What Customers Want: Using Outcome-Driven Innovation to Create Breakthrough Products and Services
Ulwick is the consultant who developed the Outcome-Driven Innovation method that Christensen later popularised as Jobs-To-Be-Done.
The book is the operational companion to Competing Against Luck: where Christensen provides the frame, Ulwick provides the research methodology.
The core move is asking customers not what they want (they cannot tell you well) but how they currently measure the success of the job they are trying to do, and where those measurements are unmet.
For product direction it is a useful rebalancing — the method is less poetic than Christensen's milkshake parable and more rigorous in practice.
Less read than Christensen, probably more useful for teams actually running research.
Central argument
Ulwick argues that innovation fails not because companies lack creativity but because they ask the wrong research questions: asking customers what they want produces unreliable answers, while asking how customers currently measure success at a job they are trying to accomplish surfaces stable, actionable criteria. His Outcome-Driven Innovation method operationalises this by having customers rate desired outcomes on both importance and current satisfaction, then targeting outcomes that score high on importance and low on satisfaction — the systematic gap between the two is where breakthrough opportunity lives. The method produces a quantified opportunity landscape rather than a set of feature requests, shifting product strategy from opinion to measurement.
Critique
The method's rigour is also its constraint: ODI assumes customers can articulate how they measure success at a job, which holds reasonably well for functional jobs but becomes strained for jobs with heavy emotional, social, or habitual dimensions — areas where digital products increasingly compete. There is also a conservatism baked into the approach: by anchoring discovery to existing jobs and existing measures of success, ODI is structurally better at improving what already exists than at identifying jobs that customers do not yet know they have, or that only become visible once a new capability exists. Teams working in genuinely new solution spaces may find the method narrows their aperture at precisely the moment they need it open.
Why it matters for product
For a CPO running discovery, the opportunity algorithm — importance minus satisfaction, at scale — gives a defensible, stakeholder-legible way to prioritise which problems deserve investment before any solution is discussed, which directly addresses the common failure mode of roadmaps built around solutions rather than unmet needs. The method also has structural implications: it separates the job of identifying unmet outcomes (a research and strategy function) from the job of generating solutions (a design and engineering function), which maps cleanly onto how product teams should be organised if they want to avoid conflating problem space and solution space too early.