequently people used site search for the view and time period I specified.

Search Terms

Here’s where you learn what people are searching for. Look for themes: if you see the same search terms coming up multiple times, there are a few conclusions you could draw.

Either you need to create new content that gives users the information they’re looking for, and/or you need to better surface existing content so it’s easier to find.

Pay attention to the “% Search Exits” column, as this tells you how many users clicked away from the search results page rather than choosing a result. You can usually infer there wasn’t a good answer for their question (or it wasn’t appropriately titled.)

Google Analytics Behavior Reports: Search Terms

Search Pages

This report displays which pages users are starting searches from. It’s important to think about this contextually. Maybe people are commonly beginning searches from your 404 page — that makes sense and isn’t anything to be alarmed about.

If, on the other hand, they’re starting searches from a product landing page, something’s wrong. The content clearly isn’t living up to the expectations they had when they clicked the ad link.

Loves Data provides a solid overview of GA’s Site Search reports if you want to explore them even further.

Events

A user clicks a button. Then they download a file. Next they watch a video.

No, this isn’t the world’s most boring bedtime story — it’s an example of a GA event. Three events, to be specific.

GA defines events as, “user interactions with content that can be measured independently from a web page or a screen load.”

Those user interactions are up to you; you’ll need to add special code to your site or app that tracks the specific actions you’re interested in. Here are the instructions.

If you’re not excited about events tracking already, I want you to get excited. There are infinite possibilities here: if you have an event set up for watching a product demo, and another for clicking a link to an external review of your tool, you can measure how many times each event happened.

Maybe you discover your video isn’t getting many plays. It’s probably time to optimize the current video, make it easier to find on your site, or create a new one. Or perhaps you see that way more users than you expected are checking out the third-party review of your product.

That tells you users want more social proof and testimonials. Since the review is favorable, you might want to put it front and center on your site.

Top Events

This report tracks the events taking place most frequently — pretty straightforward. You’ll see total events (e.g. how many times that event happened) and unique events (how many sessions included one or more occurrences of that event).

If you’ve set values for your events, this report also shows you how the total value of each event and its average value (or the total value divided by the frequency.)

Pages

In this report, you can see which pages generate the most actions. I typically add “Event Category” as the secondary dimension, then filter for the event I’m most interested in.

To give you an idea, my team tracks “Blog CTA.” This event fires whenever a user clicks a CTA embedded in a blog post. To get to the report below, I added “Event Category” as the second dimension, then filtered for “Page begins with blog.hubspot.com” (so I’d only see URLs on the blog) and “Event Category equals Blog CTA.”

Now I can see which posts generate the most CTA clicks. Hopefully, you’re starting to see the power of event tracking!

Google Analytics Acquisition Reports for pages

Events Flow

The Events Flow report tracks the order in which events take place on your site. It can tell you:

A. Whether particular events tend to happen first — and if they trigger other events

To give you an idea, maybe users frequently watch your demo video, then click the CTA to schedule a call with a salesperson.

B. Whether certain event categories are more common than others

Imagine you see that videos are played far more often than PDFs are downloaded.

C. Whether users act differently based on segment

For example, perhaps people coming in via organic scroll to the bottom of your pricing page far more than people coming in via social media.

Note:This report is very subject to sampling. (Read more about GA’s data sampling practices here.) Sampled data is usually pretty accurate, but it means the more important the conclusion you’re drawing, the less uncertainty you’ll be able to tolerate.

To reduce the level of sampling, make the date range smaller.

Publisher

If you monetize your website with Google AdSense or Ad Exchange, you can use the Ad Manager and Google Analytics integration to bring information on how your ad units are performing into GA.

I won’t go into any more detail here, but I recommend reading the following resources if you want to know more:

5. Google Analytics Conversion Reports

Google Analytics Conversion Reports

If you have a website, you have an objective — probably several — for the people who visit your site.

Ecommerce store owners want their visitors to subscribe to their mailing list, make a user account, add something to their cart, and/or complete the order confirmation process.

Media companies want their visitors to stay on their site for as long as possible and/or view a certain number of pages (all the better to maximize their ad revenue.)

B2B businesses want their visitors to download an ebook, sign up for a webinar, or book a call with a sales rep.

