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Growth idea action plan

Superhuman PMF survey before growth spend

Ask active users how disappointed they would be without the product, then hold back growth spend until the answer shows real pull.

rare tacticlow budget

Why this can grow a startup

A waitlist, launch spike, or loud investor meeting can make weak pull look stronger than it is. Superhuman used the Sean Ellis question on recently active users and found only 22% would be very disappointed if the product disappeared. That gave the team a plain operating number instead of a mood. The growth lesson is uncomfortable but useful: before buying demand, measure whether the current users would fight to keep the product. If the answer is soft, spend the next sprint improving fit instead of filling a leaky bucket.

Company example

Superhuman surveyed users who had used the product at least twice in the prior two weeks, found a 22% very-disappointed score, and treated that as evidence it was not ready to scale demand.

Source and metric

Source: First Round Review: How Superhuman Built an Engine to Find Product Market Fit · Browse First Round Review: How Superhuman Built an Engine to Find Product Market Fit tactics

Superhuman moved from 22% very-disappointed to 58% within three quarters after using the PMF engine.

Customer ResearchActivationProduct-Led Growthproduct-market fitcustomer researchactivationpre-scale
GrowthDex operator note

When to use it

Use this when Customer Research, Activation, Product-Led Growth is relevant to product-market fit, customer research, activation and you can run a bounded test with a low budget.

When not to use it

Do not use it as a substitute for customer evidence, a clear owner, or a measurable stop condition. Local platform rules and market behavior still need checking.

Founder checklist

  1. Read First Round Review: How Superhuman Built an Engine to Find Product Market Fit and identify what is directly supported.
  2. Choose one channel context: Customer Research, Activation, Product-Led Growth.
  3. Define the test around Superhuman moved from 22% very-disappointed to 58% within three quarters after using the PMF engine..
  4. Set an owner, evidence window, and stop condition before launch.

Explore the context

Product-Led GrowthCustomer ResearchActivationProduct-Led Growth

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Advisory bridge

Apply this with an operator

Choose product surfaces that compound distribution without hiding weak activation or retention.

Work with Ian