When your Amazon business throws up a hundred small problems at once, fixing each one individually is the slow way out. Danny McMillan walks through collaborative upstream problem solving — a method that combines systems thinking, affinity mapping and root-cause analysis to find the handful of shared causes behind a long list of issues, turning roughly 10 days of work into 2-3 hours.
Key Topics
- Collaborative upstream problem solving – fixing the shared conditions behind many problems instead of each problem one by one
- The four-part method – systems thinking, affinity mapping, root cause analysis and collaborative validation
- Leverage points – the smallest set of changes that makes many problems disappear
- Why domain knowledge still wins – the human in the loop checks that the right problems were solved
- A Claude example – forgotten decisions, contradictory outputs and stale documentation as one shared context problem
- Turning the method into a skill – reverse engineer the transcript in Claude
Timestamps
- 00:00 – Danny goes solo, Adam out this week
- 00:25 – Perplexity names the method: collaborative upstream problem solving
- 00:46 – The problem: a friend buried in Claude issues
- 01:36 – How do you turn 100 problems into a few?
- 01:58 – The combined approaches: systems thinking first
- 02:20 – Affinity mapping and root cause analysis
- 02:43 – Collaborative validation: domain knowledge wins
- 03:26 – Leverage points, not faster fixes
- 03:57 – Why domain knowledge matters: avoiding the wrong problems
- 05:03 – Clusters and markdown handover: 2 to 3 hours versus 10 days
- 05:18 – The five-step practical approach
- 06:22 – Claude example: forgotten decisions and stale documents
- 07:06 – Avoiding fixes that break other outputs
- 07:25 – A hypothesis, not a diagnosis
- 07:44 – Turning this into a Claude skill
- 07:55 – The method in one paragraph
- 08:23 – Wrap-up
Key Takeaways
- Solve the conditions, not the problems – look for the shared causes generating a long list of issues.
- Cluster before you fix – affinity mapping turns 100 items into a handful of meaningful groups.
- Treat drivers as hypotheses – validate them with the person who holds the domain knowledge before acting.
- Test one intervention first – make a small change and check whether several related problems improve.
- AI executes, you think – the human in the loop and domain expertise decide whether the fixes are right.
The Five Steps
- 1. Capture examples – the problems, plus the outcome the person wants
- 2. Cluster the symptoms – group problems by meaningful relationships using affinity mapping
- 3. Propose underlying drivers – recurring conditions that might explain each cluster, held as hypotheses
- 4. Validate together – check the groupings, exceptions, constraints and what is fixed
- 5. Test one intervention – a small change, then check whether related problems improve
Notable Quotes
“I’m not trying to solve a hundred problems faster. I’m trying to find the smallest set of changes that could make many of these problems disappear.” – Danny McMillan
“AI isn’t just your assistant. You’re driving the process yourself. It’s you who’s doing the thinking.” – Danny McMillan
“I solve the conditions creating the problems, not the problems themselves.” – Danny McMillan
Resources Mentioned
- Perplexity – used to find a concise name for the thinking approach
- Affinity mapping – the established technique for grouping many issues into meaningful clusters
- Claude – used to organise the material and build the markdown handover files
- Cron job checks – a scheduled way to keep documentation current and replace stale information
Connect
Questions on the method? Email Danny McMillan at danny@sellersessions.com.
Seller Sessions is the leading podcast for advanced Amazon sellers, hosted by Danny McMillan.