Google Analytics makes it possible to measure all of these things — plus many more.

A goal is essentially a conversion that you’ve defined (which is why this info shows up under the Conversion section.)

There are four main types of goals:

  • Destination: This goal is completed when a user reaches a specific page, like a product page, order confirmation page, or thank you page
  • Event: This goal is completed when a predefined event fires (like the Events you can set up as, well, Events — think watching a video or sharing something to social media)
  • Duration: This goal is completed when a user’s session lasts longer than a pre-set time
  • Pages/screens per session: This goal is completed when a user views a specific number of pages (or screens for an app) per session

Once you’ve identified your goals, take a look at these instructions for creating, editing, and sharing them. This guide on choosing goal values is also quite helpful.

Overview

Head here to learn how you’re doing goal-wise across the board. I get the most from this report when I compare date ranges and/or look at goal completions by segment.

For example, quickly looking at goal completions by device reveals mobile visitors sign up for the blog newsletter much less frequently than desktop and tablet visitors. That could be because it’s hard to sign up for the newsletter on a phone — or it could be mobile users are looking for one thing and ending their session as soon as they’ve found it. I should dig in more to decide which case it is.

Goal URLs

Knowing a goal was completed isn’t helpful in and of itself; you also need to know where it happened. Suppose you’ve embedded the same form in three separate pages on your site.

It’s great that Daenerys Stark from Dragonstone, Blackwater Bay just filled out your form to get in touch with a consultant, but which page did she fill it out on?

The Goal URLs report shows you. It breaks down conversions by “Goal Completion URL” (read: where it went down.)

Google Analytics Acquisition Reports for goal urls

Reverse Goal Path

Reverse Goal Path is the unsung hero of the Conversion section. Well, I’m singing its praises now. This report allows you to see the last three pages a user visited before completing the goal.

It’s useful for goals that aren’t sequential. Maybe you have a contact form that appears in multiple places on your site, or there are two different paths that lead users into buying your ebook. Thanks to this report, you can understand the various ways people arrive at the end destination — and there’s no need to set up a funnel.

I usually filter down to a specific goal completion location or goal previous step 1, 2, or 3.

For example, since I’m interested in seeing which blog posts generated leads from content downloads, I added “Goal Previous Step – 1 containing blog.hubspot.com” to the filter.

Google Analytics Conversion Reports: Reverse goal path

Here’s what I got:

Google Analytics Acquisition Reports for reverse goal path

“(Entrance)” means the user came to the site on that step; “(not set)” means the user didn’t complete any steps prior to that one — because they weren’t on the website yet.

For a comprehensive exploration of Reverse Goal Path, take a look at OnlineMetrics’s guide.

Funnel Visualization

For sequential goals, Funnel Visualization is your go-to report.

Going back to the ecommerce example, the last goal would be “Arrived at the order confirmation page.” The goal before that, or goal #3, would be “Clicked checkout.” The goal before that, goal #2, would be “Added something to cart.” And the goal before that, goal #1, would be “Looked at product listing page.”

At each stage, you can see user drop-off. That lets you identify areas where you can improve conversion rates; for example, maybe you lose a lot of users during the checkout process. You change the flow so they can check out as a guest (versus needing to create an account), which dramatically reduces checkout abandonment.

To see this level of detail, you’ll need to map out your goals as a series. If all of your goals are simply the end objective, like “Arrived at the order confirmation page,” you won’t be able to reverse-engineer how users progress.

The Funnel Visualization report also requires you to mark the first step in the goal path as required or not. If you tell GA that yes, the first goal needs to be completed, Funnel Visualization will only show you the sessions where the user first finished goal #1. If a user skips goal #1 and goes straight to goal #2, their session won’t be represented here.

Goal Flow

If Funnel Visualization is the uptight relative who always made you take your elbows off the table and wash your hands before you ate, Goal Flow is the laid-back, fun relative who’d randomly take you out of school to go to the zoo.

All that to say: Goal Flow gives you a lot more freedom than Funnel Visualization. Unlike the latter, Goal Flow shows you all sessions that led to the completed end goal — regardless of whether the user completed the required goal #1 or not.

Another difference from Funnel Visualization: Goal Flow also shows you loopbacks — i.e. when a user goes back to a previous page or refreshes their current one.

