Jobs to Be Done Interview Analysis
Every job statement arrives with the interviews behind it, the interviews that contradicted it, and the total, so nine of fourteen stops reading like one.
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Fourteen interviews go in and a one-page portrait comes out. The trait eleven people described and the trait one participant described at length are in the same box, in the same font, and nothing on the page records which was which. That is usually sold as clarity. It is the reason a persona cannot be argued with, and Nielsen Norman Group's own account of why personas fail leads with the failure it produces: personas that were created and then never used.
So every job statement in this pack carries three numbers: how many interviews described the job, how many described doing something else, and out of how many. Nothing gets deleted for being unpopular. A statement at 1 of 14 stays, labelled 1 of 14, which is all a roadmap conversation needs. Statements are written as jobs rather than as features on purpose, because the underlying job does not go away when the product that served it does.
Then it writes the anti-persona: who the product is explicitly not for, by segment, with the evidence, which is the only artifact in this space that can actually decline a request. Coding the transcripts first is research synthesis, unsolicited feedback out of a support queue is feedback triage, and the jobs that survive get ranked against everything else you owe in the prioritization pack. Or download the whole pack as Word and CSV files.
What is in the pack
Evidence Register
Every job statement with support, contradictions, the study total, target-segment support as its own column, transcript minutes, and the interview ids behind each count so any row can be checked against the transcript.
Interview Index
One row per session with the column research repositories omit: how the participant was recruited. Customer list, inbound demo, churned account, panel. It is the most predictive field in the study, because a mixed channel almost always means a mixed segment.
Statement Candidates
Every candidate drafted, including the rejections, with the test it failed and where the job underneath went. Half of these are the phrases a readout would otherwise have led with.
Segment Behaviour Comparison
Each statement checked against usage data by segment: job frequency per unit, reach of the feature that already serves it, time-to-outcome, and whether the segment even has the prerequisite the job assumes.
Job Statements and Persona Briefs
The write-ups. Statements carry their counts and their incumbent workaround. Briefs carry jobs in support order, populations, and an acceptance criterion instead of a personality. No names, no stock photos, no invented quotes.
Anti-persona Note
Who the product is not for, by segment, with the interviews, the account count, the behavioural separation, and the specific evidence that would reopen the question.
How it works
- 1
Send the transcripts
Dovetail, Otter, Descript, Lookback, Grain or plain text, one file per session, plus usage data by segment if you have it.
- 2
Draft wide, cut hard
Candidates get tested four ways: job or outcome, job or solution, falsifiable or not, and whether the phrasing came from the participant or the guide.
- 3
Count every statement
Supported by, contradicted by, out of, and target-segment support split out, with transcript minutes recorded next to prevalence.
- 4
Name the boundary
Two or three personas the evidence supports, and the anti-persona that gives the set the power to decline something.
Frequently asked questions
How is this different from a persona template?
A persona template gives you boxes to fill. Every attribute you write into one carries the same implied confidence, so the trait eleven people described and the trait one described render identically. This pack keeps the count against every statement, which is the single thing that turns a portrait into something a room can check.
What does the anti-persona actually do?
It lets the persona set decline a request. Saying the product is for independent restaurant groups sounds like a decision and functions as none, because every inbound request comes from somebody who runs restaurants. The anti-persona names the segment, the account count, what they use instead, and what evidence would reopen it.
Why record how long each job was discussed?
Because a synthesis session run from memory weights airtime, and airtime is close to uncorrelated with prevalence. In the worked example the two statements with the most minutes have three supporters each, while the job thirteen of fourteen people described ranks third. Showing both columns is what stops one articulate participant setting a roadmap.
What happens to a statement only two people described?
It stays, labelled 2 of 14, with the interviews named. Nothing is deleted for being unpopular, because a two-person statement can be an early signal or a segment finding, and the count is what lets you tell later. What it cannot do is arrive on a roadmap looking the same as a thirteen-person statement.
Do I need usage data as well as interviews?
The counts work without it and get much sharper with it. Behaviour is what separates a job people report from a job people perform, and prerequisite measures settle the most arguments: a job that assumes a central labour team cannot be a job in a segment that has no central team. Send whatever exists.
How many interviews is enough?
Enough that the denominator means something, which in practice is around a dozen per segment you want to make claims about. The register tells you where you are rather than asserting a threshold: it shows which interview each statement first appeared in, so you can see whether the last three added anything new. The continuous discovery pack turns that into a running rate.
Does it replace Dovetail or my research repository?
No, and it starts where they stop. A repository stores and tags sessions. What is missing between the tags and the readout is the counted register, which no repository builds because it requires reading every transcript for one statement at a time. Code the corpus first with research synthesis if you want the extracts too.
Find out which findings survive a count
Send the transcripts however they exported. The first number back is how many statements have unanimous support in your target segment, and it is usually fewer than four.
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