Is a customer data platform (CDP) the answer?
Email open rates have long been a key indicator of campaign performance for marketers. Last year, Apple launched Mail Privacy Protection (MPP) to stop the firing of the tracking pixels used to indicate an email was opened.
Since its introduction, MPP has been rolled out across Apple mobile and desktop operating systems and will have 90% adoption by the end of 2022. With Apple having 50% of the market in many countries, this has a big impact for marketers who have used email open rate as a success metric to help plan future content, send, and lead scoring strategies.
In short, it means that they will see dramatically lower email open rates for contacts using Apple products or, artificially high statistics if they include Apple’s synthetic response. For further information on the effect of MPP on open rates, please read Chad White’s excellent blog post, “How to define active email audiences.”
The loss of open rates should not be mourned, though, they were arbitrary. The loss presents an opportunity to devise new, more meaningful metrics to guide decision making and illustrate success. In fact, the opportunity helps us look more forensically at the attributed influence of email to the conversion.
See how email guru Chad White and the rest of Oracle Marketing Consulting can help you with your email campaigns.
To find these new metrics, marketers must look to behavioral data to understand a contact’s actions when they receive an email. Traditionally, marketers have used the ‘click through’ metric to understand the number of contacts who went from an email to the website. (That data point is generated when the contact clicks on a link within the email text.)
When a contact does not click an embedded email link, it does not mean that the email message has not resonated with the target audience. Many contacts will read the email and visit the website after to research the offer. From a data perspective there will appear to be no direct link between the email sent and a visit to the website; however, the relationship can be plausibly inferred using identity resolution and correlation.
Many digital businesses now use customer data platforms (CDP) to learn a website visitor’s identity. Marketers use identity resolution to establish if a visitor is known or unknown to the marketer and manage communications to them accordingly.
Learn how to make your customer data more effective with a CDP.
CDPs pull in data from many back and front office applications—including email—to do this. Resolving a visitor’s identity against their email address has three outcomes:
The last advantage may seem tenuous if you are accustomed to a direct correlation from pixel tracking or click throughs. In this new privacy centric environment, those metrics are more difficult to obtain.
See how to anticipate customer needs with a CDP.
The best way of understanding this new paradigm is to look back to the days of advertising when there was no direct link between a contact viewing an advertisement and a contact taking an action. In those days, it was very apparent in call centers when a TV advertisement containing a Freephone or 1-800 number was broadcast— the call center would be deluged with inbound calls.
This was a direct correlation. Any inbound call following the broadcast was attributed to the advert as a measure of its success. In the same way, digital marketers can attribute a website visit to an email campaign if the visit was within X days of the email send date. The ‘X’ is a variable that can be agreed internally, for example it may pertain to the number of days an offer was available or an arbitrary week. An even better attribution metric would be to take a second step in this evolution and correlate the content viewed to the content of the email.
The loss of email pixel tracking is like the loss of the third-party cookies in display advertising, it has a negative effect on marketing effectiveness. Marketers need to adapt in this new era of privacy constraints and use more first-party data points to establish better metrics. Embracing these new correlated metrics will give deeper insights and better email attribution to marketing effectiveness but it will require the marketer to wean themselves off the data produced by pixels.
Interested in learning more on how to combine customer data from every source to create a single, dynamic, and accurate view of each customer?
Adrian is passionate about data and has worked in digital since the 90s. Now, he helps Oracle's Strategic accounts get the most from data.