If the user skips a step, Funnel Visualization “backfils” it. Goal Flow doesn’t.

If you edit an existing funnel or create a new one, Funnel Visualization will show you all your data from that moment onward. Goal Flow, on the other hand, can show you data from the past.

You can also toggle the Dimension and Level of detail of the report, as well as the segment, to get even more granular.

I recommend looking at various segments to see which convert at the highest and lowest rates — plus where they commonly drop out.

google analytics goal flow

Note: This report is subject to sampling. (Read more about GA’s data sampling practices here.) Sampled data is usually pretty accurate, but it means the more important the conclusion you’re drawing, the less uncertainty you’ll be able to tolerate.

To reduce the level of sampling, make the date range smaller.

Smart Goals

This report is helpful if you’re A) using Google Ads and B) not measuring conversions. Basically, Google uses machine learning to identify your “best” sessions — or those likeliest to generate conversions — and then translates those themes into Smart Goals.

Once you have Smart Goals, you can use them in Google Ads to optimize your ads performance.

Smart Goals are controversial within the marketing community because the data is minimal and businesses will be far better served by setting up their own conversion tracking. Keep that in mind if you decide to use them.

GA4 Tools

Although many of the tools, reports, and features of GA4 are similar to those from Universal Analytics listed above, there are some key differences we’ll address in this section.

1. Home

GA4 Tools: Home

The Home tab is a customizable dashboard of reports, stats, and other figures. You can adjust what you see in the Home tab so that you see a snapshot of the overall performance of your website.

In the example below, we can see users, new users, active engagement time, and total revenue because these metrics are most important for my site.

GA4 Tools: Home real-time report

2. Reports

GA4 Tools: Reports

The Reports tab shows the Reports Snapshot first which includes the same information from the Home tab in this example.

GA4 Tools: Realtime snapshot

You can also switch to other reports listed below:

  • Reports Snapshot
  • Realtime
  • Life Cycle
  • User

3. Explore

GA4 Tools: Explorations

Explorations in Google Analytics let you look into the ways your visitors navigate your site to discover new pages and content. There are several preset explorations you can activate, or you can create a new one from scratch.

4. Advertising

GA4 Tools: Advertising

When you click on the Advertising tab, you’ll start on the Advertising snapshot screen. Here, you’ll see the channels and touchpoints that drive the most conversions on your site. In order for this tab to be most helpful, you’ll need to set up goals and conversions in Google Analytics.

GA4 Tools: Advertising Snapshot

5. Configure

1663256408 288 The Ultimate Guide to Google Analytics in 2022

The Configure tab is where you’ll set up key pieces of your reports including events, conversions, audiences, and other custom definitions. As you get more familiar with your Google Analytics instance and the data you want to see, you can configure these settings to get a granular look into your data. This will allow you to solve problems, answer questions, and make decisions that are specific to your site and the outcomes you want to achieve from it.

GA4 Tools: Configure

Now You’re Ready to Track

Google Analytics is a highly valuable tool for any business as it gives you tangible data that you can apply to grow your business. Bookmark this guide and come back to it as your data tracking becomes more sophisticated.

Good luck on your Google Analytics journey.

Editor’s note: This post was originally published in August, 2017 and has been updated for comprehensiveness.

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Here’s Optimizely’s Automatic Sample Ratio Mismatch Detection

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Here's Optimizely’s Automatic Sample Ratio Mismatch Detection

Optimizely Experiment’s automatic sample ratio mismatch (SRM) detection delivers peace of mind to experimenters. It reduces a user’s exposure time to bad experiences by rapidly detecting any experiment deterioration.

This deterioration is caused by unexpected imbalances of visitors to a variation in an experiment. Most importantly, this auto SRM detection empowers product managers, marketers, engineers, and experimentation teams to confidently launch more experiments. 

How Optimizely Experiment’s stats engine and automatic sample rate mismatch detection work together

The sample ratio mismatch actslike the bouncer at the door who has a mechanical counter, checking guests’ tickets (users) and telling them which room they get to party in.

Stats engine is like the party host who is always checking the vibes (behavior) of the guests as people come into the room.

