This recent Marketing Profs article was penned by David Bean, Ph.D. - the founder of Attensity, one of the largest text-analytics companies around.
I think it, again, misses the mark. And, again, this is not in any way a criticism of David, his company or text analytic tools in general (I really like these tools). It's a criticism of how they are currently getting pitched.
A good portion of his article argues against traditional research methods in favor of harvesting unstructured conversational data from consumers and customers. There are two major problems with the assumptions behind this argument.
Firstly, traditional research methods, by and large, are not broken. David refers to traditional research methods as struggling to 'make sense of it all'. As if they are somehow unfocused. Yes, research practitioners come across unfocused briefs, but they are not the norm. The norm is to have a clear objective. Most research gets commissioned when there are some specific issues afoot. A lot of this research solves these issues effectively. And it does it without asking consumers questions like 'So what do you think would make this better?' - which, in most situations, doesn't get you any good answers.
Yes there are bad examples of questionnaires and focus groups. But this is usually the practitioners fault, not the methodology (if you're having a hard time finding a good research firm, drop me a line, I can point you in the right direction).
The irony in David's article is that after a page of argument against using structured research to solve issues, he pulls out a quote from a structured research study to prove his point! (470 responses from Planet-feeback.com is not the best source to understand the 'trust in advertising' issue).
Second, there is an assumption that unstructured data is more useful to understand everything from new product ideas, effectiveness of marketing campaigns, product positioning, etc. This is a tall order. People will write in to companies with emotionally laden stories of how they were helped/liked/loved/made to feel good, or the opposite. But they rarely talk about how the brand 'needs to be slightly more edgy to really make me like you'; 'I loved your ads but you need to change the color tones to more suit your brand's style; or 'here's a product idea for you...'. Fanciful stuff.
The myriad of stories you receive from customers need a solid layer of interpretation before they become useful. This typically involves structured research.
The gist of my argument is don't try and create a cure for a disease that doesn't exist. Text analytics is a great augment for your research and intelligence gathering efforts, not a replacement for them. I know there is a need to go after an internal budget when selling in a solution like this. And there is pressure to justify the cost. But this argument will be a tough sell.
A good text analytics tool is like a radio. Without a radio, you can't tune into a radio channel. You just get a bunch of meaningless static. Meaningless static is what most companies hear when they try and listen to consumers or customers. A text analytics tool gets you access to the channels you need to listen to. From there, you can decide what you need to know in more depth.
The problem with a radio is that it is one-way communication. You can't direct the conversation. This is a limitation of the medium. It's not a selling point.
