Key Principle
The Opportunity Algorithm ranks desired outcomes by their growth leverage using a single asymmetric formula (p. 109):
Opportunity score = importance + max(importance - satisfaction, 0)Importance and satisfaction are each measured as the percentage of respondents rating 4 or 5 on a 1-5 scale, then converted to a 0-10 range. The max(..., 0) term is the critical design choice: when satisfaction falls below importance, the gap is added to importance, double-weighting the signal. When satisfaction meets or exceeds importance, the term zeroes out and the score caps at the importance value alone (p. 109). This one-directional sensitivity amplifies unmet needs while suppressing noise from already-satisfied outcomes.
Scores are always calculated within a specific outcome-based segment, never across the whole market, because aggregated scores mask the segment-level gaps that drive strategy (p. 109-110).
The scored outcomes are then plotted on an Opportunity Landscape — an importance-by-satisfaction scatter plot that partitions every outcome into underserved, appropriately served, or overserved zones, "revealing with a high degree of precision where the targeted segment is under- and overserved" (p. 110).
Why This Matters
Without quantified scoring, teams default to intuition, loudest-voice politics, or technology-push logic when choosing which outcomes to address. The Bosch circular saw case demonstrates the cost of this default: laser guidance was the "innovative" option by conventional logic, yet the algorithm showed it solved nothing customers actually needed. The winning strategy used existing technology to address 14 real, quantified gaps — and the resulting CS20 became "the company's best-selling circular saw in North America for over 10 years" (p. 108).
The Opportunity Landscape converts a subjective portfolio debate into a single visual that separates "defend" (table stakes), "cut" (overserved), and "grow" (underserved) decisions. This eliminates organizational argument about where to invest next and exposes the fastest paths to ROI — which are often not new products at all, but repositioning existing ones around the gaps the data reveals (p. 112-115).
Good Examples
Bosch circular saw — scored gap drives product design. "Minimize the likelihood that the cut goes off track" scored among 270 users: importance 74% (7.4), satisfaction 28% (2.8). Score: 7.4 + (7.4 - 2.8) = 12.0, well above the underserved threshold. This outcome, alongside 13 others, directed the CS20 design using existing technology (p. 109-110).
Cordis — landscape exposes messaging gap. The Opportunity Landscape revealed that an existing product already satisfied outcomes competitors missed, but those strengths had never been communicated. Messaging existing "un-messaged strengths" to the right segment drove market share from 1.5% to 5% in six months (p. 114).
Coloplast Wound Care — landscape reframes the value proposition. Competitors messaged "heal faster," but 10 of 15 underserved outcomes in the target segment concerned preventing complications. Reframing the value proposition around this divergence drove double-digit growth in under six months (p. 115).
Counterpoints
Technology-push ignores algorithm output. Bosch evaluated laser guidance for circular saws. It looked innovative but addressed no unmet outcomes — "a sure recipe for failure" that "would have added to the cost" with "little impact on getting the job done better" (p. 108).
Aggregating scores across the full market. Running the algorithm on the entire population rather than within outcome-based segments masks the very gaps that create strategic leverage. The segment-specific calculation is what makes the score actionable (p. 109-110).
Treating overserved outcomes as growth opportunities. Outcomes with satisfaction >= importance cap at the importance value. Investing further in these wastes resources; the correct move is cost reduction — stripping expensive features here to fund investment in underserved outcomes (p. 111).
Key Quotes
"When building an effective innovation strategy there is no room for hunches or guesswork." — Tony Ulwick, p. 108
"Deciding which unmet desired outcomes to target for growth is the essence of strategy and the most important decision a company will make." — Tony Ulwick, p. 108
"The chance that a development team will develop a product that addresses the most underserved outcomes to target if they don't know precisely what those underserved outcomes are is extremely low." — Tony Ulwick, p. 112
Rules of Thumb
- Score >= 10 = underserved. This is the threshold that flags an outcome as a growth opportunity (p. 110).
- Score = importance when satisfaction >= importance. The algorithm deliberately caps these; do not chase them.
- Always score within a segment, never the full market. Aggregation destroys the signal.
- Table stakes (high importance, high satisfaction) are constraints, not differentiators. Any new product must match them or be disqualified (p. 111).
- Overserved outcomes are cost-reduction targets. Strip investment here to fund underserved gaps (p. 111).
- Check for targeting, messaging, and framing gaps before building. The first ROI from the algorithm often comes from repositioning existing products, not developing new ones (p. 114-115).
- The same quantitative study powers segmentation, competitive analysis, strategy, and opportunity targeting — one dataset throughout (p. 109).
Related References
- Desired Outcome Statements - The inputs the algorithm scores
- The Growth Strategy Matrix & Five Strategies - Strategy selection based on algorithm results
- Outcome-Based Segmentation - Segments where the algorithm is applied