Multiple-Solution Decision Method
Generate several viable paths, test them cheaply, and select for context.
- Difficulty
- Easy
- Time to result
- ~days to results
- Steps
- 5
- Confidence
- 95%
The method separates understanding a problem from selecting its solution. First, define the desired outcome and constraints without importing a previous employer's playbook. Generate multiple approaches, including options that differ in cost, speed, complexity, and operating assumptions. Next, classify the decision by risk: reversible, low-risk options can be tested through minimum viable versions, while payroll, financial reporting, and other high-risk functions require stronger controls. Compare the resulting evidence rather than defending the most familiar idea. The output is a solution chosen for the current company and situation, not merely the solution that worked elsewhere. The method also guards against applying one successful framework, such as growth experimentation, indiscriminately across every department.
Origin
Extracted from Marketing Against The Grain during a debate about leaders assuming that every problem has only one correct solution.
Core principles
- 01Common problems can require uncommon solutions.
- 02Past playbooks are inputs, not automatic answers.
- 03Low-risk decisions permit faster experimentation.
- 04The best solution fits the current context.
- 05One method should not be forced onto every problem.
How to run it
- 1
Define the Outcome
State the problem, desired result, constraints, and unacceptable outcomes without embedding a preferred tactic in the definition.
Pro tip Ask what must become true rather than how the team has solved similar problems before.
Watch out A solution-shaped problem statement will bias every option that follows.
- 2
Generate Distinct Paths
Develop several approaches that differ meaningfully in mechanism, not just minor execution details.
Pro tip Include at least one option that does not resemble the incumbent industry playbook.
Watch out Renaming variants of the same idea does not create genuine choice.
- 3
Classify the Risk
Assess each option for reversibility, operational exposure, cost, and consequences of failure. Reserve rapid experimentation for situations where failure is tolerable.
Pro tip Use smaller tests as risk rises, but do not pretend critical controls are experiments.
Watch out Minimum viable execution is unsafe when mistakes could disrupt payroll, compliance, or financial reporting.
- 4
Run Minimum Viable Tests
Test the safest and most promising options at the smallest scale capable of producing useful evidence.
Pro tip Set the comparison criteria before results arrive.
Watch out Do not force experimental methods onto departments or decisions where the risk profile makes them unsuitable.
- 5
Select for Context
Compare evidence, constraints, and customer value, then choose the solution that best fits the current situation. Scale it while remaining willing to revisit the choice.
Pro tip Treat prior experience as useful context rather than proof.
Watch out Familiarity can feel like evidence even when the new company's conditions are different.
In the wild
A new marketing leader avoids copying a former employer's SEO playbook. The team compares a content-led model, programmatic landing pages, partnerships, and product-driven acquisition, then pilots the two reversible options with the strongest fit for its audience.
→ The company selects a context-specific acquisition model using comparative evidence rather than reputation.
A founder wants every department to adopt growth experimentation. Instead, the company classifies finance controls as high risk, tests an onboarding assistant within HR, and reserves rapid minimum viable trials for reversible internal workflows.
→ Experimentation expands without weakening critical operational controls.
Common mistakes
Importing the Old Playbook
A leader recognizes a familiar problem and assumes the former company's solution remains correct despite changed constraints.
Testing High-Risk Operations Casually
The team treats payroll, auditing, or another critical function like a reversible growth experiment.
Forcing One Framework Everywhere
A successful method is applied across the company even where its assumptions and risk tolerance do not fit.
Is it for you?
Best for
It is best for leaders and teams confronting ambiguous problems with several plausible solutions.
Not ideal for
It is not ideal for tightly regulated or irreversible operations where experimentation could cause unacceptable harm.
From the transcript
“The problems are very common. The solutions are very uncommon, right?”
“if you have a big hairy problem that is hard to solve, think about multiple solutions and know that there is more than just one…”
“most people struggle with, how do I actually have a selection of solutions and minimal viable version of them, and decide which one's the right…”
From the episode
5 Marketing Mistakes That Are Killing Your Business