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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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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10 Best User Features of Adobe Analytics

Adobe Analytics (former: Omniture) is a powerful Web analytics tool that provides a user with information of different levels of complexity and for different purposes and contains several useful features.

Adobe Analytics (former: Omniture) is a powerful Web analytics tool that provides a user with information of different levels of complexity and for different purposes. After having been working with this solution for three months, I can point out its ten best user features.

Feature one: flexibility in adding metrics. All reports already contain data, but Adobe Analytics allows you to select additional metrics for most reports. Those include: bounce rate, visits, visitors, exits, entries, etc. In addition, you can create calculated metrics from existing ones, using a variety of mathematical functions.

Feature two: segment creation. Segments basically filter data by different parameters, e. visit number or device type. To create a segment, select the parameter and one of the following: value equals/does not equal, value exists/does not exist, value is larger than…, smaller than..., then enter the value. You can segment your audience on hit, visit, or visitor basis. Examples of segments: all page URLs that contain “product”, all visitors with more than one visit, all visits from mobile devices, etc. You can also compare segments— display the metrical data from two segments side by side and see the difference in percentage. E.g. you can compare users of smartphones to users of tablets.

Adobe Analytics Adjust Chart Type
Visualization in Reports

Feature three: visual representation. Every report includes a graph that can be either hidden or adjusted: the chart type can be changed, metrics can be selected or deselected or even split to be displayed on two different graphs. Data visualization helps you to perceive the trends or shares at first glance.

Feature four: Favorites and Bookmarks. Favorites function lets you save the created reports in your personal account and display their names on the right-hand side when you log in. Bookmarks is a similar function, however, you can share the bookmarks with other users of Adobe Analytics reports. One more way of sharing is a report is generating a short link to it and sending it per email.

Feature five: Dashboards. You can include any existing report into a custom dashboard. You can arrange repotlets in the dashboard the way you need and select if only the graph, the data table or both should be displayed. The dashboard allows you to set rolling or fixed time range for all the reports it includes. It is also possible to print, share and download dashboards. Dashboards are a convenient way to to summarize the performance of your website and monitor how business goals are being met.

downloads
Download Options

Feature six: download options. Each report can be downloaded and saved in different formats, e.g. XLS, CSV, PDF, etc. You can set up automatic download and forwarding of a certain report to a stakeholder.

Feature seven: pathing reports. There are several reports in Adobe Analytics that enable you to record and analyze the user journey on the site to improve its usability. The most flexible reports are Path Finder and All Paths. In Path Finder, you can select user journeys that start with, end with or contain any page you are interested in. All Paths report shows user journeys on the site for a selected period, and you can additionally filter the report by path length.

Feature eight: Activity Map. This browser extension is easily installed and visualizes user activity on a given page. It records what links on a page were clicked within a reporting period and presents the data as bubbles with numbers or as a heat map. This is especially useful if you want to investigate where to place the most important information on your page or how the links placed “below the fold” perform.

Feature nine: Workspace. Workspace is a tool that allows you as a user to create customized reports and visualizations as well as print and share them. For example, you can select any segments and present them on a Venn diagram or a doughnut chart. Other diagram types include: bar charts, scattergrams, cohort tables, etc. Workspace gives you immense flexibility in working with data and presenting the results to stakeholders.

Feature ten: Targets and Alerts. Target empowers a company to access its current performance against a business goal (e.g. a desired number of visitors or page views). Adobe Analytics will generate a  report that shows how the target is being met. Alerts, once you set them up, is a basic way to oversee the stability of the system. Should  the main indicators fall below or rise above a secure span, the system will send out an email alert to the specified users.

Alerts
Adding an Alert

As you see, Adobe Analytics offers a number of possibilities for deep-dive into the data. However, due to its complexity and cost, this software is only useful for large companies with significant amount of traffic on their website.

All Images: Adobe Analytics (screenshots and featured images).
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Common Reasons for Incorrect Data in Web Analytics

Incorrect data is an acute problem for online businesses. While most businesses have started to realize the importance of data-mining and performance-based marketing, it seems that some of the effort has been going in the wrong direction. The point is not about having as much data as possible, but rather having the data you can actually work with – structured, complete and correct.

Below I will outline some common sources of erroneous or incomplete data and give some advice on how to avoid the mistakes.

Unclear or imprecise definitions of KPIs.

