It’s a well known bromide of user research: customers don’t always know what they want – even when they think they do. Just because they can articulate it explicitly and provide detailed use cases, is no guarantee that once they get the thing they’ve asked for and desired, that they will in fact want it.
This is especially problematic for products that rely on ongoing usage and revenue (e.g. through recurring fees or advertising), and sell the up-front product for little or no cost as a loss-leading carrot. Inability to sustain interest after the initial value has been sated is the nightmare scenario for products like this – which include virtually all mobile apps that want to be actual money makers.
Bryce Roberts offers a cautionary tale of his own making:
For years I told anyone that would listen how much I wanted an app that let me snap a picture of my meal and would tell me how many calories were on the plate….
So when Meal Snap was announced last year, I was thrilled.
I quickly paid my $2.99 and downloaded the app before heading out to breakfast with the kids. I decided to take it for a spin and snapped this picture to test it out. The app worked as advertised and within a few minutes of uploading the image, I got the results back….
And I never opened the app again.
Here was an app that I was so vocal about wanting, nay, needing. Yet when I actually had exactly what I’d been wishing for, I found it didn’t do much for me.
What turned this seemingly great app into one that he just used once? “Turns out I’d developed a good enough sense for calorie counting that my estimates were just as accurate if not more so than the magical app of my dreams.”
This is obviously a one-off, anecdotal case, but it reinforces the caution we have to use when listening to customer input. Things that seem intuitively right and useful at first can turn out to be just the opposite, as Bryce’s example shows.
This is why I’m sanguine about Clayton Christensen’s “jobs to be done” approach — fundamentally it’s a good concept, but it doesn’t get close to the level of predictability that is sometimes ascribed to it. There are many nuances about how to interpret a given a job, and whether you can actually build a business off a job even if there appears to be demand for it (there are lots of jobs to be done that don’t get addressed for financial reasons — just ask anyone suffering from an obscure disease that won’t get a hit drug like Lipator).
With present day research and analysis methods, there remains an irreducable amount of uncertainty about new product success, even when customers have told us loud and clear what they want.