A Comprehensive List of Email Analytics Metrics

In this article, I will describe 13 important email analytics metrics, what they mean, and how email campaign performance can be improved.

In this article, I will describe different metrics related to evaluating performance of email campaigns and how they can be improved.

Here is a typical funnel of an email campaign – from mailing list to conversion:

Subscription ⇒ Email Send ⇒ Email Delivery ⇒ Email Open ⇒ Email Click Through ⇒ Landing Page Visit ⇒ Conversion Funnel from Email ⇒ Conversion from Email

Let us review a set of email metrics connected with each step of this funnel.

  1. Subscription conversion = subscriptions / visits of the sign-up page. In order to get more people to subscribe, your sign-up form should have a clear call-to-action and ask only for necessary information. Short info on data protection will establish trust and offers of incentives (e.g. a free sample, a whitepaper download, etc.) will motivate more users to share their email address.
  2. Subscriber list growth rate = (new subscribers-old subscribers) / old subscribers. In order to constantly increase your subscriber base, you should both receive sufficient amounts of traffic to your subscription page and have a high subscription conversion rate.
  3. Number of emails sent. This is the starting quantifying point of email campaign funnel analysis. The only way to improve this metric is to increase the size of the mailing list. However, to ensure that the email addresses are valid and to comply with double opt-in procedure, avoid buying email lists.
  4. Delivery rate = number of emails delivered / number of emails sent. Delivery of the emails you send depends on several factors: white- or blacklisting of your IP by email service providers, existing or non-existing (hard bounce) email addresses, how full the recipient’s mailbox is (soft bounce), if a user has moved previous emails to spam, etc. In order to increase the delivery rate, make sure to revise your mailing list often and to remove obsolete or false addresses. In addition, you should always include a clearly visible Unsubscribe link and avoid using HTML-only emails with images.
  5. Open rate = number of emails opened / number of emails sent. This metric is greatly influenced by the subject and the timing/frequency of the emails sent. In order for the email to appear relevant for the user, you can apply segmentation and some degree of personalization to your email campaigns. When comprising the subject of the email, be precise and avoid words and expressions which can cause your email be filtered as spam.
  6. Unsubscribe rate = number of unsubscribe requests / emails sent. Clearly, it is best to keep unsubscribe rate as low as possible. In order to do this, make sure to deliver the message relevant to the recipient. In addition, high mailing frequency (e.g. once per day) will likely cause most users to unsubscribe (see my next post). Ideally, you should let the subscriber choose the mailing frequency optimal for them.
  7. Click-through rate = clicks on the links with the email / emails sent. In order to measure how many times a link in the email was clicked you can apply a campaign ID to the  URL, e.g. https://marketing-to-convert?cid=email&campaign=spring-break&link-id=001. In order to increase the click-through rate, the email CTA should be clearly visible and correspond to the email subject. You may also want to place links in the body of the email and  behind corresponding images. As has been stated above, the offer should be delivered at the right time and to the right user. Thus, factors such as user past activity and interests will play a role.
  8. Unique open and click-through rate. These metrics are basically the same as above however, only one open and click-through is counted per visitor (even if they interacted with the email multiple times).
  9. Landing page visits. Normally, this number will be equal to click-throughs. In case it is not, do review link tagging and check if there are any broken links.
  10. Cost per visit = total cost of a mailing campaign / number of visits to the landing page. Using this metric, you can compare the effectiveness of different campaigns. (The cost of a campaign is the cost you incur for sending an email multiplied by the number of emails sent.) Improving cost per visit can be achieved by generating more visits from your mailing i.e. by offering relevant content and compelling CTA’s.
  11. Landing page bounce rate = bounces / visits to the landing page. In order to decrease bounces on the landing page, make sure that landing page reflects the information in the link user clicks on. E.g. if you are making an email campaign about a particular product, do not send users to Products Overview page.
  12. Pages per visit from email. This metric demonstrates if users found your site engaging enough to move on from the landing page. However, a large number of pages viewed may signify that your site is difficult to navigate. Make it clear to the user where to go next from the landing page by integrating links or offering a small navigation menu.
  13. Conversion from email = number of conversions / emails sent. Bear in mind that conversion is not always a sale. Contact form submission, leaving a review or recommending your product to a friend can be counted as conversion actions. In any case, conversion rate will be the most important measure of your email campaign success. Optimizing conversion rate involves all stages of the funnel: from segmenting the mailing list to streamlining the user journey from the landing page.

This list is by no means an exhausting one but contains some important metrics that can be used to track the performance of email marketing campaigns.

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Through the Looking Glass: Website Reporting Approaches

In this article I will describe some common approaches to reporting and analytics.

If you already have analytics system in place, it is time to think about reporting on your website. In this article I will describe some common approaches to reporting and analytics.