If SRM does its job right, then stats engine can confidently tell which party room is better and direct more traffic to the winning variation (the better party) sooner.

Why would I want Optimizely Experiment’s SRM detection?

It’s equally important to ensure Optimizely Experiment users know their experiment results are trustworthy and have the tools to understand what an imbalance can mean for their results and how to prevent it.

Uniquely, Optimizely Experiment goes further by combining the power of automatic visitor imbalance detection with an insightful experiment health indicator. This experiment health indicator plays double duty by letting our customers know when all is well and there is no imbalance present.

Then, when just-in-time insight is needed to protect your business decisions, Optimizely also delivers just-in-time alerts that help our customers recognize the severity of, diagnose, and recover from errors.

Why should I care about sample ratio mismatch (SRM)?

Just like a fever is a symptom of many illnesses, a SRM is a symptom of a variety of data quality issues. Ignoring a SRM without knowing the root cause may result in a bad feature appearing to be good and being shipped out to users, or vice versa. Finding an experiment with an unknown source of traffic imbalance lets you turn it off quickly and reduce the blast radius.

Then what is the connection between a “mismatch” and “sample ratio”?

When we get ready to launch an experiment, we assign a traffic split of users for Optimizely Experiment to distribute to each variation. We expect the assigned traffic split to reasonably match up with the actual traffic split in a live experiment. An experiment is exposed to an SRM imbalance when there is a statistically significant difference between the expected and the actual assigned traffic splits of visitors to an experiment’s variations.

1. A mismatch doesn’t mean an imperfect match

Remember: A bonified imbalance requires a statistically significant result of the difference in visitors. Don’t expect a picture-perfect, identical, exact match of the launch-day traffic split to your in-production traffic split. There will always be some ever-so-slight deviation.

Not every traffic disparity automatically signifies that an experiment is useless. Because Optimizely deeply values our customers’ time and energy, we developed a new statistical test that continuously monitors experiment results and detects harmful SRMs as early as possible. All while still controlling for crying wolf over false positives (AKA when we conclude there is a surprising difference between a test variation and the baseline when there is no real difference). 

2. Going under the hood of Optimizely Experiment’s SRM detection algorithm

Optimizely Experiment’s automatic SRM detection feature employs a sequential Bayesian multinomial test (say that 5 times fast!), named sequential sample ratio mismatch. Optimizely statisticians Michael Lindon and Alen Malek pioneered this method, and it is a new contribution to the field of Sequential Statistics. Optimizely Experiment’s sample ratio mismatch detection harmonizes sequential and Bayesian methodologies by continuously checking traffic counts and testing for any significant imbalance in a variation’s visitor counts. The algorithm’s construction is Bayesian inspired to account for an experiment’s optional stopping and continuation while delivering sequential guarantees of Type-I error probabilities.

3. Beware of chi-eap alternatives!

The most popular freely available SRM calculators employ the chi-square test. We highly recommend a careful review of the mechanics of chi-square testing. The main issue with the chi-squared method is that problems are discovered only after collecting all the data. This is arguably far too late and goes against why most clients want SRM detention in the first place. In our blog post “A better way to test for sample ratio mismatches (or why I don’t use a chi-squared test)”, we go deeper into chi-square mechanics and how what we built accounts for the gaps left behind by the alternatives.

Common causes of an SRM  

1. Redirects & Delays

A SRM usually results from some visitors closing out and leaving the page before the redirect finishes executing. Because we only send the decision events once they arrive on the page and Optimizely Experiment loads, we can’t count these visitors in our results page unless they return at some point and send an event to Optimizely Experiment.

A SRM can emerge in the case of anything that would cause Optimizely Experiment’s event calls to delay or not fire, such as variation code changes. It also occurs when redirect experiments shuttle visitors to a different domain. This occurrence is exacerbated by slow connection times.

2. Force-bucketing

If a user first gets bucketed in the experiment and then that decision is used to force-bucket them in a subsequent experiment, then the results of that subsequent experiment will become imbalanced.

Here’s an example:

Variation A provides a wildly different user experience than Variation B.

Visitors bucketed into Variation A have a great experience, and many of them continue to log in and land into the subsequent experiment where they’re force-bucketed into Variation A.

