If you’re in marketing or sales, you have probably managed a lot of email marketing campaigns. And you’ve also likely viewed your Google Analytics data many times.
But do you know how to make the most of the data from your campaigns and Google Analytics? Do you know how to compare data from your email campaigns with Google Analytics?
Google Analytics can give you data that you won’t see in your email marketing campaign managers (MailChimp, Campaign Monitor, etc.).
With that said, your email marketing campaign manager does have a lot of great data. Most give you the following information on each individual campaign:
- Sent emails
- Successful deliveries
- Undelivered emails
- Total opens
- Open rate
- Last opened
- Forwarded emails
- Clicks per unique opens
- Total clicks
- Last clicked
- Abuse reports
In the following MailChimp screenshot, you can see many of these essential email marketing metrics.
However, few email marketing campaign managers report what really matters: how much money have I made from my last email?
In order to understand how your email campaigns fit into your overall business strategy, and how much revenue they are generating, there are several questions you need to ask yourself.
- How many users made a purchase in your online store through an email campaign?
- What percentage of your entire site income is email generating?
- Do you understand how your visitors become customers? Is your email campaign integrated into that journey?
Google Analytics can help you answer all of these questions. Follow these five steps to help you figure out how much money your email campaigns are generating.
1) Tag your email campaigns in Google Analytics
If you don’t tag your email campaigns properly, you won’t get anywhere. To tag your campaigns, either your email marketing tool needs to be connected/integrated with Google Analytics (via a plugin or an add on), or you need to tag all the URLs of your emails by hand.
(If you don’t know what UTM tags are, please visit this complete guide to UTM tracking for Google Analytics.)
Integrating Google Analytics
Let’s talk about the first option: integrating Google Analytics and your email marketing manager. We’ll use MailChimp as an example. MailChimp makes it very easy to connect campaigns with Google Analytics. If you use MailChimp, check out these premium responsive email templates.
To integrate Google Analytics and MailChimp, you must first sync your MailChimp and Google Analytics accounts. The Integrations section in MailChimp is in Account > Integrations. Find “Google: Analytics, Contacts and Docs” and then click Connect. Be patient. It may take 24-48 hours for data to come in.
After that, set up analytics tracking by filling in the field “Google Analytics link tracking” in the campaign’s Setup screen.
Note: If you use this method, don’t forget to activate Google Analytics tracking and define the Campaign name for every single campaign.
Tagging URLs by hand
If you prefer to have more control over the entire process, the best way is to create the URLs manually. You can create the URLs using this form, but we strongly recommend the McGaw.io UTM tool (a really useful Chrome addon).
In the URL structure, you should include:
- Website URL (in our example, “targetdomain.com”)
- Campaign Source (in our example, “yourdomain-newsletter”)
- Campaign Medium (in our example, “email”)
- Campaign Name (in our example, “blackfriday”)
There are also two optional fields:
- Campaign Term
- Campaign Content
After adding that information, the URL would look something like this:
If you use UTMs, every click on your email will be registered in Google Analytics under Acquisition > Campaigns > All Campaigns report. All clicks on our example email will show as “blackfriday” campaign, as you can see in the screenshot below. It is useful to include the date in the campaign name, so you can easily identify different campaigns.
One more tip: You don’t have to spend hours and hours designing your email campaign. Check out these great sales email templates to save yourself time and money.
2) Define your goals
After you have a system in place for tagging your URLs, you must define the goal that you want to measure. Do you want to know when your customers visit a URL? Complete a transaction? Request information? Fill in a form?
Though every business is different, we are going to work through an example involving an online shop. The goals of this shop are to
- And sell.
In this case, we will focus on transactions, the point at which we bill customers and make money.
Now that we have covered tagging our campaigns and defining our goals, we need to understand the data behind various email campaigns.
3) Understand the data behind your email campaigns
Let’s continue looking at our example of an online shop. The goal is to sell items, so when comparing campaigns, one of the most important things to know is, which campaign sold more?
As you can see on the right side of the screenshot, the first campaign resulted in the most transactions. You can alternatively sort the campaigns by transactions instead of sessions (which is the default). Studying this report, you might wonder about another interesting question: what is the relationship between sessions and transactions? In other words, what campaign best converts your visitors into customers?
You can answer this by monitoring the “E-commerce Conversion Rate” metric in the same report. As a result of the second campaign, more than 2 visitors out of every 100 made a purchase.