Ad-Hoc Reporting: This type of reports is done only once, usually to answer a research question or after a major update. For example, if you want to find out where the site visitors geographically come from but suspect this will not change significantly month on month. Regular reporting  can be done monthly, weekly or daily to track performance over time.

Reporting by segment summarizes information using the segments you have (see my post on segmentation). Detailed reporting examines the data at page level (this way you can find out how different pages perform and what key user journeys are). Holistic reporting  contains website data in general (for example, you want to display KPIs of the whole site). Most likely you will combine different levels of data granularity in your report.

The reports you produce will differ depending on the stakeholder group you create them for. For example, reports for senior management will demonstrate trends and bird’s eye view of data. Reports for Marketing department may contain numbers on campaign performance, click-through rates and page/product rankings.

Analytics reports can also produce different types of analysis from descriptive to casual. At the same time, the goal of reporting may include increasing conversion, analyzing user journey, leading users to certain pages, increasing time on site, decreasing bounce rates, optimizing campaign performance, understanding SEO or referral traffic, etc.

You will most likely need to decide how to benchmark or evaluate the data. One way is to compare the data to the previous reporting period (e.g. June data to May data). This, however, does not take into account seasonal or weekday fluctuations, which may have a significant impact on performance. Another way is to compare the reporting period to the same period in the past, e.g. June 2017 to June 2016. This will provision for fluctuations, however, will not consider how different the conditions could have been in the past or what overall traffic growth was realized. A third way to approach benchmarking is compare your performance to that of your industry segment or your main competitor (this data will seldom be available). And finally, if a business goal is set, the KPIs will be evaluated against this goal.

I hope this article has given you some ideas what can be included into your reporting. Apart from setting the framework, it is important to provide human and technical resources for successful reporting.

Picture source: Unsplash

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Divide and Rule: Types of Segmentation in Web Anaytics

In order to analyze traffic to your website you need to segment it. A lot of times, possibilities for segmentation will depend on the analytics system you use and the data you track. In this article, I will outline how you can create segments using the data commonly provided by analytics software. I will also evaluate, to what extent you can make use of the created segments.

First group of segments: user-based

Geo and tech characteristics of users belong to this group. If you have a way to find out the gender or the age of your users (registration, dialog windows), you can segment visitors even more granularly.

user-based-segments
User-based segments: by device type.

Geo-segmentation includes segmenting by cities, regions, countries as well as browser languages.

Geo-data is usually collected based on user IP. Although this information seems to be interesting (often presented in form of maps), it is only relevant if you offer a location-based product/service (e.g. only available in a few cities) or if your website is localized for several countries and you want to make sure that international users are routed to the correct country site.

Tech segments are based, for example, on device type or browser name and version, as well on as the user domain. From my experience, these parameters are not going to be used extremely often either. If you get a significant number of visitors from a certain device or a certain browser,  you should better make sure that your site works on them. Other than that, it useful to group sessions where an HTTP or a JavaScript error occurred (if an error is recurring and you can reproduce it, a fix is needed).  Additionally, IP address or user domain name are used to exclude internal traffic (i.e. the traffic from your company).

Second group of segments: content-based

As can be guessed from the name, such segments relate to the content of the site. In other words, by applying this segmentation, you can see what happens on certain parts of your website or on groups of pages. If set correctly, this gives a structured and precise insight into the website performance.

Some ways to apply this segmentation are:

  • By product (if you have a few products, e.g. 1-5)
  • By brand or by product type (if your website showcases a lot of products)
  • By department/division (if this is how your site is organized)
  • By content for different customer groups (gender, age, type of customer: business or private…)
  • By site component (forms, product description, FAQ…)
  • By country site (if the data is not split in the database)

This is by no means an exhaustive list. By looking at your site, you may come up with a more suitable (for you) way to segment content. Then you can use the grouping, for instance, to compare how different products perform or to create a product ranking by popularity/engagement.

Third group of segments: traffic-based

This type of segments will contain general break-up of traffic, without precise description of particular users. The most common segments will include: by traffic source, by referring domain, by time of visit (day of the week, hour).

New vs repeat/returning visitors segment is based on the cookie set on a visitor’s device (however, as a growing number of visitors do not accept cookies or delete them regularly, the data from this segmentation should be treated as an approximation).

Such segments are extremely useful in order to analyze how the performance changes depending on the type traffic flowing to the site. What is more,  traffic type will most probably have a larger impact on performance rather than user-based characteristics,

Fourth group of segments: interaction based

This is a large group of segment that describes how visitors interact with the site.

You can create segments by number of page views per visit (page depth), by bounce rate, by time on site, etc. For the majority of these parameters, you will need to come up data ranges, e.g. number of page views per visit may be split into the following segments: 1 view, 2-3 views, 4-6 views, 7 or more views. Besides, you can include events – such as forms submission,

This type of segmentation will enable you to start the analysis “from the other end”, e.g. what are the traffic sources or pages in best performing segments.

