Moving beyond variable data to variable content
In the mid-20th century, companies like Xerox, Canon, and Hewlett-Packard (HP) developed the first laser and inkjet print technologies, essentially reinventing a 500-year-old medium. The ability to print without movable type and plates opened the door to personalized print communications. For the first time, we weren’t limited to static, one-to-all messaging. It’s not surprising then that the term variable data printing (VDP) has been around for as long as the technology that made it possible. In fact, print industry expert Frank Romano has suggested that he has been using the term since 1969, the same year Xerox invented the laser printer.
You’ve seen variable data printing in action if you’ve received a letter addressed to “Dear [Insert your first name here]” or an invoice or a bank statement. It’s a simple, linear method of personalization where the contents of a data field (every letter, number, and space) are copied and dropped into a defined variable field. While VDP was groundbreaking in the 1970s, our current digital, data-driven marketing world demands we take the next step: variable content in print. Therefore, it’s important to understand the difference between variable data and variable content.
With variable data, the data itself is the message. With variable content, the data informs the message. At More Vang, we think of the distinction as Personalization vs. Relevance.
We accomplish variable data printing by mapping the columns and rows in your data into the print deliverable. For example, the mail panel on a postcard uses your CRM contact data like so:
>>First name<< >>Last name<<
>>Company name<<
>>Address 1<<
>>Address 2<<
>>City<<, >>St< >>Zip<<
By contrast, variable content allows you to create subtly different marketing deliverables by varying both the images and copy based on information in the data. The linear process we use in variable data printing evolves into more of an algorithm with “if/then” scenarios driving the variable content. For example, if you have gender and job level as two fields in your data, you can tailor the images and copy accordingly to make the message relevant to each individual.
An entry-level, male professional may receive a postcard that looks like this:
While an executive female may receive a postcard like this one:
Not only are the images different, but the headline copy varies as well. If we were to show this example as a workflow, it might look something like this:
This is a fairly simple, one-to-many approach you can implement by segmenting your data into defined groups. You can imagine how this diagram might grow as you get more granular. What if you considered image selection based not only on gender and job level but age and ethnicity as well? You’d need far more than four image assets to accommodate all of the possible combinations. It’s easy to get carried away and overwhelmed by the amount of content you’d need to create to do truly one-to-one marketing. Segmenting data based on personas is a great way to make your marketing more relevant while not expending too much time and effort creating customized content.
We expect digital algorithms, like those used by Meta, Amazon, and Google, to shape our online consumer experience. It’s time to apply that same level of personalization to our other marketing channels. Delivering this type of Algorithmic Direct Mail at scale, however, is no easy feat. It requires creating customized content, as well as data analytics and segmentation. But considering 80% of consumers are more likely to transact with a brand when they’re offered personalized experiences, it is worth the effort to make your direct mail as relevant as possible.
