Value Curve
A value curve compares offerings on the factors one customer segment values. It comes from Blue Ocean Strategy (W. Chan Kim and Renée Mauborgne). Used here, it answers one question: does our offering meet this segment's specific needs far better than anything else they can use? A good strategy competes to be unique, not the best at everything.
It is a starting point, not the whole answer. It feeds the Alternatives part of the value proposition (the value-proposition skill), and the value proposition needs all six parts.
Before you start
- One segment per curve. Needs differ between segments, so a curve that mixes them shows no focus. If the segment isn't chosen yet, find clusters of customers with similar underserved needs first (
market-segments, thenbeachhead-segment). - The factors are the segment's needs, not your features. Phrase each as an outcome the customer wants ("find live sports events", "content for kids"), solution-agnostic, the way jobs to be done are phrased.
- Evidence comes from customers. For every need, interviews ask how important it is and how satisfied they are with what they use today. They also ask which competitors' products they use, and what they would pay relative to those (a Van Westendorp price survey is one way). About 10-15 interviews per segment.
- With no interviews yet (only websites, reviews, the owner's own knowledge), the curve can still be drawn, but label every score an assumption and name its source. Its first output is then what to test with customers.
Steps
- Score every need for the segment. For each one, opportunity to add value = importance × (1 − satisfaction), with both on a 0-1 scale. This is Dan Olsen's formula from The Lean Product Playbook: simple, and it agrees with the jobs-to-be-done opportunity score where it matters most (high importance, low satisfaction).
- Pick 5-7 factors.
- The needs with the highest opportunity scores.
- Must-haves: needs this segment rates highly important even though today's products already satisfy them. Their low opportunity score doesn't make them optional; dropping one loses the customer.
- Leave out needs this segment doesn't rate as important, even if competitors compete hard on them.
- Add relative price as a factor, scored so a lower price scores higher. Then every factor reads the same way: higher is better for the customer.
- List the offerings. Your product (as planned, or as it is), the 2-4 competitors the segment actually uses, and at least one substitute: the spreadsheet, the agency, the manual workaround, doing nothing.
- Score each offering 0-100 on each factor from what customers said and what reviews show, not from feature lists. A feature that exists but doesn't serve the need scores low.
- Draw it. Factors on the horizontal axis, ordered by importance to the segment; score on the vertical axis; one line per offering, with a legend. Keep the table of scores beside the chart, with the source of each score.
- Read it, and write the reading down in a few sentences:
- Where we must be far ahead: the segment's top-opportunity needs. If our line isn't clearly above everyone else's there, there is no reason to switch.
- Where parity is enough: the must-haves.
- What we deliberately don't compete on: factors where we sit at or below competitors because this segment doesn't care. Saying it out loud is the strategy.
- Lines that look the same: if our curve has the same shape as a competitor's, we're competing on degree, not difference. Blue Ocean's four actions help reshape it: which factors to eliminate, reduce, raise, or create.
Output
- The segment the curve is for, in one line.
- The table: factors × offerings, each score with its source (an interview count, a review, or "assumption").
- The chart.
- The reading (step 6), and the Alternatives line it gives the value proposition: our unique value against each competitor and substitute, and the relative price.
- The weakest assumptions behind it, as what to test next.
Example
A streaming service for one segment: people who care about live sports events and concerts, and whose needs today's services underserve. The scores are illustrative.
| Factor | Our product | Competitor A | Competitor B |
|---|---|---|---|
| Sports events | 95 | 40 | 30 |
| Concerts | 90 | 35 | 45 |
| Content for kids (must-have) | 70 | 75 | 70 |
| Movies | 30 | 90 | 85 |
| Relatively low price | 60 | 50 | 65 |
- We aim to be the best at what this segment needs most: sports events and concerts.
- Content for kids stays at parity. Customers are already satisfied with it, but it matters to them.
- Movies are left low on purpose: this segment doesn't rate them as important.
Mistakes
- Scoring features instead of needs, which turns the curve into a feature comparison.
- Too many factors. Past seven, the shape stops saying anything.
- Factors the segment doesn't care about, kept because competitors fight over them.
- Trying to beat every competitor on every factor. That is the opposite of a strategy, and usually unaffordable.
- One curve for several segments.
- Treating assumed scores as findings. Unlabelled guesses look exactly like evidence on a chart.
How you can tell it worked
The curve is for one segment, every score has a source or is labelled an assumption, and the reading says where the product must be far ahead and what it deliberately doesn't compete on.
