Applying database marketing techniques to digital marketing.

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A few days ago I came across this interesting blog post in which author David Raab describes the differences between digital marketing and database marketing.

He describes the 3 main differences as:

  • database marketing has known contacts and heaps of data for them (or at least you’re moving in that direction), whereas with digital marketing you’re generally dealing with anonymous visitors.
  • database marketing allows you to predict response, rather than react to it.
  • database marketing tightly controls the message each customer can see, whereas digital marketing has vastly less control over who sees what.

He goes on to say that database marketing generally focuses on prediction, whereas digital focuses on reaction; database on precise response, digital on inferred response.  As well as a few others.

It’s a really great read and remains relevant today despite the fact that it was written around 3 years ago, but there a couple of things David discusses that have changed over time.  

Time has improved capability.

Firstly, I completely agree that with database marketing you’re working with known contacts and known demographics and other attributes; whereas with digital channel you’re generally working with unknown visitors. However, as digital becomes more sophisticated and customers return and are logging in, the lines are blurring between the database marketing and digital marketing. In fact, you’re now able to apply database marketing to digital marketing.

Use your known customer traits.

I’m happy to say that nowadays it’s less about web analytics, more about customer analytics. The industry is slowly progressing forward from web analytics, through digital analytics and digital customer analytics, to full blown customer analytics (the nirvana).

By measuring behaviours of known customers across digital channels, you can leverage and/or enhance the data in the CRM or other database platforms. By focusing on the various behaviours of different types of customers, through these insights you can change digital behaviour.  

Being familiar with the characteristics and traits of the “known” visitors means through analytics you can now use the information to build lookalike or propensity models. And because they exhibit similar traits to known visitors this means you can begin to target unknown visitors (a.k.a. prospects) with different experiences, content and offers. Using tools like Adobe DataWarehouse you can export behavioural and conversion data against known customers, and use that in tools like SPSS to build models.

So, happily, I think the main differences that David talked about can now blur due to significant advances in measurement technology, the skill sets of the individuals doing it, and the types of digital solutions that our audiences are interacting with.  You just need to want to do it.


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