At its most basic level, personalization is a message whose content is tailored to the stated or implied needs or interests of the recipient. Personalization is made up of two components: data and content.
The term “personalization” is used to describe everything from adding someone’s name into an email to using predictive analytics to send a re-engagement email when a subscriber is likely to become inactive.
Since each of these things is, technically, personalization, we break down personalization as a series of steps in the evolution of your email program:
Hopefully, your days of batch and blast are distant memories, and once you’re ready to move beyond simple data insertion, there are basically two sources of data you can tap into to evolve the personalization of your email program. They can be leveraged in tandem or alone:
Preference Centers:A much-heralded feature where a person goes to a site and selects what they want to hear about.
My personal view is that preference centers are good from a permissions perspective if you have several communications channels (email, SMS, phone, direct mail) running and you want to provide a single place for customers to opt in or out, or if you have a product offering that is specific and long-lasting.
For example, if you are a large company, and a customer would likely want information specific to a product line only, then it may be worthwhile to ask for a product preference. But for many, especially retailers with thousands of SKUs, the idea that someone’s interests hold over time is difficult to believe.
The core problem with preference centers is that very few customers use them, so their use in personalization is ultimately limited.
Contextual content:Use environmental data to tailor the message at the time of open.
By environmental data, I mean what we can know about them based on their location at the time of open, weather, device type and so forth. These data are usually provided by an outside service that can augment it, combined with data you have internally.
Using these data, you can better segment which content to show each individual. For example, a national retailer may use home location or open location to determine whether or not to show a parka in January. The point is that this is a shortcut to collecting data, but instead using data that are immediately available.
Link categorization:Categorize all of the links in your emails, and depending on what people click on, infer some level of interest or preference.
This is where the rubber meets the road, because you can triangulate what people are interested in if you do it over time. The challenge to this is setting up tracking properly — it takes more time, and the analysis of who is clicking on what can be complicated if you are doing more than simply seeing whether or not someone clicked on a link with a specific categorization.
If you start to do any trending or priority of categorization, this generally requires a tool set beyond an ESP (email service provider) to determine priority of interests. But once you have some level of categorization, you have information that allows you to do two things:
An advancement on link categorization is leveraging site tagging to understand what a person is looking at or has looked at to inform selection criteria for a campaign or a piece of content.
Today, a mini-industry has popped up inside of the email industry to send “triggered” emails based on particular page views because many of the larger traditional email platforms have failed to capitalize on this need. But that “triggered message” industry is ultimately geared toward sending an email in the moment vs. understanding site actions over time and using that information for content selection.;