The Email Marketing Metrics for Decision Making

Posted by Gabriel Gastaud on

The daily work devoted to email marketing programs involves a lot of people and departments. Making decisions is a large part of the job, involving various tests, improvements, and learning effects required at the different stages of the email marketing funnel. Having overall visibility in order to identify the consequences of each action is key in order to ensure improvement of the whole program, and not to simply fix a small patch without understanding its effect as a whole.

Key Performance Indicators help you monitor and make decisions. However, not using the correct metrics in your decision making can lead to serious effects and unfortunate consequences for subscriber behavior. To prevent this, you need to rethink how you use KPIs for better data-driven decision making.

Rethink the Key Performance Indicators 

Open and click rates are the most common metrics in email marketing. Unfortunately, they can lead you into a misleading fog. The main reason why these metrics could be biased is that their calculations use the hypothesis that you constantly reach 100% inbox placement. Making decisions based on open rates requires perfect visibility on your inbox and spam placement rates… but is it actually the case for you? Probably not!

Having an overall picture of the email conversion funnel helps you to identify the right indicators for the right improvements. We have drawn up the 5 steps of the email funnel and presented the related indicators that you should monitor in order to improve overall program performance.

Focus on the 5 steps of the email conversion funnel

emailfunnel

From the sending platform to the end-user conversion, we drew up the 5 following steps of the funnel:

  1. Delivery – The starting point is the delivery of your emails which means that they have been accepted by the receiving server (usually the mailbox provider).
  2. Inbox placementDelivery is different from deliverability. This second step confirms whether your emails have reached the inbox or if they have been placed in the spam folder.
  3. Inbox Activity – Reading your content is the first measurable action between the subscriber and the email program and demonstrates their interest or curiosity in your communication. Inbox Activity also covers other user behaviors in the mailbox interface, including actions like delete without reading or forwarding on your content.
  4. Clicks – Clicks demonstrate specific interest about the content in your message. While most clicks usually lead to the website landing page, they could also include traffic toward the unsubscribe process.
  5. Conversion – Conversion means that expected goals have been successfully delivered. The definition of these goals is related to the business and to the email communication goal (i.e. purchasing order, survey validation, etc.).

The right metrics for the right decisions

Deploying dashboards with misleading metrics leads to wrong decisions. According to the defined funnel, the following metrics have been graded by their accuracy for decision making. The end goal is to help you define the best metrics to monitor, and improve your decision making.

Step #1 Delivery

Metric

Accuracy for Decision Making

Comments

Delivery Rate
Delivered volume / Sent volume

Low

Useful in order to identify general problems but inadequate for decision making. The reason why delivery rate changes depends on several factors requiring you to investigate other metrics like your hard bounce rate and rejected rate.
Hard Bounce Rate
Hard bounce volume / Sent volume

High

Relevant for evaluation of list quality. Typically, poor data collection points and list hygiene are the common causes of a high hard bounce rate.
Soft Bounce Rate
Soft bounce volume / Sent volume

Medium

Relevant mainly for identifying blocks from mailbox providers, based on reputation, policy or technical settings.
Rejected Rate
Soft bounce (reputation & policy related) / Sent volume

High

Relevant for identifying delivery problems related to spam and reputation.

Step #2 Inbox Placement

Metric

Accuracy for Decision Making

Comments

Inbox Placement Rate
Return Path proprietary data (Inbox Monitor)

High

Must have in order to identify deliverability issues per mailbox provider.

Step #3 Inbox Activity

Metric

Accuracy for Decision Making

Comments

Open Rate
Opens / Delivered emails

Low

A common metric which is interesting but not as beneficial as a decision making tool. As open rate requires your recipients to allow images to show (the tracking of opens requires enabling images to be displayed) the read rate is potentially more accurate metric as it includes emails that are read even when images are disabled..
Opens per Unique User
Total opens / Unique opens

High

Powerful in order to identify serious interest, and viral marketing.
Read Rate
Return Path proprietary data

High

Must have in order to identify reads including activated and inactivated images. Read rate based on Inbox placement will provide you with more accurate insights.
Complaint Rate
Complaints / Delivered emails

Medium

Useful in order to identify spikes and patterns for complaints sources. Please, note that complaints coverage (via FBL) has some restrictions. In addition, lower complaint rates may be due to Inbox placement troubles.
Inbox Complaint Rate
Return Path proprietary data

High

Relevant for understanding negative behaviors based on Inbox Placement.
‘This is not Spam’ Rate
Return Path proprietary data

High

Click Rate on the “This is not spam” button. This button is powerful in order to change filtering and indicate how engaged your subscribers are.
Deleted Rate Before Reading
Return Path proprietary data

High

The email is deleted before reading, indicating poor engagement between the subscriber and the brand.
Forwarded Rate
Return Path proprietary data

High

Efficient in order to evaluate the viral effect and the degree of subscriber engagement.

Step #4 Clicks

Metric

Accuracy for Decision Making

Comments

Click Rate
Clicks / Delivered volume

Low

Usual metric used in order to identify email activity, however, this metric is focused solely on clicks divided by the delivered volume, and does not account for the intermediate steps (Inbox Placement and Inbox Activity). A change in click rate is related to many factors that are ignored in the existing calculation.
Click to Open Rate
Clicks / Opens

High

The best metric in order to identify the quality of the content proposal and the design of the communication.
Core Business Click to Open Rate
(Clicks – non-positive clicks) / Opens

High

Click to open rate metric excluding clicks on non-positive links (ie. web version, view T&Cs and unsubscribe clicks). This is an accurate metric for the analysis of responsiveness.
Unsubscribe Rate
Unsubscribes / Delivered 

Low

This metric can be misleading and should not be used in isolation. A decreasing unsubscribe rate may not reflect a positive increase in engagement, as often, a decreasing unsubscribe rate is more accurately a result of a decreasing inbox placement rate. Making decisions based on this metric alone can be a mistake as the metric does factor changing in inbox placement and inbox activity into its calculation
Evasion Rate
Unsubscribe clicks / Clicks

High

Evaluates the clicks distribution between unsubscribe and other links. Powerful in order to identify disengagement without being misleading.

Step #5 Conversion

Metric

Accuracy for Decision Making

Comments

Bounce Rate (web analytics)
Visits without any clicks / Visits

High

Evaluates the relevance and effectiveness of the landing page. The lower this rate is, the better it is for your funnel.
Percentage of New Visits
New visits / Visits 

Medium

Useful in order to identify the part of the audience which is returning, or new visitors to the website during the past 30 days (average depending on web analytics session duration).
Conversion Rate
Conversions / Visits

High

Useful in order to identify the proportion of visits coming from email that have been converted. This conversion action is related to the company and the campaign goals.

Based on this funnel we can understand why some usual metrics like open rate, click rate and unsubscribe rate are not – in isolation accurate for decision making. Each indicator has to be calculated on the current or previous step of the funnel. Without this context, the learnings may be biased. Decision making for marketers should account for these biases in order to improve their overall email program performance, and the ROI that is generated.


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About Gabriel Gastaud

As a Senior Email Strategist, Gabriel Gastaud is part of the Professional Services team at Return Path. He has worked with numerous major brands to improve their sender reputation by solving inbox placement issues and improving overall subscriber engagement. Based in Paris, Gabriel has eight years of email marketing experience. As a data-driven consultant, he understands how to analyse data, identify key learnings and deliver the best possible results.

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