The bad doctor problem

Here’s a scenario I see constantly. A team looks at their funnel data, sees a big drop-off at, say, checkout, and concludes: “Our checkout page is the problem. Let’s optimize it.”

They Google the latest CRO tactics. They redesign the page. They run a dozen A/B tests. Nothing moves.

Why? Because they acted like the bad doctor. They saw a symptom (low checkout conversion) and jumped straight to a prescription (optimize the page). They never stopped to diagnose the actual problem.

Maybe the checkout page is fine. Maybe the drop-off exists because a page upstream didn’t adequately communicate value. Maybe 40% of the traffic came from dogs who hijacked their owners' laptops.

The diagnostic process

1. Define the goal and constraints.

Where are you trying to go, how much can you invest, and by when? “Grow revenue” isn’t a goal — it’s a direction. “Add $400k in MRR within 90 days without increasing costs more than $50k/month” is actionable. Without constraints, you can’t evaluate tradeoffs between problems worth solving.

2. Map the levers.

Build a growth model. Break your North Star into its inputs. Revenue = customers × revenue per customer. Customers = leads × conversion rate. Leads = visits × sign-up rate. Keep breaking it down until you reach levers you can actually influence.

3. Identify high-impact levers.

You need to understand which levers have the greatest influence on your North Star. How you do this depends on your stage and data. A mature product like Duolingo can run a formal sensitivity analysis by applying a uniform increase to each lever and modeling the output. An earlier-stage company might work backwards from the goal: if we need $50k in new monthly revenue, what are the possible levers, and which ones does our model suggest are most efficient?

Either way, the point is the same: let the math narrow the field instead of guessing which lever "feels" most important.

4. Select the problem worth solving.

Knowing which levers have the most mathematical impact doesn't tell you where to focus.

You also need to consider: can we actually move this thing? How much would it cost? Is there evidence it's genuinely underperforming, or does the math just make it look attractive?

The right problem to solve isn't always the highest-impact lever. It's the one that best balances impact, feasibility, and evidence — all while staying within the constraints.

5. Diagnose before you prescribe.

This is the step everyone skips. You’ve selected the lever. Don’t brainstorm solutions yet. Ask why that lever is stuck.

Talk to users. Talk to customer-facing teams — support, sales, anyone who hears directly from customers. Study historical data: has this metric always been this way, or did something change? Look at how other companies have solved similar problems. Watch session recordings. Examine what’s happening upstream in the funnel.

6. Now enter Solution Discovery.

This is where the conventional growth playbook kicks in — and where it actually works, because it's grounded in real evidence. Now we can form testable hypotheses, run them through prioritization frameworks, design experiments, and start generating learnings that help us hone our hypotheses and get closer and closer to the truth.

Steps 1–5 are Problem Discovery. Step 6 is Solution Discovery. Most teams start at step 6. Some start at step 3. Almost nobody does steps 4 and 5. And that's where the real leverage lives.