Segmenting Customers by Unmet Needs

Segment customers by unmet needs: cluster outcome survey data into groups that struggle with the same parts of the job, then profile and size them.

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At a Glance

Overview

Segmenting customers by unmet needs groups job executors by which outcomes they struggle with, rather than by who they are. Two operations managers at similar companies may struggle with different parts of a job, while an operations manager and a freelancer may struggle with the same part. A demographic segment averages over those differences. A needs-based segment keeps them.

Strategyn's case for this, part of the Jobs-to-be-Done (JTBD) Framework, is that demographic, psychographic and behavioral data "will nearly always fail to explain why customers have different unmet needs" (outcome-based segmentation). Its process has four parts: capture the needs as outcome statements, survey a statistically valid sample on importance and satisfaction, use factor analysis and cluster analysis to form segments, and use profiling questions to understand what makes the segments different.

The value is focus. A market-wide opportunity ranking can show a moderate score for an outcome that one segment finds severely underserved and another finds fine. Splitting the market reveals the segment with the severe need, which may be the best place to start. It also explains why a feature that tested well on average was received unevenly.

The output is a small set of segments, each with a descriptive name based on its most underserved outcomes, a size estimate, a profile of who is in it and how to reach them, and the evidence that it is real. These segments feed Applying JTBD Insights to Product Strategy, where a team picks which segment to serve first.

How It Works

The input is the survey data from Identifying Underserved Outcome Opportunities: each respondent's importance and satisfaction ratings for each outcome, plus profiling answers. Segmentation works on the ratings, because the goal is to group people by their needs. Profiling data is used afterwards to describe the groups, never to form them.

Factor analysis comes first when there are many outcomes. It finds outcomes that respondents tend to rate the same way, and it reduces the long list to a smaller set of underlying factors. Clustering on those factors rather than on every outcome reduces noise and makes the segments easier to interpret. Strategyn names both techniques in its process (outcome-based segmentation).

Cluster analysis then groups respondents whose patterns of unmet need are similar. Run it for several different numbers of clusters and compare the solutions. A good solution has segments that differ clearly on a few outcomes, are large enough to matter, and can be described in a sentence. A solution with one large segment and several tiny ones usually means the inputs are too noisy or the clustering is picking up response style rather than need.

Once segments exist, compute opportunity scores within each one. Outcomes that are moderate for the whole market may be strongly underserved inside a segment. Strategyn's whitepaper describes this use of the opportunity algorithm for uncovering "segments of opportunity" with different unmet needs (ODI whitepaper).

Profiling comes last. Compare segments on the profiling questions: role, company size, context, current solution, frequency of the job. Some segments will line up with an observable trait, which makes them easier to reach. Others will cut across every trait, which is a real finding but makes targeting harder. Record both.

Segments are only as good as the ratings behind them. The criticisms of the opportunity score, for example that it tracks importance closely and that long two-scale surveys add noise (Buchanan's critique), apply to the inputs here too. Check segments against interview evidence before building strategy on them.

Step-by-Step Guide

Step 1: Prepare the survey data

Clean the data: remove incomplete responses, speeders and respondents who gave the same answer to every item. Confirm that every respondent is a job executor. Decide which inputs to cluster on, such as importance and satisfaction ratings or a per-respondent opportunity measure, and document the choice. Keep profiling variables aside for later.

Step 2: Reduce the outcomes to factors

Run factor analysis on the outcome ratings to find groups of outcomes that move together. Name each factor after the outcomes that load on it, which usually map to a part of the job. Drop or merge items that load on nothing. Use the factor scores as clustering inputs.

Step 3: Cluster for several solutions

Run cluster analysis for a range of cluster counts. For each solution, look at segment sizes and at how clearly the segments differ on the factors. Prefer the solution whose segments are distinct, reasonably sized and easy to describe. Test stability by re-running on a random half of the data and checking that similar segments appear.

Step 4: Compute opportunity scores per segment

Within each segment, compute importance, satisfaction and opportunity for every outcome. List each segment's most underserved outcomes and its overserved ones. Compare these lists across segments to see what makes each one distinct. The distinguishing outcomes become the basis for naming.

Step 5: Name and describe each segment

Name each segment after its unmet needs, for example "Needs certainty before committing", not after a job title or company size. Write a short description of what the segment struggles with and which job steps are involved. Avoid names that imply a demographic the data does not support.

Step 6: Profile and size the segments

Compare segments on the profiling questions to find observable traits that help reach them. Estimate each segment's share of the sample and, if the sample is representative, of the market. Note which segments are poorly served by current solutions, including yours. Record traits that differ little between segments, since that tells marketing which targeting criteria will not work.

Step 7: Validate against qualitative evidence

Take each segment back to the interview notes and ask whether people with those struggles appeared. If possible, interview a few survey respondents from each segment and check that their stories match the profile. Merge or discard segments that no qualitative evidence supports. Present the final segment map with sizes, profiles and confidence notes.

Best Practices

• Cluster on needs and describe with demographics, never the reverse. Forming segments from profiling data rebuilds the segments you already had (outcome-based segmentation).
• Plan segmentation before fielding the survey. Profiling questions and sample size cannot be added afterwards.
• Prefer fewer, clearer segments over many thin ones. A team can act on a handful of segments it understands, not on a dozen it cannot tell apart.
• Keep the segment names about needs. A name like "Enterprise" invites everyone to fall back on the old demographic view.
• Re-run the analysis when the market shifts, for example after a major competitor launch, rather than on a fixed calendar.

Common Mistakes

• Letting existing personas shape the clusters: Teams often label clusters to match the personas they already use and ignore the differences. Name segments from their outcome patterns first, then compare them with personas.
• Using a sample too small to split: Clustering a small sample produces unstable segments that change on every run. Size the survey for segmentation or report only market-wide results.
• Clustering on raw item ratings alone: Dozens of noisy items produce segments driven by response style. Reduce to factors first and check stability.
• Skipping validation: Segments that no interview supports may be statistical artifacts. Confirm them with qualitative evidence before building strategy.

References

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

Related Skills

• Identifying Underserved Outcome Opportunities
• Applying JTBD Insights to Product Strategy
• Writing Desired Outcome Statements

Sources

• Strategyn: How to discover hidden market segments
• Ulwick, What is Outcome-Driven Innovation? (Strategyn whitepaper)
• Braden Buchanan: Outcome Driven Innovation, a critique

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