The “consumers don’t do what they say” dilemma, and why Decision Science is the answer
What consumers say don't always line up with what they do. Some believe the answer is to make a choice: trust their words or trust their behavior. I say both of these options leave you with a half-story. The best option is a third one, which is to study consumers where the say-do disconnect happens: inside their decision-making processes. Leveraging Decision Science can help you understand how consumer decisions get wonky so you can address the wonkiness and help consumers get what they really want.
You’ve got a great product or service, and you want a lot of people to buy it.
You’ve done your due diligence through consumer research and big data analytics.
You’ve optimized your product/service features. You’ve perfected your pricing model. You’ve nailed down your positioning and messaging, and you’ve hired the right agency to take your brand to the next level.
You’ve done all the right things.
But what if you’ve checked all the boxes but still don’t see the kind of revenue growth you were looking for? What could be wrong?
Well… a few things. In this post, I’ll talk about only one thing, but it’s a big thing, and it has everything to do with a shift in focus from understanding consumers as people to understanding their decision-making.
If you’re focusing on consumers as people, you’ll eventually come across the realization that consumers don’t always do what they say they want to do. This is a problem, because if they’re saying one thing and doing another, you can’t really know how to build something right for them. The contradiction makes certainty in your own decisions hard.
Some businesses address this dilemma by relying only on behavioral data, deciding against market research, whether it’s qualitative (like focus groups or interviews) or quantitative (like surveys). Other businesses do the opposite, throwing their energy into market research or social listening.
Though choosing makes things easier (it eliminates the contradiction), it sweeps half of your insights under a rug. And what you choose not to see can surprise you.
A better approach would be to examine consumer behavior in that space where the say-do contradiction is actually created: inside the decision-making process.
Think of decision-making as a sort of computational program: you input data or information, the program does its magic, and then it spits out an answer. As long as the program works, it’ll give you the right answer.
But humans don’t have great computational programs because, well, we’re human. We have a tendency to employ mental short-cuts and be swayed by our social situations. Stuff goes into our computational program, gets warped, and then comes out disjointed. Knowing where things get off track can help you address it. It can give you insight into how to nudge consumers into doing what they really want to do.
Here’s an example. You’re about to launch a startup that helps people find the right landscapers for their residential or commercial leads. Consumer research indicates strong appeal. Research also indicates that residential homeowners are disappointed in the level of quality they get from who they hire. Commercial property owners are frustrated with the lack of landscaping expertise they’ve seen.
So you have your landscapers listed by expertise: people who just need a mower can hire a mower, and people who want waterfalls and indigenous plants can get exactly that. You also enable consumers to rate their landscapers on quality.
But no one is using your service. Early users are on your site, poking around. They’re selecting landscapers and looking at profiles, but they aren’t hiring any. They are, however, spending time on your site’s content pages, which have articles offering landscaping ideas. You decide to follow the behavior: you hire a content strategist to create more content and to figure out how to use that content to convert viewers into hirers. You assume that content is what they value, according to their behavior, and that content is the way to their hearts.
But then your Director of Marketing reminds you that, according to survey research, people interested in hiring landscapers aren’t really interested in reading about landscaping. They want their landscaper to know everything so they don’t have to.
So what now? Discount the market research? Or discount the behavioral data?
A dive into the decision-making process could be the answer. For instance, what if people who came to the site do want to hire a landscaper and do find the site appealing. What if they actually love the ratings and filtering options.
What if the real issue was that, for people with their existing landscapers, the costs of switching (finding someone new on your site, communicating with them, trying them out) are too high (who has the energy?), and the anticipated regret might be very real (what if the new landscaper is worse than the old one?). Maybe their existing landscaper is sub-par, but someone new is too risky.
For people who have no landscapers yet, maybe there are too many landscapers on your site to choose from (more options, and more trade-offs, can make choice deferral more likely).
The decision-making process introduces new variables to the equation - variables that matter but that are often overlooked in market research or big data analytics.
There’s more going on than what people say or what they do. Also relevant is how they decide. Without that missing piece, and without Decision Science, you may be seeing only part of the picture.
Which means you may be missing out on a huge part of the revenue.
#consumerinsights #consumerbehavior #businessstrategy #consumerdecisions #decisionscience #marketresearch #bigdata