Even if your company is not very large, make sure that everyone dealing with data understands precisely what is included in statistics and how it is calculated. It even makes sense to create a written document defining the KPIs, no matter how simple or self-explanatory they might seem.

For example, if you want to calculate CTR (click through rate) for banner advertising, which is, in essence, the number of clicks on the banner divided by the number of banner impressions, you can ask yourself a range of questions. Do you want to apply unique banner impressions and unique clicks (that is, per user)? At what intervals do you want to measure the CTR (per hour, per day, per week)? How do you go about natural fluctuations in CTR (e.g. during the day vs at night), do you want to make them part of your statistics or just ignore them and take the average? How do you group the data: by region, by traffic source, by banner type, etc.? If you run banner tests, do you want to exclude the test data from the overall statistics?

As you can see, the answers to these questions might greatly influence the outcome, i.e. the CTR your  data analyst will produce at the end of the day.

Technical issues

The more complex a system is, the more likely it is to malfunction. Always check for technical issues if dealing with data inconsistency. For this, it is best to work with statistical and technical benchmarks, based on the normal system behavior in the past.

Technical problems influencing the data consistency may, firstly, be caused by the under-performance of the system itself (for example, the banners are not served for a period of time, or are not displayed correctly in some browsers). Secondly, even if the system functions well, there might be problems in capturing or storing the data (e.g. not enough RAM to perform operations causes the database server to crash). And thirdly, if you do not query the database directly but let the data flow through a business intelligence system, there might be all kinds of compatibility problems between the systems.

Incorrect calculations

This is the least predictable source of data inaccuracy. It starts with how the data is collected and aggregated in the system. Even if one has enough clarity on how the KPIs are constructed, there is always a chance that the setup of the analytics system will divert from the desired parameters. Furthermore, if data processing and analysis are largely done manually, the probability of a mistake rises with every step. Also, merging the data from several sources can impact the data consistency in a negative way, especially if some parameters have to be converted or rounded up before the merging.

Thus, human factor should not be underestimated, and the only way to minimize the number of errors is double checking the calculations, or better still, automating as much of data processing as possible.

In conclusion, if you find that the data does not “seem right”, even after you have excluded every possibility of a mistake, you might need to look for the reason outside of your analytics system. One of my previous blog posts looks at this issue in more detail.

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Mobile Marketing KPIs: What to Consider

This post gives an overview of essential mobile marketing KPIs that would be considered for a performance dashboard. It also outlines a general framework of how these KPIs relate to each other.

mobile_kpis
Mobile marketing KPIs structure

Conversion funnel is an important part of any performance dashboard. The funnel shows how the user travels through different stages and interacts with the ad and then with the product.  A simple conversion funnel for a mobile marketing display campaign advertising for an app would be:  Impressions-Clicks-Installs-First Interaction, with percentage share between them.

Return KPIs can be measured as monetary values: revenue per user, lifetime user value, etc. However, non-monetary KPIs (number of installs, daily active users, daily active paying users, average session length, etc.) are also important, especially in the cases where monetization is detached from the download (free apps or image campaigns).

Cost KPIs will largely depend on your payment model for the advertising. Possible ways are:

Cost per mille (per thousand impressions)

Cost per click

Cost per install (also possible: cost per lead, cost per download)

Cost per engagement

Cost per revenue or revenue share models

ROI is calculated as a percentage share of return on what has been invested in advertisement, for a product that does not monetize immediately, the ROI is calculated per time period (daily, weekly) in user cohorts.

The framework at the bottom of the picture shows how the KIPs can be adapted to achieve a significant level of preciseness and data consistency.

Timeframe includes both how the KPIs are aggregated (daily, weekly, monthly) and what time periods they cover (past, current or future/trend data).

Segmentation is essential for getting the working data. Making the segments too broad will impact the data consistency negatively and working with micro-segments is mostly too difficult to implement and does not allow to make solid conclusions based the data sample.  As an example, consider segmenting the data by country or region, by traffic channel or by device type.

Sample statistics includes the parameters that analyze the quality of data and how the sample behaves in general. Instead of just aggregating and averaging the data, consider such parameters as median, min. and max. amounts and standard deviation. Besides, look at the size of the segments you work with: which of those have the strongest impact and why? Also, learn to recognize patterns in your timeframe, and how the data fluctuates on a daily, weekly or seasonal basis.

After you have decided on the KPI data you will include for your dashboard and reporting, consider making the data more visual and more structured for the intended users. In one of my next posts, I will cover the issue of data visualization in more detail.

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