Enrich your contacts’ profile with web access and navigation information

Thanks to the releases of the last few weeks, we have completed a project we had been working on for a few weeks to allow you to enrich your contacts’ profiles with information related to web browsing. A new version of Sendhttps://explore.contactlab.com/send/?lang=en, the platform’s digital messaging feature, is now available.

What can you do with this release?

Anyone who has to do with digital marketing and newsletters has at least once heard of url tracking. And anyone who deals with websites, portals, ecommerce, has heard at least once about anonymous contacts.

With this release we create an even closer relationship between the two concepts, collecting information on the behavior of your email audience and naming some of the anonymous visitors, for example, to your ecommerce.

Every time you send an email and insert traceable links, you can know how many of your recipients clicked on that particular url and arrived at your online shop. What you cannot know, however, is who among your contacts has accessed and performed specific navigation actions.

With the aim of collecting more qualified data on the profiles in your database and customising the relationships with marketing activities with content designed for each contact, we have introduced a feature in the platform that allows you to insert a new parameter in the links used in your emails. Its value is automatically taken from the field that identifies the contact in the Marketing Cloud and allows the association of the latter with the behavior detected on the website addressed by the links.

What it means

In this way, the immediate identification of a contact in the Marketing Cloud makes it possible to:

  • Enrich the profile with new information characterising the individual user’s behavior.
  • Profile and create segments for your campaigns from the information collected.
  • Hypothesise the activation of appropriate triggers and the triggering of specific customer journeys or differentiate progressive steps.




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