2013-09: Behavior vs. Intention

I had an interesting conversation the other day.  It stemmed from the name of this networking lunch group that I am part of.  It’s called Burgers & Granola (B&G).  The name of the group originated a few months ago, when we started to meet on a regular basis.

At the time, we had a conversation on online advertising, and how most advertising is moving towards behavior based marketing as the “truest” form of understanding how a person is likely to behave.  One person in particular described how there should be a different setting so that you can chose whether you are being marketed to because of your behavior as opposed to your intentions.  Interesting idea but it felt as though I heard this before.

In a way. We’re talking about attitudinal vs. behavioral marketing.  Most of the marketing used to be attitudinal (answering questions like “How do you feel about X?”), and there’s been a big push towards behavioral marketing (Measuring patterns like “We know that you don’t buy X”).  Advocates of behavioral marketing will tout the fact that people lie to themselves (“I need to lose weight, so should buy granola”) but that their behavior is better model for what they actually do (“I bought a burger instead”). With big data, low cost of processing and storing data, and advent of mobile and digital (see my post on demographics of 1).  There is good evidence that behavioral marketing is here.  But does that mean that attitudinal marketing is gone?

I would argue that it’s not.  Attitudinal marketing comes very much from primary and qualitative research.  Things like focus groups, phone interviews, participatory meetings, email and intercept surveys are tools used for attitudinal marketing.  It has very much a role in helping understand people’s perspective on a topic (e.g.: politicians), but also in understand why certain behaviors did not happen (e.g.: why didn’t you buy product X), to develop clues as to how to refine the product.  You could argue that A/B testing allows to do this, but realistically for certain products (a TV, a banking account) A/B testing might be hard to successfully study.