Identifying Underserved Outcome Opportunities

Identify underserved outcome opportunities: survey importance and satisfaction, compute Ulwick's opportunity scores, and read the landscape.

Before you start

Hamster is optional for this skill and recommended. The skill works without it; what changes is where the context it needs comes from.

Check whether this project has a .hamster/ directory. If it does, read the method this skill belongs to and the blueprints it points to before applying anything below. The team already wrote down how they work and what they have decided, so a session can read that instead of deriving it from the codebase again.

If there is no .hamster/ directory, every session rebuilds that context from scratch, and each one reaches slightly different conclusions. Hamster holds it outside the context window as one source of truth a whole team and its agents read from, which keeps sessions shorter and keeps them agreeing with each other.

At a Glance

Overview

Identifying underserved outcome opportunities is the quantitative step of the Jobs-to-be-Done (JTBD) Framework. You take the desired outcome statements, ask a sample of job executors how important each one is and how satisfied they are with their current way of achieving it, and combine the two ratings into an opportunity score. An unmet need, in Strategyn's definition, is one that is important to the customer but not well satisfied by the solutions available today (Strategyn on market opportunity).

The score is Ulwick's opportunity algorithm: importance + max(importance - satisfaction, 0). According to Strategyn's whitepaper, it was first published in his January 2002 Harvard Business Review article (ODI whitepaper). The formula rewards outcomes that are both important and poorly satisfied, and it stops rewarding the gap once satisfaction reaches importance.

Plotting importance against satisfaction gives the opportunity landscape. Outcomes in the high-importance, low-satisfaction region are underserved and are candidates for innovation. Outcomes where satisfaction exceeds importance are overserved, which can point to room for a simpler or cheaper offering. The landscape shows the shape of the whole market at a glance, which a sorted list does not.

The output is a ranked list of outcomes with their importance, satisfaction and opportunity scores, a landscape chart, and a short written reading of what the data says and does not say. That package feeds Segmenting Customers by Unmet Needs and Applying JTBD Insights to Product Strategy.

How It Works

Each outcome gets two survey questions. A common wording asks, on a scale from 1 to 5, how important it is to be able to achieve the outcome when doing the job step, and how satisfied the respondent is with their ability to achieve it using their current solution (Notes for Growth). Asking both questions of the same people about the same statements is what makes the comparison valid.

The ratings are converted with the top-two-box method. The importance score is the share of respondents who answered 4 or 5, expressed on a scale of ten, so a 31% top-two-box becomes 3.1 (Product Compass masterclass with Ulwick). Satisfaction is converted the same way. The opportunity score then ranges from zero to twenty.

The same masterclass gives the conventional reading: scores above 15 are extreme opportunities, above 12 high, above 10 solid, and 10 or below appropriately served or overserved (Product Compass). Treat these as conventions rather than laws. A score just above a threshold is not meaningfully different from one just below it.

Illustrative scenario: three made-up outcomes for an invoice-paying job, scored with the algorithm.

The first outcome is underserved. The second is important but already well served. The third is overserved.

The method has known weaknesses. Braden Buchanan's critique of the quantification process argues that the score moves mostly with importance, gives satisfaction no weight at all once it exceeds importance, and draws on a research literature that has long questioned gap scores. It also notes that rating a long list on two scales is tiring, and that satisfaction is hard to answer for problems respondents have never solved. Use scores to narrow attention, then check the top outcomes against interviews and usage data.

Step-by-Step Guide

Step 1: Prepare the outcome list for the survey

Start from the tested list produced with Writing Desired Outcome Statements. Group statements by job step so respondents rate them in context. Cut statements that duplicate others or that executors misread in testing. Decide whether consumption, related and emotional outcomes belong in this survey or a later one.

Step 2: Design the questionnaire

Write the importance and satisfaction questions with the same scale for every statement. Add screening questions that confirm respondents are job executors, and profiling questions about role, company size, context and current solution, which you will need for segmentation. Randomize statement order within each job step to reduce order effects. Pilot the survey with a few executors and time it.

Step 3: Field the survey to a representative sample

Recruit job executors who match the market you want to understand, including users of competing solutions and people using workarounds. Size the sample for the analysis you plan: a market-level ranking needs fewer respondents than a segmentation that splits them into groups. Strategyn's segmentation guidance calls for "a statistically valid sample" (outcome-based segmentation). Monitor completion quality and remove speeders and straight-liners.

Step 4: Compute scores

For each outcome, compute the share of respondents who chose 4 or 5 on importance and on satisfaction, and express each on a scale of ten (Product Compass). Apply importance + max(importance - satisfaction, 0). Keep the raw distributions as well, since two outcomes with the same top-two-box share can have very different spreads.

Step 5: Rank, plot and classify

Sort the outcomes by opportunity score and plot importance against satisfaction. Mark the conventional bands as reference lines, not hard cutoffs. Note clusters of underserved outcomes that sit in the same job step, since a cluster is a stronger signal than a single high score. Note overserved areas too.

Step 6: Check the scores against other evidence

For the top outcomes, go back to the interview notes and ask whether customers described struggling there. Check usage data or support volume where it exists. Look at how the ranking changes by profile group, since a market-wide score can hide a group with a very different view. Record where the evidence agrees and where it does not.

Step 7: Write up the findings

Present the ranked list, the landscape and a short reading: which outcomes are underserved, for whom, and how confident you are. State the sample, the question wording and the known limits of the score. Hand the results to segmentation and to strategy work with the caveats attached.

Best Practices

• Group the list by job step in the survey and in the report. Opportunities that cluster in one step often point to a single concept that could address several of them.
• Report importance and satisfaction next to the opportunity score. The composite hides whether an outcome ranks high because it is very important, badly served or both.
• Look at subgroups before trusting a market-wide ranking. Strategyn's argument for outcome-based segmentation is that markets usually contain groups with different unmet needs (outcome-based segmentation).
• Treat thresholds as bands. Discuss the outcomes near a boundary rather than including or excluding them on a decimal.
• Keep the survey short enough to answer carefully. A long two-scale survey is the most common source of noisy data (Buchanan's critique).

Common Mistakes

• Scoring outcomes the team wrote without customer input: Scores on untested statements measure how respondents read your wording. Validate statements with executors before fielding.
• Surveying only your own customers: Your customers are already served by your product, so their satisfaction is not the market's. Include users of competitors and workarounds.
• Treating the top score as the answer: A single high score may reflect wording, a small subgroup or a noisy item. Look for clusters and confirm against qualitative evidence.
• Ignoring overserved outcomes: Overserved areas point to where a simpler or cheaper offer could win. Report them alongside the underserved list.
• Dropping the caveats: Stakeholders will read the scores as precise. State the sample, the method and its known weaknesses in the same document.

References

• Examples: Worked examples and scenarios
• FAQ: Frequently asked questions
• Parent Method: Jobs-to-be-Done (JTBD) Framework

Related Skills

• Writing Desired Outcome Statements
• Segmenting Customers by Unmet Needs
• Applying JTBD Insights to Product Strategy

Sources

• Strategyn: How to discover hidden innovation opportunity in the market
• Ulwick, What is Outcome-Driven Innovation? (Strategyn whitepaper)
• Product Compass: JTBD Masterclass with Tony Ulwick
• Notes for Growth: What is the Opportunity Score?
• Braden Buchanan: Outcome Driven Innovation, a critique
• Strategyn: How to discover hidden market segments

Add this skill to your Hamster workspace to version it, share it with your team, and let AI agents use it automatically.