Jobs-to-be-done-statements (JTBD) refer to the idea that human beings behave differently according to the situation they are in and the goals they aim for in that situation. There is a debate as to whether humans actually aim for outcomes or progress in general. My understanding is that a JTBD-statement describes a human beings behaviour in a specific situation with a specific intent in mind. Thereby, humans use a tool to get a job done, not because they want to own the tool.
A famous example is the notion that homeowners do not buy quarter inch drills because they need drills or feel the urge to own them, but they buy them because drills are tools to do the job of drilling a hole in the wall — better than a spoon, for a example. Buying a drill is not the result of a psychological preference but part of a causal change in a homeowner’s life. There is a situation that requires a hole in the wall because the homewoner finally wants to hangup a picture. A screw can hold that picture and it demands a hole. A JTBD statement refers to this causal chain:
Causal chain: Drill → Hole → Screw → A Hanging Picture
Why is this relevant? The causal chain illustrates that new solutions can enter the homeowner’s life from different angles. An innovation team can design a new drill because a homeowner needs it. However a team can also follow the chain and reconsider how holes be put into walls without drilling them. In the end, the homeowner does not need a drill. Or a team reconsiders that screws are not necessarily perfect tools to hang pictures on the wall, it may be some kind of strong tape or glue or a different picture frame or a different wall.
The Job-to-be-Done perspective allows us to see qualitative data in the context of causality. Someone applies some kind of behaviour in order to achieve something in a given situation. That behaviour can be explained through context, not primarily through psychological preferences.
For the exercise I often apply Alan Klement’s version of a Job-to-be-Done Statement or a Job Story:
“When I am (situation), I (action), so that (outcome)”.
With this framework in their hands, team members first scan their nugget-framed data points and write them down as a JTBD-statement from a user’s perspective — the “I” in that statement refers to whoever said or did something, not the team. For example “When I am alone in my apartment (situation), I watch the latest Netflix specials (action), so that I feel entertained (outcome).”
Not all data points can be integrated into JTBD-statements for several reasons. Sometimes these data points neither describe a situation, nor a behaviour or an expected outcome. If a user simply said “I don’t like intransparency” and the team does not have a picture in my mind what that statement means in that user’s life, an interpretation can only be made by guessing. Usually at this point, teams realize that they missed a lot of valuable information from their interviews because they did not accurately asked for a better understanding of the scenes that their interviewees were refering to.
Usually two or three JTBD-statements in the exercise are enough to do the job. The active interpretation of qualitative data as Jobs-to-be-Done-statements demands an open discussion by all team members. Jobs to be Done already indicate fields of opportunities for new solutions, yet they still focus on single data points from individual interviews without putting them in a larger context.
An alternative version to a Job-to-be-Done-Statement can be a User Story that would also describe the causality of a person’s action in a given situation with a specific goal in mind. User Stories however are based on the idea that a certain type of user or a certain role has an impact on their behaviour:
“As a (role), I want to (action), so that (outcome)”
I am not dogmatic about this. Both frameworks work well in defining causality in user behaviors. Important is, that team members begin to actively connect the causal chain of a user’s behaviour or action in the context of a situation and an expected outcome. This can change the perception of data points dramatically. Seemingly uninteresting data points can now appear to be more relevant because they refer to a relevant situation, or a relevant tool, or a relevant outcome.