As you can see, applying correct segmentation to site traffic will provide you not only with more data but also with more insights into what influences the amount of traffic and its performance over time. But what if you feel that besides segmenting you need to group and generalize traffic somehow? This would be a clear case for cohort analysis, which I may talk about in one of the next posts.

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360 Degrees: Types of Data Analysis

According to J. Leek, there are six data analysis questions we can ask. Let us look at these questions in detail. As an example, I will use a website of a marketing agency. The conversion goal of this website is the contact form that users fill out and send. We want to ask questions about the conversion.

Descriptive analysis: What is..?

Here the goal is to describe a set of data, without inferring anything from it or making a prediction. It can be performed by itself or be a starting point of a more in-depth analysis. An example in case of an agency would be the number of forms sent or the conversion rate (e.g. the number of submissions divided by the number of page visits).

Explorative analysis: Where is..?

This analysis looks at the data more deeply, discovering the connections between different variables. However, this cannot be used for prediction or does not necessarily imply causation. In the example above, exploratory analysis can be used to look for connection between form submissions and day of the week or where the form was placed on a page.

Inferential analysis: Who is..?

In this type, we infer about a larger group of users from a small group. E.g. we can set up a user survey or conduct focus group research with some users to find out how they interact with the site and the contact form in particular.

Predictive analysis: What will be..?

This analysis makes prediction about future occurrences based on some known variables. For example, we could predict the fluctuation in the number of form submissions according to the day of the week. Prediction should not be confused with causality, e.g. if there is a peak of form submissions on Monday, it does not mean that Monday causes form submissions.

Causal analysis: Why is…?

This is used to define one-to-one relationships between different variables. Causality will almost always look at the average cases, so some outliers or different behaviors should not be excluded. As an example, placing the form at the top of the page will usually increase the conversion, as opposed to placing it below-the-fold. Here, a better form placement actually causes more visibility and increases the chance of a submission.

Mechanistic analysis: How is..?

This a rare and difficult type of analysis that allows for understanding how exactly variables influence each other in individual objects. This would often bring the analysis of data down to the set of equations.

All in all,  you will need to perform different types of analysis of the raw data to get a clear picture and come up with a set of recommendations.

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Data at Your Fingertips: How to Design a Dashboard

Data dashboards are a way to represent large amounts of data in a condensed and visual manner. A dashboard typically consists of widgets or reportlets. Each of widgets includes one or several KPIs, supplemented by graphs, small data tables, etc.

If you are supposed to prepare a concept for a dashboard, the first task is to find out the stakeholders’ requirements. Who are you building the dashboard for? What data do they need to see, what insights do they want to gain and how detailed should the information be? E.g.  the dashboard for the marketing department will substantially differ from the dashboard you are building for the CEO of the company.

Secondly, prepare a rough draft of the dashboard layout and the widgets it will have. Try not to include more than 10-12 widgets, otherwise your dashboard may become too cumbersome to view and understand. This is also where you define what KPIs the dashboard will show.

Rough draft of a dashboard.
Rough draft of a dashboard.

Thirdly, decide what time period will be included. The majority of dashboards will show only today’s or this week’s data. However, if the website has significant monthly or daily fluctuations in performance, you may want to include a more extended period of time. If stakeholders want to use the dashboard for performance monitoring, the data naturally has to be real-time or with a minimum time lag.

In the fourth place, consider how to represent the data you will gather. A widget may contain a number, a graph, a data table, or a combination of these. The graphical representations of numbers is the most visual way that allows to grasp the meaning of data within seconds. Choose the type of graphics wisely: a line graph will show the development of KPIs in time, the pie chart will show what percentage each segment or product contributed to the total and a bar chart is a good way to visualize and compare several dimensions. The picture below shows how different data may be visualized. In addition, you can use scattergrams, process visualizations, bubble diagrams, etc. In any case, make sure that your graphs are not cluttered and convey a meaningful story.

graphics on a dashboard
Examples of graphics.

numberIf you choose to use a data table in your widget, select only top 5 entries and do not include more than three  columns. In addition, do not be afraid to put a single number in your widget, in case this number is important.

The next decision is what technology you will use. The majority of modern analytics systems include a dashboard feature. If the feature is not sufficient for your requirements or is missing altogether, you may consider using specialized data visualization software (in fact, even Excel offers dashboard building). The best solutions will be those that allow for automatic export and processing of data, without much manual work. Ideally, you should be able to pull data from several sources – e.g. from the website itself, from the order processing system and from social media.

Also think how you will share the dashboard with stakeholders and if the system provides you with this option. Some will prefer viewing the real-time data, others will be satisfied with a weekly/monthly report in PDF format.

And finally, create a visual mock-up of your dashboard for the stakeholders before starting to implement your concept.

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