But, visitors who were bucketed into Variation B aren’t having a good experience. Only a few users log in and land into a subsequent experiment where they will be force-bucketed into Variation B.

Well, now you have many more visitors in Variation A than in Variation B.

3. Site has its own redirects

Some sites have their own redirects (for example, 301s) that, combined with our redirects, can result in a visitor landing on a page without the snippet. This causes pending decision events to get locked in localStorage and Optimizely Experiment never receives or counts them.

4. Hold/send events API calls are housed outside of the snippet

Some users include hold/send events in project JS. However, others include it in other scripts on the page, such as in vendor bundles or analytics tracking scripts. This represents another script that must be properly loaded for the decisions to fire appropriately. Implementation or loading rates may differ across variations, particularly in the case of redirects.

Interested?  

If you’re already an Optimizely Experiment customer and you’d like to learn more about how automatic SRM detection benefits your A/B tests, check out our knowledge base documentation:

For further details you can always reach out to your customer success manager but do take a moment to review our documentation first!

If you’re not a customer, get started with us here! 

And if you’d like to dig deeper into the engine that powers Optimizely experimentation, you can check out our page faster decisions you can trust for digital experimentation. 

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How to Use Email Marketing Automation to Encourage SaaS Adoption

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How to Use Email Marketing Automation to Encourage SaaS Adoption

SaaS adoption refers to the process that earns your product a permanent place in your user’s workflow. This happens when you empower your audience to extract useful value from your solutions.

Email, a tried and tested communication tool, plays an essential role in helping brands relay their product’s value to their customers and educate them on how to make the most of it.

However, smaller teams might find themselves at a crossroads, balancing the need for personalized communication with the scale of their user base

Email marketing automation offers a practical solution by ensuring that each message is tailored and timely, yet sent out with minimal manual effort.

In this article, let’s look at five tips that will help you build robust email marketing automation that will motivate your audience to adopt your tool and make it a part of their daily lives.

1. Segment your audience

Audience segmentation is crucial for personalizing your emails, which in turn, can significantly boost SaaS product adoption. Remember, a message that resonates with one segment might not strike a chord with another.

The key to effective segmentation is understanding where each customer is in their journey. Are they new subscribers, active users, or perhaps at the brink of churning?

Here are some actionable steps to segment your audience effectively:

  1.  Analyze User Behavior: Look at how different users interact with your SaaS product. Are they frequent users, or do they log in sporadically? This insight can help you create segments like ‘active users’, ‘occasional users’, and ‘at-risk users’.
  2.  Utilize Sign-up Data: Leverage the information gathered during the sign-up process. This can include job roles, company size, or industry, which are excellent parameters for segmentation.
  3.  Monitor Engagement Levels: Keep an eye on how different segments interact with your emails. Are they opening, clicking, or ignoring your messages? This feedback will help you refine your segments and tailor your approach. Plus, consider setting up small business phone systems to enhance communication with your audience.

2. Create campaigns based on behavior

Sending behavior-based campaigns is pivotal in effective email marketing. By focusing on performance metrics such as open rates, click-through rates, and engagement times, you can gauge the effectiveness of your emails and adjust your strategy accordingly.

You can also use digital signage to entertain or make customers aware of something new – product or service, through a digital sign.

Different types of email campaigns serve various purposes:

  1. Educational Campaigns: These are designed to inform and enlighten your audience about their problem. They can include tips, best practices, and how-to guides. The goal here is to provide value and establish your brand as a thought leader in your industry.
  2. Interactive Campaigns: These campaigns encourage user engagement through surveys, quizzes, microblogging platforms, or feedback forms. They not only provide valuable insights into user preferences but also make the recipients feel heard and valued.
  3. Onboarding Campaigns: Targeted toward new users, these messages help them get the value they seek from your product as soon as possible. They can include step-by-step tutorials, video guides, or links to helpful resources.

4.Re-engagement Campaigns: Aimed at inactive users, these emails strive to reignite their interest in your SaaS product. They might include product updates, special offers, or reminders of the benefits they’re missing out on.

3. A/B test before deployment

Rather than pushing a new campaign to your entire audience as soon as you draft the emails, A/B testing helps you know whether your messages are any good.

