Catalog
method#Product#Delivery#Governance#Software Engineering

Jobs to be Done (JTBD)

JTBD is a customer-centered method that aligns product decisions around the concrete tasks and desired outcomes customers try to accomplish.

Jobs to be Done (JTBD) is a structured product method for identifying the concrete tasks customers hire a product to accomplish.
Established
Medium

Classification

  • Medium
  • Business
  • Design
  • Intermediate

Technical context

Miro or MURAL for workshops and mappingJira/Linear/Shortcut for implementation and trackingUser research tools (Lookback, Dovetail) for recording and analysis

Principles & goals

Focus on jobs and desired outcomes, not on demographic segments.Situations and context determine job relevance and solution success.Measurable hypotheses and experiments instead of pure ideation.
Discovery
Domain, Team

Use cases & scenarios

Compromises

  • Confirmation bias in selecting interviewed users.
  • Incorrect metric derivation that doesn't reflect customer behavior.
  • Excessive fragmentation of the product roadmap due to overly fine jobs.
  • Ask about concrete situations and expected results.
  • Define outcome metrics before implementation.
  • Involve cross-functional teams in synthesis and prioritization.

I/O & resources

  • Interview guide with situational questions
  • Access to real users or observational data
  • Business goals and success criteria
  • Catalog of prioritized jobs with outcomes
  • Hypotheses for experiments and metrics
  • Aligned recommendations for roadmap and MVPs

Description

Jobs to be Done (JTBD) is a structured product method for identifying the concrete tasks customers hire a product to accomplish. It shifts focus from demographic segments to situational user needs and supports prioritization, idea validation, and outcome-driven roadmaps.

✔

  • Clearer customer orientation through outcome focus.
  • Better prioritization by real customer impact.
  • More robust hypotheses for experiments and validation.

✖

  • Resource intensive: requires high-quality interviews and analysis.
  • Insights are context-dependent and not always immediately scalable.
  • Risk of overspecifying jobs instead of exploring solution space.

  • Validated jobs

    Number of customer jobs validated through interviews/tests.

  • Outcome fulfillment rate

    Percentage of users achieving the desired outcome.

  • Experiment success rate

    Share of experiments that confirm hypotheses about jobs.

Redesign of a checkout flow

The team identified the job 'pay quickly and securely' and reduced friction through outcome tests.

New search feature for mobile users

JTBD interviews revealed users' job to 'find information quickly on the go'; the solution focused on time-to-result rather than click counts.

Roadmap prioritization

Product decisions were prioritized by expected outcome impact on defined customer jobs.

1

Prepare: define goals, align stakeholders and recruit participants.

2

Conduct: perform situational interviews and observations.

3

Synthesize: cluster jobs, desired outcomes and pain points.

4

Validate: define experiments and measure outcomes.

⚠️ Technical debt & bottlenecks

  • Missing documentation and traceability of interview findings.
  • No standardized templates for outcome definitions.
  • Lack of integrated measurement instruments in product analytics.
Interview capacity and recruitmentAnalytical synthesis capabilityStakeholder-aligned prioritization
  • Collecting superficial user quotes without context analysis.
  • Documenting jobs as a feature list without outcome measurement.
  • Using JTBD as a marketing label without methodological application.
  • Confusing jobs with solutions or features.
  • Overgeneralizing from a few interviews too quickly.
  • Choosing metrics that do not reflect customer behavior.
Qualitative interviewing and moderationSynthesis and pattern mappingOutcome definition and metric derivation
Customer understanding and contextual dataMeasurability of outcomesDecision-making capability of the product team
  • • Limited time for qualitative research
  • • Privacy and consent for user data
  • • Budget constraints for elaborate studies