What needs to be finished first
A clearly formulated job to be done with named beneficiaries and context is available, onto which desired outcomes can be anchored.
What needs to be ready before start
Outcome statement template (“Minimize/Maximize [metric] at [job step]”); interview guide with outcome-mining questions; survey tool for quantitative scoring (for example Qualtrics, Typeform); statistics tool for opportunity score calculation; CSV export for segmentation.
A lead researcher with ODI experience (internal or external), two to four interviewers, a statistics analyst for evaluation, sponsor with innovation mandate, optional Sales or CS voice for recruitment.
JTBD and beneficiaries; market size and sample plan; recruiting channels; measurement scale (typically 5 or 10 point for importance and satisfaction); definition of done for study; innovation mandate (what happens with underserved outcomes).
Multiple weeks (outcome mining 1-2 weeks, survey 2-3 weeks, analysis 1 week)
Outcome-mining interview plan in place, survey tool configured, scoring sheet prepared for opportunity score formula (Importance + max(Importance - Satisfaction, 0)), and sample strategy with at least 100 respondents per segment.
The one question this method answers
Which desired outcomes are important and insufficiently fulfilled for which customer segments, and where is the largest quantitative innovation lever?
Marker: Phase
| Step | Duration | Action | Hint |
|---|---|---|---|
1Phase 1: Outcome mining | 1-2 weeks | Conduct 10-15 qualitative interviews with JTBD beneficiaries. Extract outcome statements in the format “Minimize/Maximize [metric] in [job step].” Target 50-150 outcome statements per job. | Outcome statements are not feature requests. Keep strict format. If a participant says "an app with X," translate it back to outcome language (“Minimize time between Step A and Step B”). |
2Phase 2: Outcome consolidation | 2-3 days | Cluster outcomes, consolidate duplicates, harmonize language. Final list 50-100 outcomes. For each outcome capture job-step link and potentially beneficiary type. | If fewer than 30 outcomes remain after consolidation, mining was too narrow. If more than 150, mining was too broad. Keep beneficiaries separate or outcomes are mixed. |
3Phase 3: Quantitative study | 2-3 weeks | Run survey across all outcomes. Two questions per outcome: importance and current satisfaction (each 1-10 or 1-5). Minimum sample 200+ from target segment, ideally multiple segments. | Survey fatigue is a risk (50+ outcomes × 2 questions). Randomize outcome order. Require minimum sample per segment for subgroup analysis. |
4Phase 4: Calculate opportunity score | 1-2 days | For each outcome: Mean(Importance) and Mean(Satisfaction). Opportunity Score = Importance + max(Importance - Satisfaction, 0). Sort outcomes by score. Thresholds: >15 = underserved (innovation lever), 10-15 = appropriately served, <10 = overserved. | If all top outcomes score <12, either the market is already well served or the sample is biased. Review sample profile and adjust segmentation if needed. |
5Phase 5: Segment by underserved patterns | 2-3 days | Cluster analysis (for example K-Means) on response patterns. Identify segments with distinct underserved-outcome profiles. Build top opportunities per segment and estimate segment size in market. | Demographic segmentation alone often misses the value. Outcome-pattern segmentation is the actual leverage. |
6Phase 6: Innovation hypotheses | 1-2 days | For each top segment and top outcome, define hypotheses for solutions or value messages. Build innovation backlog prioritized by opportunity score and segment size. Sponsor review. | Hypotheses are not solutions. Solutions are discovered in discovery sprints. ODI defines the direction, not the solution. |
What comes out at the end
Outcome statement library, opportunity score table, segmentation report with opportunity profiles per segment, top-opportunity list, innovation hypothesis backlog, sample and method documentation.
One study per market or target group as its own version with date. Outcome library grows and updates are tracked. Follow-up studies reference previous version for delta analysis (for example after market change).
Compact working template for Outcome-Driven Innovation with context, input, output artifacts, and next step.
Innovation method by Ulwick that captures customer wants as measurable desired outcomes.
When and for what do we use this method?
Which data, observations, decisions, or materials are available?
Short notes along the runsheet.
Owner, date, and success signal.
Concrete filled scenario, fictional example
Concrete filled scenario, fictional example
Outcome mining: 12 interviews, 87 outcome statements, 64 remained after consolidation.
Survey: n = 312 solo freelancers in DACH, annual revenue 100-500k EUR. Field period 14 days.
Top opportunities (score > 15):
Segmentation (3 clusters):
Innovation hypotheses (excerpt):
Next steps: start discovery sprints for segment A (38% of market, highest opportunity profile).
Recognize symptoms and steer against them
Statements say "App should book automatically" instead of "Minimize booking time".
Keep strict format: Minimize/Maximize + metric + job step. During interviews translate features back repeatedly. If someone says a feature, ask what desired outcome they want.
Survey has only 80 responses, cluster analysis unstable, opportunity scores high variance.
Minimum total sample 200+, at least 100 per segment. For smaller markets, run qualitative methods instead and treat findings as hypotheses, not a study verdict.
Outcomes sound generic for every market ("maximize satisfaction") and have no specificity.
Outcomes must be job-step specific. If one outcome is identical across three markets, it is too abstract. Split the job into more steps.
Segments are built by age, industry, region, but opportunity profiles are similar.
Run cluster analysis on answer patterns. Demographics are secondary. ODI value comes from pattern-based segmentation.
Top opportunity is "app with OCR function" instead of outcome statement.
Keep outcome and solution levels separate. ODI provides outcome goals, Discovery finds solutions. Fixing solutions too early forfeits method value.
Study identifies underserved outcomes clearly, but nobody has mandate or budget to build.
Clarify innovation mandate before start. If innovation capacity is missing, postpone study. Results without consequence demotivate team and customers.
Done signals checkable in under a minute
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