Here are some best practices for A/B testing in email automation:

  1. Test One Variable at a Time: Whether it’s the subject line, email content, or call-to-action, change just one (or a couple) element per test. This clarity helps in pinpointing exactly what works and what doesn’t.
  2. Choose a Representative Sample: Ensure that the test group is a good mix of your target audience as a whole. This way, the results are more likely to reflect how your entire audience would react.
  3. Measure the Right Metrics: Depending on what you’re testing, focus on relevant metrics like open rates, click-through rates, or conversion rates. This will give you a clear picture of the impact of your changes. Along with these steps, it’s important to use an SPF checker to ensure your emails aren’t marked as spam and increase the deliverability rate.
  4. Use the Results to Inform Your Strategy: Once you have the results, don’t just stop at implementing the winning version. Analyze why it performed better and use these insights to inform your future campaigns.
  5. Don’t Rush the Process: Give your test enough time to gather significant data. Adopt comprehensive marketing reporting solutions that give you a clear picture of your campaigns’ efficacy.

4. Leverage email templates

When managing multiple email automation campaigns, each with potentially dozens of emails, the task of creating each one from scratch can be daunting. Not to mention, if you have multiple writers on board, there’s a risk of inconsistency in tone, style, and branding.

Email templates are your secret weapon for maintaining consistency and saving time. They provide a standardized framework that can be easily customized for different campaigns and purposes.

They are also a great way to communicate with your customers. Another way to communicate efficiently with your customer is through best small business phone systems, which is especially efficient when conveying information about your product or service.

Here’s a rundown of various types of templates you should consider having:

  1. Welcome: For greeting new subscribers or users. It should be warm, inviting, and informative, setting the tone for future communications.
  2. Educational Content: Used for sharing tips, guides, and resources. If you are making this template to introduce online GCSE physics tutor services that you provide, you should be clear, concise, and focused on delivering value in your template.
  3. Promotional: For announcing new features, offers, or services. It should be eye-catching and persuasive without being overly salesy.
  4. Feedback Request: Designed to solicit user feedback. This template should be engaging and make it easy for recipients to respond.
  5. Re-engagement: Aimed at rekindling interest among inactive users. It should be attention-grabbing and remind them of what they’re missing.
  6. Event Invitation: For webinars, workshops, or other events. This should be exciting and informative, providing all the necessary details.

5. Use a tool that works for you

Email is more than just a marketing platform; it’s a multifaceted tool that can drive customer engagement, support, and retention. Given its versatility, it’s crucial to choose the right email automation tool that aligns with your specific needs.

When selecting an email automation tool, consider these key features:

  1. Intuitive Interface: Even your non-technical team members should find it easy to use.
  2. Robust Segmentation Capabilities: The tool must offer advanced segmentation options to target your emails accurately.
  3. A/B Testing Functionality: Essential for optimizing your email campaigns.
  4. Integration with Other Tools: Look for a tool that integrates seamlessly with your CRM, analytics, and other marketing platforms. Additionally, integrating a multilingual translation support can further enhance the tool’s versatility, allowing you to reach a diverse audience with tailored content in their preferred languages.

Popular tools like Mailchimp and ActiveCampaign offer free trials which are great for brands to take these for a spin before making a choice.

Wrapping up

Leveraging email automation makes it easier for SaaS brands to market their solutions to their audience and ultimately increase adoption rates.

Segmenting audiences, creating messages based on their behavior, testing emails before setting campaigns live, utilizing templates for speed and consistency, and adopting a tool that you are comfortable working with are essential email marketing automation tips to help you get started on the right foot.

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Marketing Team Reorgs: Why So Many and How To Survive

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Marketing Team Reorgs: Why So Many and How To Survive

How long has it been since your marketing team got restructured? 

Wearing our magic mind-reading hat, we’d guess it was within the last two years. 

Impressed by the guess? Don’t be.  

Research from Marketing Week’s 2024 Career and Salary Survey finds that almost half of marketing teams restructured in the last 12 months. (And the other half probably did it the previous year.) 

Why do marketing teams restructure so often? Is this a new thing? Is it just something that comes with marketing? What does it all mean for now and the future? 

CMI chief strategy advisor Robert Rose offers his take in this video and the summary below. 

