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Coding
Jobs to Be Done (JTBD)
Try itA structured interview and analysis process that uncovers the actual job customers hire your product to do — and who they're really competing against.
What it does
A framework for understanding that customers "hire" products to make progress in specific circumstances, not simply acquire features. The skill guides users through Switch Interviews — structured conversations with recent switchers — to reconstruct the moment of change: what pushed them away, what pulled them in, what anxiety almost stopped them, and what habits had to shift. The output is a job statement in When/I want to/So I can format, a competitor set that includes adjacent and non-obvious alternatives (not just same-category products), and an analysis of functional, emotional, and social dimensions. This surfaces why customers don't switch even when your product is technically superio…
When to use it
- Customers aren't switching despite a better product
- Churn is high but exit surveys don't identify who wins the job
- Product team is debating feature priorities instead of what customers need
- Building an AI product and need to understand what users hire it for vs. what it outputs
The skill document
Jobs to Be Done (JTBD)
Overview
People don't buy products — they hire products to do a job (make progress in a specific circumstance, across functional, emotional, and social dimensions). Customers switch when a new hire does the job better; they churn when your product stops serving the job. Developed by Christensen, Moesta, and Taddy Hall; codified in Competing Against Luck (2016). Rooted in Levitt's 1960 insight: "People don't want a quarter-inch drill, they want a quarter-inch hole."
Composes with pmf-crossing-the-chasm, mvp, switching-costs, first-principles.
When to Use
- Product is technically excellent but customers don't switch from incumbents
- Demographic segmentation produces segments that don't behave alike
- Churn is high but exit surveys don't predict it; roadmap debate is feature-vs-feature
- New market entry: "who is our customer" instead of "what job"
- Building an AI-native product or "AI wrapper": are users hiring us for output/features, or for progress (get unblocked, ship faster) — and are we losing to AI adoption, the base model's own app, or non-consumption?
Not when: commodity; regulatory-compliance purchase; org buyer with different motivations than end-user.
Coaching Novices (Adaptive Front Door)
- Engine mode: concrete product/customer case → run The Process directly.
- Coach mode: unfamiliar or no concrete case → guide step by step.
In Coach mode, respond one step at a time. Each [WAIT] is a hard stop — output only that step's question, then stop.
- One-line: people hire products to do a job — the competitor set is everything the buyer considered, not just your category.
- Check fit: commodity / regulatory-buy / no-choice → not this lens.
- Elicit the real product and customer behavior they're trying to understand.
[WAIT — do not advance until user responds]
- One question at a time: what job is the customer hiring this for? what circumstance? what did they hire before?
[WAIT — do not advance until user responds]
- Close: job statement + the non-obvious competitor they're actually choosing between.
[WAIT — do not advance until user responds]
The Process
Step 1 — State product + assumed customer (starting point; will be dismantled).
Step 2 — Switch Interviews. Interview recent switchers to/from your product. Reconstruct the switching moment: (1) First thought — when did you first realize you needed something different? (2) Circumstance — what was going wrong? (3) What else did you consider? (4) Push — what was actively wrong with the old? (5) Pull — what attracted the new? (6) Anxiety — what almost stopped you? (7) Habit — what behavior had to change? (8) First use — how did you feel?
Step 3 — Extract job statement: When [circumstance], I want to [motivation], so I can [outcome].
Step 4 — Identify actual competitor set: Direct (same category) / Adjacent (different category, same job) / Non-consumption (do nothing) / Surprising non-obvious.
Step 5 — Map all three dimensions: Functional (practical task) / Emotional (how they want to feel) / Social (how they want to be seen).
Step 6 — Diagnose churn or wins: Churn: what job? what did they hire instead? what did the new hire do better? Wins: what did they fire? what became unbearable? what anxiety was overcome?
Step 7 — Design from the job. Every feature: does it help progress in the specific circumstance? does it serve functional/emotional/social dimensions? does it reduce Push/Pull/Anxiety/Habit barriers?
Output Template
JTBD Analysis:
Job statement: When [circumstance], I want to [motivation], so I can [outcome].
