TL;DR Extract evidence-based solutions out of data-driven decisions
This is a proposal on how to conduct interview questions on the fly for the best possible collection of qualitative data to be considered as "ready for design thinking".
When you ask someone for feedback, they will lie to your face. The easy fix is to capture concrete Stories. In concrete stories, they will tell you about their actual behavior.
We begin with:
"Tell me about problem
x."
At this point x ****can be anything. It can be a rant, it can be an anecdote, it can be a dealbreaker, it can be positive, it can be negative, it can be null. The overwhelming objective for every incoming data-driven x is to extract the evidence into a narrative worth solving.
In order to do this, we will apply a little pseudo algebra.
Problem x |
Desired Outcome y |
Triggering Event z |
|---|
We must investigate what the respondent actually wants. This is modeled as Desired Outcome y*.*
y is the goal the respondent wants to reach.
This is what we will consider ideating into Design Thinking for a value proposition.
Reciprocally*, Triggering Events* z create a desire for the better.
zs are important because they allow the respondent to focus on "when" while narrating the "what" without the "who".
zs plant the seed in the respondents to engage based on bad experience, a change in circumstance, or finding awareness.
zs give us actionable Stories.