Marketing means frequent change 

Marketing Week’s 2024 Career and Salary Survey finds 46.5% of marketing teams restructured in the last year — a 5-percentage point increase over 2023 when 41.4% of teams changed their structure. 

But that’s markedly less than the 56.5% of marketing teams that restructured in 2022, which most likely reflected the impact of remote work, the fallout of the pandemic, and other digital marketing trends. 

Maybe the real story isn’t, “Holy smokes, 46% of businesses restructured their marketing last year.” The real story may be, “Holy smokes, only 46% of businesses restructured their marketing.” 

Put simply, marketing teams are now in the business of changing frequently. 

It raises two questions.  

First, why does marketing experience this change? You don’t see this happening in other parts of the business. Accounting teams rarely get restructured (usually only if something dramatic happens in the organization). The same goes for legal or operations. Does marketing change too frequently? Or do other functions in business not change enough? 

Second, you may ask, “Wait a minute, we haven’t reorganized our marketing teams in some time. Are we behind? Are we missing out? What are they organizing into? Or you may fall at the other end of the spectrum and ask, “Are we changing too fast? Do companies that don’t change so often do better? 

OK, that’s more than one question, but the second question boils down to this: Should you restructure your marketing organization? 

Reorganizing marketing 

Centralization emerged as the theme coming out of the pandemic. Gartner reports (registration required) a distinct move to a fully centralized model for marketing over the last few years: “(R)esponsibilities across the marketing organization have shifted. Marketing’s sole responsibilities for marketing operations, marketing strategy, and marketing-led innovation have increased.”  

According to a Gartner study, marketing assuming sole responsibility for marketing operations, marketing innovation, brand management, and digital rose by double-digit percentage points in 2022 compared to the previous year.  

What does all that mean for today in plainer language? 

Because teams are siloed, it’s increasingly tougher to create a collaborative environment. And marketing and content creation processes are complex (there are lots of people doing more small parts to creative, content, channel management, and measurement). So it’s a lot harder these days to get stuff done if you’re not working as one big, joined-up team. 

Honestly, it comes down to this question: How do you better communicate and coordinate your content? That’s innovation in modern marketing — an idea and content factory operating in a coordinated, consistent, and collaborative way. 

Let me give you an example. All 25 companies we worked with last year experienced restructuring fatigue. They were not eager creative, operations, analytics, media, and digital tech teams champing at the bit for more new roles, responsibilities, and operational changes. They were still trying to settle into the last restructuring.  

What worked was fine-tuning a mostly centralized model into a fully centralized operational model. It wasn’t a full restructuring, just a nudge to keep going. 

In most of those situations, the Gartner data rang true. Marketing has shifted to get a tighter and closer set of disparate teams working together to collaborate, produce, and measure more efficiently and effectively.  

As Gartner said in true Gartner-speak fashion: “Marginal losses of sole responsibility (in favor of shared and collaborative) were also reported across capabilities essential for digitally oriented growth, including digital media, digital commerce, and CX.” 

Companies gave up the idea of marketing owning one part of the customer experience, content type, or channel. Instead, they moved into more collaborative sharing of the customer experience, content type, or channel.  

Rethinking the marketing reorg 

This evolution can be productive. 

Almost 10 years ago, Carla Johnson and I wrote about this in our book Experiences: The 7th Era of Marketing. We talked about the idea of building to change: 

“Tomorrow’s marketing and communications teams succeed by learning to adapt — and by deploying systems of engagement that facilitate adaptation. By constantly building to change, the marketing department builds to succeed.” 

We surmised the marketing team of the future wouldn’t be asking what it was changing into but why it was changing. Marketing today is at the tipping point of that. 

The fact that half of all marketing teams restructure and change every two years might not be a reaction to shifting markets. It may just be how you should think of marketingas something fluid that you build and change into whatever it needs to be tomorrow, not something you must tear down and restructure every few years.  

The strength in that view comes not in knowing you need to change or what you will change into. The strength comes from the ability and capacity to do whatever marketing should. 

HANDPICKED RELATED CONTENT:  

Want more content marketing tips, insights, and examples? Subscribe to workday or weekly emails from CMI.

Cover image by Joseph Kalinowski/Content Marketing Institute 

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