Competitor set: Direct / Adjacent / Non-consumption / Surprising
Dimensions: Functional / Emotional / Social
Forces of progress: Push / Pull / Anxiety / Habit
Implications: Features to build / cut / Marketing angle / Competitive set to track
→ Method in Action: Christensen and the Milkshake Study, 2003
→ 2026 lens: What People Hire an AI Assistant to Do (2023–2026)
Pack: Common JTBD Patterns
| Domain | Job shape | Non-obvious competitor |
|---|---|---|
| Productivity SaaS | "Under deadline, make artifact look credible to boss" | Boss not asking; meeting cancelled |
| Consumer food | "Tired after work, feed kids without feeling like failure" | Ordering delivery; cereal |
| Banking/fintech | "Worried about money, feel like I have a plan" | Calling a parent; not checking balance |
| Dating apps | "Lonely Tuesday night, feel like there are possibilities" | Re-watching a show; texting an ex |
Applying It Well
- Customers articulate the job reliably; they cannot reliably predict which features serve it. Ask "what were you trying to accomplish when you switched?" not "what should we build?"
- The most dangerous competitor is usually outside your category — a different way of doing the job, or non-consumption.
- Most products serve 3-7 distinct jobs. Discovering the second and third explains cohort behavior differences.
→ Primary sources: references/sources.md
Common Rationalizations
[D] = designed upfront | [O] = observed in real use. [O] entries are more valuable.
| Fake move | Reality |
|---|---|
| [D] "We surveyed customers and they want X feature" | Customers articulate jobs, not features. Re-interview using Switch methodology. |
| [D] "Our customer is millennials / mid-market companies" | Demographic categories are not jobs. Same person has 5 different jobs across her day. |
| [D] "We don't have competitors" | Every job has alternatives, including non-consumption. Can't name the competitor = don't understand the job. |
| [D] "JTBD is just user-needs research" | User-needs lists features; JTBD reconstructs the switching moment. Different output. |
| [D] Treating the job as functional only | Emotional and social dimensions are where premium pricing and brand loyalty live. |
| [D] Skipping Switch Interviews because "we already know" | If you can't name Push/Pull/Anxiety/Habit for 10 recent switchers, you don't already know. |
| [D] Treating churn as "they lost interest" | Customers fire your product because something else does the job better. Identify the new hire. |
| → Add [O] entries here after each real use — paste the actual failure pattern | What went wrong and why |
Red Flags
- Segmentation is purely demographic; competitor set lists only same-category products
- Roadmap features justified by "customers asked" without job context
- Churn analysis stops at "less engaged" instead of identifying the new hire
- Job statements without a circumstance; functional dimension only; no Switch Interview ever run
Verification
- 5-10 Switch Interviews conducted (not feature surveys)
- Job statement: When/I want to/So I can with explicit circumstance
- Functional, emotional, social dimensions named
- Competitor set includes adjacent, non-consumption, and surprising alternatives
- Push, Pull, Anxiety, Habit forces identified
- Product implications derived from the job; primary job chosen if multiple exist
Part of deciqAI Knowledge Skills — 227 open-source thinking skills that make rigor executable for AI agents. The same skills power every deciqAI agent, which runs them autonomously to operate your company. See it run → https://www.deciqai.com/c/jobs-to-be-done · ⭐ Star the repo → https://github.com/deciqAI/knowledge-skills · Contributions welcome.
Agents: latest version & machine-readable metadata → https://www.deciqai.com/s/jobs-to-be-done.json
Questions people ask
- How is this different from standard user research or feature surveys?
- Standard research asks what features customers want. The Switch Interview reconstructs the actual switching moment — what pushed them away, what pulled them in, what anxiety almost stopped them. This surfaces the job and the true competitor set that surveys miss.
- Does this work for B2B products with multiple decision-makers?
- Yes, but it requires identifying whose job matters most. The skill notes that an org buyer can have different motivations than the end-user. Interview the person who made or influenced the switching decision, and distinguish their job from the end-user's job.
- When should I NOT use this framework?
- Avoid when the product is a commodity with no job-level differentiation (electricity, raw materials), when purchase is driven entirely by regulatory/legal compliance, or when customers have no real choice. These situations lack the competitive dynamics that JTBD analysis requires.
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