Quick answer
An opportunity solution tree (OST) is a product-discovery map, popularised by Teresa Torres, linking one desired outcome to the customer opportunities that could drive it, the solutions for each opportunity, and the experiments that test them.
What is an opportunity solution tree?
An opportunity solution tree (OST) is a visual map that connects one desired outcome to the customer opportunities that could drive it, the solutions that might address each opportunity, and the experiments that test those solutions. It was popularised by product discovery coach Teresa Torres and is the central artefact in her Continuous Discovery Habits.
Its job is to stop teams jumping from a goal straight to a feature. Every solution on the tree has to hang off a customer problem, and every problem has to plausibly move the outcome.
The four levels
| Level | What goes here | Example |
|---|---|---|
| Outcome | One measurable product outcome the team owns | Increase trial-to-paid conversion from 9% to 12% |
| Opportunities | Customer needs, pain points and desires, in the customer's words | "I can't get my data in without a developer" |
| Solutions | Ideas that could address one opportunity | A CSV importer; a guided setup call |
| Experiments | Quick tests of the riskiest assumption behind a solution | A painted door test; a prototype test with 5 users |
Opportunities are usually broken into smaller sub-opportunities until each is specific enough to solve. A good outcome is a product metric the team can influence, close to a north star metric but more direct.
How to build one
- Agree on one outcome with leadership. A tree with three outcomes is three trees.
- Gather opportunities from customer interviews, support conversations, reviews and behavioural data. Phrase each as a need, not a solution ("I need to know if the import worked", not "add a progress bar").
- Structure them into a hierarchy: big opportunity, then the specific situations underneath it.
- Choose a target opportunity by size, importance to customers and fit with strategy.
- Generate at least three solutions for it, so you compare rather than fall in love with the first idea.
- Test assumptions behind the solutions with small experiments before building.
- Revisit weekly as you learn.
A worked example
Sample data for a SaaS analytics product with 1,000 trials a month and a 9% trial-to-paid rate.
Outcome: trial-to-paid 9% → 12%.
| Opportunity (customer's words) | Evidence | Trials affected |
|---|---|---|
| "I can't get my historical data in" | 38% of trials never complete a data import (funnel); top theme in pre-sales chats | 1,000 × 38% = 380 |
| "I don't know which report answers my question" | 27% of activated trials view only the overview page | 1,000 × 62% activated × 27% ≈ 167 |
| "My team can't see it" | Trials with one user convert at 6%, with 2+ users at 18% (segments) | 1,000 × 70% single-user = 700, but a weaker direct link |
The import opportunity is the largest well-evidenced one, so it becomes the target. Three solutions: a CSV importer, an import-from-competitor tool, and a free import call. The riskiest assumption behind the importer is that people have their data in CSV at all, so the first experiment is a painted-door "Import CSV" button, counting clicks per trial before anything is built.
Note the sizing uses behavioural data, and the wording comes from conversations. The tree needs both.
Common mistakes
- Solutions disguised as opportunities ("users need a dashboard builder").
- An output as the outcome ("ship the importer") instead of a change in behaviour.
- Only one solution per opportunity, which turns the tree into a justification for a decision already made.
- Building without testing assumptions, skipping the bottom level.
- Drawing it once. The value is in updating it weekly.
Using TapCub as evidence for the tree
- Funnels and segments in All-platform analytics › Behavior › Behavior analysis and Users › Segments & tags size opportunities: how many trials stop at each step, and how converted users differ from the rest.
- Live chat transcripts and reports give opportunities in customers' own words; code them as described in qualitative data.
- Custom events measure experiments, such as clicks on a painted-door button, by trial and by segment.
For how discovery fits into the wider product process, see product discovery and product hypothesis.
Sources
- Torres, T. Opportunity Solution Trees: Visualize Your Discovery to Stay Aligned and Drive Outcomes. Product Talk.
- Torres, T. (2021). Continuous Discovery Habits: Discover Products that Create Customer Value and Business Value. Product Talk LLC.
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Product Adoption Consultant
Explores customer onboarding, live conversations, and self-service, turning common usage questions into practical guidance and improvements.