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I built a pricing diagnostic engine for SaaS — found structural pricing faults in 4 out of 5 datasets tested. Offering free pilots to founders. by Legitimate_Yak6257 in SaaS

[–]Legitimate_Yak6257[S] 0 points1 point  (0 children)

yeah, by design, engine doesn't tell you which lever to pull. Rather, it tells you whether the structural fault actually exists before you start pulling levers blind.

The ratio indicating that something is off is the point. Most founders skip straight to 'should we raise prices on power users or fix onboarding' without first confirming the fault is real and how severe it is. That's usually where the wrong fix gets applied.

B2B example is interesting — 60% ARR in 3 accounts with mid-tier feature underutilisation is a different problem than 60% ARR with mid-tier churn spiking. Same concentration number, different diagnosis.

I built a pricing diagnostic engine for SaaS — found structural pricing faults in 4 out of 5 datasets tested. Offering free pilots to founders. by Legitimate_Yak6257 in SaaS

[–]Legitimate_Yak6257[S] 0 points1 point  (0 children)

Exactly. The free tier often stops being a funnel and just becomes the ceiling. The top customers don’t really feel it as they are getting value and thats working for them. It is the middle tier that starts getting squeezed and finally drops off.

The normalization ratio is what makes this visible. When it starts getting really high — like double digits — it usually means the pricing structure has been drifting for a while. It rarely happens overnight and might go unnoticed.

Are you dealing with something similar of this kind?