Most JTBD research stops once you’ve gathered rich interview data. The real magic happens when you turn 17 different stories into a shared language that reveals why people buy. This post shows that step – live and unfiltered.
Where we are in the research now:
At this stage of the live case study we have achieved the following:
- We have interviewed and analysed the Four forces of Progress for 17 different interviewees
- We have a deep understanding of what each person was trying to accomplish by taking a course, and;
- We know the context they were in when they made the decision to spend their money
So what do we do with all of this data? How can we take 17 unique stories and interpret them in a way that lets us form Jobs-to-Be-Done?
We now have 17 stories describing people’s struggles and choices. The next task is to find the patterns – the shared forces that explain why they switched.
That is the subject of this post – watch the video below for a quick overview:
I think remember when I told you that one of my fears was that I feel is 12 [interviews] really enough and … I don’t feel that fear anymore … because there’s so much data to work with and we still haven’t taken all of the people into the canvas.
JTBD Switch Interview Data Preparation
In Jobs-to-Be-Done language, Pushes are the frustrations or changes that make the old way untenable.
- Commonly phrased “When I…{struggle with something new}”
Pulls are the attractions of a new way.
- Commonly phrased “So I can… {achieve some new goal}”
Our goal is to turn dozens of individual pushes and pulls into a concise set of shared motivations.
Before we can analyse the and create jobs, we need to get the right “common language” for the motivations. The objective is to translate this from individual stories into shared motives:
- We use “card sorting” as a technique to drag similar-sounding statements into groups
- Once we have a “pile” of similar sounding Push or Pull statements, we try to give the group a name – following the same “When I …”/”So I can…” structure of the Four Forces
- The shared name should encapsulate a shared motivation for every interviewee that is in this group
Here’s an example:
First focus on the three brightly coloured tiles:
- These are the individual “Push” statements from 3 different interviewees
- Kiran’s relates to the fact he used to pay a graphic designer to help him create designs for work when he lived in India.
- However since moving to the UK this is harder, and he cannot get the same level of collaboration, which causes him to want to learn how to do this work himself
- Niky’s statement is slightly different. She used to run a charity where she had help, tools and support to create images, but…
- Now she’s launching her own business she is on her own and needs to learn the skills herself.
- Wes’ story is related to the work he does – previously he would outsource design work to an ex-colleague, but…
- Once the design work becomes a core part of his business, he feels the need to learn the skills himself (to make more profit)
- Kiran’s relates to the fact he used to pay a graphic designer to help him create designs for work when he lived in India.
Though each story is personal, all three share the same underlying motive:
- I used to have someone else to do this graphic design work, but now I need to learn how to do it myself
The important thing is what is NOT in the group
Look again at the group above. What is NOT there?
If you said “The other 14 interviewees” then you win the prize!
Why is this important?
- We need a way to analyse the different stories so we can see what motivates people to buy
- When we have 17 interviewee transcripts alone, we cannot see any patterns
- If we start to group people based on common motivations and separate those who were motivated to buy for this reason from those who were not, we can form our “Jobs-to-Be-Done”.
Grouping lets us move from anecdotes to analysis. Once every interviewee is coded against each motivation, we can see who shares which drivers – and that’s what allows clustering.
Each coloured block above represents a shared motive. The aim is not to simplify people’s stories, but to make comparison possible.
The image above shows more of these groups from the exercise I ran with Janis (the full exercise is documented in three videos below – but given the total run time is around 7 hours, I’m sure most people won’t watch it all).
- The colour coding of interviewees lets you quickly see which interviewees resemble one another
- For example, Green, Slate Gray, Orange and Pink appear together in at least 2 of the visible groups
- We repeat this across the Pushes and the Pulls
- The goal is to find common MOTIVATING forces and create a shared description
- The description needs to be detailed enough that we can explicitly say “These interviewees were motivated by this force” AND “The other interviewees were NOT motivated by this force”.
- We do not group the Anxieties or Habits for this analysis
- We are only looking for common motivations to buy at this stage
- We will use the anxieties and habits to understand how to improve sales ONCE we see them in the context of a Job-to-Be-Done
I come from a place where for three years I was just absolutely winging it… and this is just such a relief to see something like this instead of being like a chicken without a head running around.
The end state: Full Group Descriptions and Coding for all interviewees
We know we’ve completed this phase when we can:
- Describe common motivation statements for all of the main motivating forces
- Clearly separate interviewees based on their common motivations, e.g.:
- When I have a career change and feel this skill could be useful in whatever comes next (Cathy, Niky, Edoardo, Naif), vs;
- When I need to create visuals for my work but lack the skills or experience and worry about my ability to do this (Kiran, Wes, Mitul)
- The end state is a fully coded board with statements and labels like the image below:
What I love the most is that just the non-bullshitty thing… how tied to reality this is — those are real conversations, those are real stories.
Want to watch the full process, end-to-end?
For transparency we have published the full process: it is messy, time-consuming, and iterative.
If you want to watch along, listen to Janis’ questions and see if you agree or disagree with the approach, please open the accordion below and get stuck in.
In the next stage, we’ll use the coded data to generate clusters and define the core Jobs-to-Be-Done – the repeatable motives that drive people to take a course like Janis’s.
Yeah, it’s interesting. You have this… I feel like when we did that wall of sticky notes and clusters… things start to map out.