Written by Claus Sølvsteen, Sales director, Peytz & Co

(Peytz & Co takes part in the FEDMA Pan European Email Marketing Benchmark)

Every Email Service Provider offers its customers a “Unique Open Rate” figure. However, different methods are being used to produce the number, making it difficult to compare results. Newer e-mail clients are making the open rate increasingly difficult to measure correctly. The result is that the “Unique Open Rate” figures are both inaccurate and incomparable.

The Danish Email Service Provider (ESP) “Peytz & Co.” has developed a new method to calculate the unique open rate. The Peytz Open Rate (POR) is used by major Scandinavian media and companies, providing these companies with far more accurate figures than previously.

Unique Open rate is loosing value

The reason why the Unique Open rate has decreased in recent years is well known. Outlook and numerous other off- and online e-mail clients have introduced a “show images” button in email clients, resulting in images in e-mails only being shown when an individual user specifically requests to view the images.

While this functionality offers the users faster email download speeds and increases security, it also blocks the only method of notifying the sender whether an e-mail has been opened or not. This notification is handled by a small gif in every e-mail sent by mass-mailing systems. When an e-mail is opened, the gif is called from a server and that call is measured. We call this gif the “counting-gif”. By assigning one counting-gif for each sent newsletter, the ESP can calculate exactly how many different e-mail recipients have opened the newsletter. This number is referred to as “Unique opened e-mails.”

However, this massive blocking of images in emails has resulted in a drastic decline of the “Unique opened e-mail” figures, regardless of whether the percentage of people actually opening the mails has remained constant.

The bottom line is that nobody really knows how many people have opened your emails. The unique open rate tells you how many have AT LEAST opened them. But as it does not include those that have opened the mail without viewing images - it will always be incorrect and lower than it should be.

Three known numbers and one secret one

The total number of opened e-mails includes:

1 Unique opened e-mails showing the counting-gif
2 Unique opened e-mails NOT showing the counting-gif

The problem is that the second number is un-measurable. The second number is the hidden one.

Now, any ESP can measure three numbers accurately:

1 Unique opened e-mails showing the counting gif.

3 Unique clicked links deriving from e-mails showing the counting-gif
4 Unique clicked links deriving from e-mails NOT showing the counting-gif

When reporting the amount of clicks in e-mails to clients some ESP only use
3 Unique opened e-mails showing the counting gif
while others add the number of undeniably opened e-mails, deriving from
4 Unique clicked links from e-mails NOT showing the counting-gif

Adding these two numbers increases the numbers of opened e-mails, and thereby the Unique Open Rate. But the two different methods make the results incomparable.

Calculating the Peytz Open Rate

The “Peytz Open rate” uses all three numbers and adds a third, estimated number.

We will use an example of a campaign sent to 100.000 e-mail addresses.

In the example the ESP has counted:
100.000 sent e-mails
30.000 opened e-mails showing the counting-gif
3.000 unique clicked links from e-mails showing the counting-gif
2.000 unique clicked links from e-mails NOT showing the counting-gif.

We now compare the relationship between unique opened e-mails showing the counting-gif and unique clicked links deriving from e-mails showing the counting-gif.

3.000 clicks from 30.000 opened

In this example, the relationship is 1:10 - so 1 out of 10 readers clicked on a link in the email.

Then we make an assumption. We assume that people behave in the same way, regardless ofwhether images are shown or not shown in their e-mails.

This assumption can obviously not be true for e-mails that only contain images (but these e-mails usually get caught by most spamfilters anyway). The assumption is obviously always true for e-mails containing only text, like really serious newsletters. And for most e-mail campaigns the truth is somewhere in between. As we have no knowledge of any scientific studies in this area, this is a pure assumption based on our experience and track record in this market.

Our assumption makes it possible to calculate the number of e-mails opened NOT showing the counting.gif

2.000 clicks equalling 20.000 opened (1:10)

Tadah! The secret number is revealed. Based on assumption and math.

The “Peytz Open rate” is then the total number of measured and estimated opened e-mail (30.000 plus 20.000) equalling 50.000 divided by the total number of sent e-mails, multiplied by a hundred:
(50.000/100.000)*100 = 50

The traditional “Unique open rate” is the total number of measured opened e-mail (30.000) divided by the total number of sent e-mails, multiplied by a hundred:
(30.000/100.000)*100 = 30

The “Peytz Open Rate” in this example almost doubles the number of opened mails and gives customers a far more realistic overview of the reach of their message.

E-mail based newsletters are becoming an increasingly important marketing tool for many companies - and receiving inaccurate statistics can result in strategic marketing mistakes that could have been avoided.