One of the most common problems, and possibly the toughest one to overcome, is the tendency to accept solutions from users without understanding the underlying problem. In other words, a user says, “I want x feature,” and instead of learning why they want that feature, new researchers tend to write down, “users want x feature," and then move on.
This is a huge issue with novices performing research, When you do this, you are letting your users design your product for you, and this is bad because, in general, users are terrible at design.
Ooh! An Example!
I participated in some user research for a company with an expensive set of products and services. Users coming to the company’s website were looking for information so they could properly evaluate which set of products and services was right for them. Typically, users ended up buying a custom package of products and services.
One thing we heard from several users was that they really wanted more case studies. Case studies, they said, were extremely helpful.
Now, if you’re conducting user research, and a customer tells you that he wants case studies, this might sound like a great idea.
Unfortunately, the user has just presented you with a solution, not a problem. The reason that this is important is that, based on what the actual underlying problem is, there might be several better solutions available to you.
When we followed up on users’ requests for case studies with the question, “Why do you want to see case studies?” we got a variety of answers. Interestingly, the users asking for case studies were all trying to solve entirely different problems. But were case studies really the best solution for all three problems?
These were the responses along with some analysis.
“I want to know what other companies similar to mine are doing so that I have a good idea of what I should buy.”
The first user’s “problem” was that he didn’t know how to pick the optimal collection of products for his company. This is a choice problem. It’s like when you’re trying to buy a new home theater system, and you have to make a bunch of interrelated decisions about very expensive items that you probably don’t know much about.
While case studies can certainly be helpful in these instances, it’s often more effective to solve choice problems with some sort of recommendation engine or a selection of pre-set packages.
Both of these more quickly help the user understand what the right selection is for him rather than just give him a long explanation of how somebody else found a good solution that might or might not be applicable to the user.
“I want to know what sorts of benefits other companies got from the purchase so I can tell whether it’s worth buying.”
The second user’s “problem” was that he wanted to make sure that he was getting a good value for his money. This is a metrics problem. It’s like when you’re trying to figure out if it’s worth it to buy the more expensive stereo system. You need to understand exactly what you’re getting for your money with each system and then balance the benefits vs the cost.
This problem might have been solved by a price matrix showing exactly what benefits were offered for different products. Alternatively, it would be faster and more effective to display only the pertinent part of the case studies on the product description page - for example, “Customers saw an average of 35% increase in revenue 6 months after installing this product.”
By boiling this down to only the parts of the case study that were actually important to the user, it gives you more flexibility to show this information - statistics, metrics, etc. - in more prominent and pertinent places on the site. This actually increases the impact of these numbers and improves the chance that people will see them.
“I want to see what other sorts of companies you work with so that I can decide whether you have a reputable company.”
The third user’s “problem” was that he hadn’t ever heard of the company selling the products. Since they were expensive products, he wanted the reassurance that companies he had heard of were already clients. This is a social proof problem. It’s like when you’re trying to pick somebody to put a new roof on your house, so you ask your friends for recommendations.
His actual problem could have been solved a lot quicker with a carousel of short client testimonials. Why go to all the trouble of writing up several big case studies when all the user cares about is seeing a Google logo in your client list?
Why This MattersThis shouldn’t come as a surprise to any of you, but users ask for things they’re familiar with, not necessarily what would be best for them. If a user has seen something like case studies before, when he thinks about the value he got from case studies, he’s going to ask for more of the same. He’s not necessarily going to just ask for the part of the case study that was most pertinent to him.
The problem with this is that many people who might also find certain parts of case studies compelling won’t bother to read them because case studies can be quite long or because the user doesn’t think that the particular case study applies to him.
Obviously, this is applicable to a lot more than case studies. For example, I recently saw a very similar situation from buyers and sellers in a social marketplace asking for a “reputation system” when what they really wanted was some sort of reassurance that they wouldn’t get ripped off. I could name a dozen other examples.
The takeaway is that, when somebody asks you for a feature, you need to follow up with questions about why they want the feature, even when you think you already know the answer!
Once you know what their problems really are, you can go about solving them in the most efficient, effective way, rather than the way the user just happened to think of in the study.
Instead of just building what the user asks for, build something that solves the user’s real problem. As an added bonus, you might end up building a smaller, easier feature than the one the user asked for.