Best sellers from emails
All this data is extremely useful, but still very basic. We can also look at what products are best sold from emails.
Best sellers can be found in this report: Conversions > E-commerce > Product performance.
Filtered data from email campaigns
Now that we have this information, it is important to know how to filter data from email marketing actions or even from each email campaign. If you have tagged your campaigns properly, all this information is at your fingertips.
The previous report gave us data on all orders, but what if we want to filter for email sales? Filtering correctly requires a consistent tagging structure that can help us to dig into the data.
We recommend tagging using this structure:
- URL for email number 1: targetdomain.com/?utm_source=yourdomain-newsletter&utm_medium=email&utm_campaign=blackfriday
- URL for email number 2: targetdomain.com/?utm_source=yourdomain-newsletter&utm_medium=email&utm_campaign=pre-christmas
- URL for email number 3: targetdomain.com/?utm_source=yourdomain-newsletter&utm_medium=email&utm_campaign=pre-blackfriday
The utm_medium is always “email”, so you can isolate all the email orders by clicking the secondary dimension: Medium.
In this screenshot, you can see it is possible to select “Medium” as a secondary dimension, which is where you will find all the different mediums your traffic is coming from. By filtering this way, you can select all “email” data.
Why would we want to know this information? Because some of your products will sell better using email than others.
You can discover which of your products sells best over email, but impulsive products or services such as restaurant reservations and hotel bookings are classic email purchases. These purchases are usually boosted with an accelerator, like the classic, “There are just X units left. Order yours before they’re gone!”
4) Data attribution
Besides all the direct sales data that we have just seen (the consumer sees the email, clicks, and buys), we are also interested in data attribution.
What is data attribution? Data attribution is how we know which channel has helped us to convert a visitor into a customer. Is this email the first contact with the customer? The latest? Somewhere in the middle?
You can find this information by looking at conversion reports. Let’s look at one kind of conversion report: Conversions > Multi-channel Funnels > Top Conversion Paths.
This conversion report looks like this:
This report shows the different paths to a sale that customers are following. In other words, this shows how all the channels are contributing to the final sales.
In the screenshot above, email doesn’t appear, so it seems that email is not leading to sales. Filtering, however, will allow us to see more detail. We are interested in the following question: What is the behavior of users who are targeted by emails and make a purchase as a result?
Let’s filter by email and see:
These are the different ways email is helping to sell stuff.
As you can see, there are users who see an email twice, then visit the site to purchase. But the combinations of Email + Direct traffic and Email + Organic traffic are even more interesting.
This data shows that many sales come from people who see our emails and then enter the site directly to make a purchase. The data also shows that many people receive the email and then look for our site in a search engine (organic traffic) and buy. Actually, most of our sales are coming in these two ways.
This means we should boost these combinations of channels.
Now that you have so much data, it can be interesting to look at the devices your customers are using to access your content. That data can then be combined with data about different channels (like email) and see how everything works together to lead to conversions.
You can view a lot of this data using the Device Overlap report. To view this report you must activate the tracking by User-ID. This way you can see reports such as:
Device overlay (devices used by the same user).
Device routes (the routes that a user follows, for example: Mobile > Desktop > Tablet).
Device acquisition (the first device that a visitor used to discover your site).
The information in these reports will help you determine what devices your users are using to see your emails.
To view this data, go to the Device Overlap report, which looks like this:
Remember, this report is only available for accounts that have activated the tracking by User-ID.
The next step is to apply a filter for source equal to “email”. This way, you can see the devices superimposed on one another and detect which devices your users are using, especially those users who buy your products.
This information might help you start answering the following questions:
Is it worth doing responsive emails?
Is it worth doing a mobile first version of your emails?
Is it worth doing a mobile first version of your site?
Do users behave differently when they open emails on different devices?
Is it worth sending different emails to users who always open them on mobile devices?
Is it worth sending (yet another) different email for users who always open emails on a desktop or tablet?
As you can see, if email marketing is important to your business, measuring your campaigns and learning how users behave is crucial. Studying this data will allow you to get the most out of this channel by learning where to focus to improve your results, and how to plan your email marketing strategy.
What do you think of these advanced Google Analytics metrics? Are you already using some of them in your email marketing campaigns? Are you integrating Google Analytics with your email marketing tool?