When Simple Would Have Worked (But We Got Fancy Instead)
The Problem
Analytics team builds a "Customer Health Score" with 23 variables, weighted by a machine learning model nobody understands. It spits out numbers from 0-100. Sales teams ignore it because they don't trust a black box. Meanwhile, the real signal was obvious: customers who don't log in for 30 days churn. That's it. One simple rule would've worked better than the fancy model. But we overcomplicated it because smart people like to build smart things. Complexity feels impressive. Simple feels too easy. But complexity kills adoption. If people don't understand it, they won't use it. It's like building a Swiss watch when all you needed was a sundial.
The Principle
Start simple. Solve the problem with the simplest solution first. If "customers who don't log in for 30 days churn," just track login recency. You don't need a PhD-level model. Add complexity only when simplicity fails. Think manufacturing: you don't need a robotic arm when a lever works. The magic happens when solutions are so intuitive that people adopt them immediately because they just make sense. Less wizardry, more clarity. Stupidly simple always wins over impressively complex.
3 Steps to Take Action
Start with the Simplest Solution: Before building anything complex, ask: "What's the dumbest simple version that could work?" Try that first. Add complexity only when simplicity fails.
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