Killing Assumptions Through User Research

Why do user research?

This text assumes that you, dear reader, are somehow involved in product creation. It could be tangible goods or digital products; it could be for profit or not. Whatever it is, you're in the business of serving people by creating something which helps them solve a problem of some sort.

Why would you engage in user research? Well, a number of possible reasons come to mind:

  • You have an existing product and you want to know how the users of this product feel about it (or part of it). Maybe you have been losing customers to the competition, or maybe your business is just not growing the way you think it should be.
  • Or you have an idea for a product, and want to test the waters; to see if anyone would even want to use such a thing as you are imagining. In other words, you want to hear someone else’s opinion, before you invest time and money going further.
  • Maybe you simply have experience or knowledge you want to put to use for a larger group of people, but you're unsure what they want?

Whatever it is, you have an interest of some sort, and it matters to you to hear the opinion of others on that interest. What can you do?

  1. You could observe people in the field
  2. You could interview people
  3. You could track and measure behavior on the internet or elsewhere (a form of observation)
  4. You could create a survey and get people to answer questions about your interest
  5. You could run workshops, combining observations, interviews and personal interaction

All of these are valid ways of discovering stuff about a group of people, and combining them is often a good idea to get a more nuanced understanding. Indeed, reading between the lines is key in user research, whether it means making notes of what you see or sense during an interview, a workshop, etc.

My definition of user-research:

This article is not about:

  • usability testing (I would put this under UX-research)
  • co-design
  • behavioral tracking

Let me explain:

User research, as understood here, is about understanding the needs and motivations of people who are, or might become, your users or customers.

  • User research is what you do before you create a product, or make changes to a product.
  • It is not about usability testing, because you have no prototypes to test yet.
  • It might be based on data from behavioral tracking, as a way to understand the motivations behind the behavior.
  • It could lead to Co-design or participatory design processes.

A little note on user-research for digital products:

Some will say that data gathered in analytics-software is also user research. I beg to differ. When you track clicks, scrolls and session-lengths in apps and on websites, you are really gathering the occurrances of digital events. Sure, most of those events are probably triggered by human users, but which ones? Software is just as capable of browsing a website as a human is. Even when they are triggered by humans, can you be sure of the intentions behind those events?

Ever got a cell-phone call from someones back-pocket?

Ever clicked the wrong button on a website?

My point isn't that all your website visitors are bots, or that none of your users know what they're doing with your product. My point is, that digital events are not always signs of a human user, and that the intentions of a human using your product are not always obvious judging by the events triggered.

Example: Cart abandonment

Behavioral data will sometimes show you user-behavior which doesn't make sense. Take the problem of cart abandonment; a common issue in e-commerce: Data shows visitors coming to a site, browsing categories and adding products to the shopping cart. All seems to be going according to plan, then the data trail ends with the visitor leaving the site before checking out. No payment. No order. Just an abandoned cart. For the sake of this example, we will assume cart abandonment is at 10%, meaning that 10% of shopping carts are never checked out. That is a serious problem.

Why does this happen? Let us say, that the software has been thoroughly tested: It works in several different web-browsers, across all major operating systems. There are no obvious reasons for visitors not to become customers, yet we can see that some don't.

We know this happens in physical stores too, because we see the signs as customers: Shopping carts or baskets full of goods, left in random locations around stores. If you could observe every customer abandoning their cart in a physical store, you might be able to guess at the reasons: Someone discovered they left their wallet at home; someone got an important phone call and had to rush out; someone couldn't find everything on their shopping list and gave up half way through.

Observing customers running into the kinds of problems and challenges human beings do, you will find plenty of perfectly good reasons for cart abandonment. In a digital store, this is much harder; precisely because you can't observe humans being humans – only digital events indicating what might be human activity.

There is data-science, and there is user-research. The data shows us that something is happening, but leaves us guessing as to why. User-research, when done right, uncovers the why.

Assumptions – Use them, then lose them

It is easy to start with an idea.

This is true, whether you are just thinking about starting a company or you are looking at behavioral data for an existing product. You have an idea, and think: “I can’t be the only one with this need”. You see something happening in the world, and you interpret it in a specific way, based on your previous experience in life.

Take the example of cart abandonment I mentioned before: With nothing but digital data to go by, you are left guessing the human intentions behind the event. If you’re a visual designer, you might guess that there is something visually wrong with the checkout flow on the website. If you’re a software developer you might develop a theory that, perhaps, the sluggish server response times you have observed in relation to the shopping cart are the reason for cart abandonment.

You’re probably a smart person, but you are not the user. You are a part of an organization serving users, which makes you partial to business needs rather than user needs. You are a person who wants this idea or interpretation to be right and true, because it is convenient to you. It fits in well with who you are, your skills, goals and your role in the organization, and if it works, you’ll be a success. This is an example of the phenomenon called bias. You could also call it subjectivity.

Another cause for assumptions is common sense knowledge. It just makes sense that things are as you think they are; your friends and collegues - even your boss - agrees with you! But they’re biased too, of course. If you, your collegues or your boss can’t back up your ideas of reality with anything better than “common sense”, then all you have are assumptions. Those will have to go.

Is it bad to start with an idea?

Yes and no. Ideas are often what sets us off in a new direction – call it inspiration. You may have an idea for the perfect product to solve someones’ problem – and that’s ok, if it makes you curious and sparks your imagination. But don’t make the common mistake of assuming other people think exactly like you do, and that their problems are exactly as you imagine them to be.

If your idea has opened your eyes to an issue someone has, then great: Set aside your idea and spend some time studying that issue. You might discover a real problem in need of a product to help solve it. However, your studies may reveal a problem your initial idea cannot solve, but because you did the research, you can now come up with an idea that’s better suited to the issue at hand.

If, however, you went ahead and designed a product around your initial idea, you risk ending up with a product which is a bad fit for solving the actual problem, or one that attempts to solve a problem which simply does not exist.

When I say “real problem”, I mean something that really bugs an amount of people large enough to be profitable (assuming you want to at least earn back your investment) or to seriously alleviate some issue for people you really want to help.

If you try ignoring your current idea at the moment and think about why you think it could help someone, then you are thinking about your assumptions.

Your current idea may, through sheer luck, be awesome, but as long as you haven’t proven the need for it through research, then you’re just assuming it’s awesome:

  • You’re assuming certain people have a certain need, which can be solved with an app, but you don’t know.
  • You’re assuming a certain functionality of this app will do the trick, but you don’t know.
  • You may even be assuming this need is serious enough for people to bother solving it, but you don’t know.

Of course, you may have something in mind which already exists in some form, but you feel you can improve it. Then at least you know there is a market, but to get a share of it you will need to solve some problems people are having with existing solutions.

Again, you won’t know what those problems are unless you ask. Until then, you’re just assuming. Becoming aware of your assumptions is important, because it lets you realize that you need to start asking questions of your existing or future users.

How do you reach this state of awareness, then?

Easy: Always assume you have assumptions to become aware off!