- In human-centered design, the team continually
iterates through cycles of making
and gathering information.
But without synthesizing that information,
data often serves as more distraction than direction.
Adopting a practice of generating
and refining your hypotheses about the user
and their needs helps the team generate insights
and put them into action.
A hypothesis combines a human need
with a motivation for that need,
so that it can be proven or disproven.
This pairing provides a strong base for engagement
with people to prove or refine the hypotheses,
and also a strong base for concept generation.
For example, a hypothesis that people don't like
hailing cabs because of the uncertainty
of quickly finding a cab could lead you
to a range of ideas for improving the experience
of getting a ride,
including a service like Lyft or Uber.
Generating a hypothesis about people's needs
is a great activity to do with your team,
and it doesn't have to take long.
It can be time boxed into even 30-minute sprints.
The first step is to review any existing data
you have access to about the potential users
of your product or service
and to write down the assumptions
about what unmet needs may exist.
Get all of your assumptions on the table.
And literally, use a large whiteboard
or a big piece of paper,
divide it into three columns,
and list the assumptions under the first column
of Unmet Needs.
For example, one of our assumptions
when we were working with rural farmers
in the developing world was
that farmers don't have access
to accurate pricing information
and so struggled to negotiate their prices well.
In the second column,
you'd label that Reasons Why,
we thought about why those unmet needs exist.
For example, farmers don't have access
to accurate prices because radio information
is not for specific to their village,
it's really talking about the whole country.
And it's all right to have multiple reasons why
for one unmet need.
At this point, it's often tempting
to jump right to concept generation.
This example is from a project we were doing
focused on mobile solutions for low income farmers
in emerging markets.
It'd be really tempting at this point
to jump right to an idea, like,
let's deliver locally accurate pricing information
to the farmers' mobile.
But that kind of thinking tends to get you
to the expected or the me, too solution.
In human-centered design, the next step
is to use these hypotheses
to plan your in-person research.
So here we use the third column
to list questions which will help us
test the hypotheses with the people we're talking to.
So we might ask questions like,
"Do you share your prices with anyone?"
"How do you use pricing data?"
"How do you get pricing data today?"
and other questions like that.
Then you're going to go into the field,
and when you come back from the field,
you've wrapped up your research.
And the research might have been
a quick session in your office
or several days spent meeting people
in their homes or their farms.
Could even be by making a prototype
to test an idea.
You'll now have a wealth of new information.
And the next stage is to take another 30 minutes or so
to refine the hypotheses.
Look back at the initial hypotheses
and write each of them down
on a separate sheet of paper.
Have the team review their insights from research
and look for those that confirm and those
that challenge your hypotheses.
If the insights from research have highlighted
a new understanding, revise the hypothesis
to reflect what you learned.
For example, from an initial hypothesis
that farmers don't have access to the latest market prices,
which results in diminished bargaining power
and income, the revised hypothesis found
that farmers do actually have access
to accurate pricing information,
on the TV, the radio, and from other farmers
returning from market,
but what they really lack is negotiating power
because they're too far from market
and the quantity of their crops
isn't enough to negotiate a better price.
With this understanding, a team is far more likely
to design a service that will be meaningful
for the people they hope will use it.
Perhaps by enabling small farmers
to find others with similar crops,
they can gather sufficient quantity
to negotiate for better prices.
Often the assumptions we make
about why people use products
and what they need and want aren't correct.
And they may reflect how our organization
sees the problem more than how those people
actually experience it.
Plus, understanding unmet needs and motivations
helps fuel the human empathy,
which effective design thinking relies on.