Playbook · Early fit
Early fit × Value
At least one segment sticks. Re-run the PMF survey for each new segment you target, classify planned features before building them, and keep mining what retained users did early so onboarding can steer toward it.
- M2 Early fit
- How do we put every new user on the path that retained users took?
- Value
- Does any segment need this badly enough to come back?
M2 × Value
The playbook
Goal. Keep the must-have segment sharp while growth is still founder-led, and check fit again before expanding to a new segment.
Moves. Re-run the PMF survey for each new segment; classify planned features as must-have, performance, delighter or reverse for the loving segment F19; mine the Habit Path of retained users so the Start track can steer new users onto it HK-T13 (Hooked Ch.3). Value is rarely the binding stage here; it binds only when a segment you depend on stops flattening.
Watch. A strong segment average can hide a large segment that declines to zero. Read curves per segment.
Lead with these moves
- TechniqueEvidence grade D. Practitioner framework or anecdote: plausible, untested.Practitioner heuristic
Codify: count habitual users and map the path they share
habitual-user rate and Habit Path
Set a realistic cadence for a devoted user, count the share of each signup cohort who meet it, and compare what those users did in their first days with users who left. The book's rule of thumb: below about 5% habitual, you have the wrong audience or a product to rethink.
Not now
- Scale paid acquisitionPaid traffic into a product that doesn't keep people yet just fills a leaky bucket.
- Annual-plan discountsLocking people in before the habit forms just delays the churn.
- Near-peer leagues (leaderboards)Leaderboards need a dense community to feel fair.
- Heavy urgency on the core loopThere's no habit to protect yet; pressure now trades long-term users for a short spike.
How to measure it
Questions to answer first
- Among users who used the product at least twice in the last two weeks, what share would be very disappointed if they could no longer use it, per segment, and from how many answers?
- For each segment, does retention at your natural cadence decline toward zero, flatten, or rise again? Which segment flattens highest, and how big is it?
- For each feature you plan, would that segment see it as a must-have, more-is-better, a delighter, irrelevant, or something some of them dislike?
- What did your most habitual users do in their first two weeks that others didn't?
- Which customer groups behave differently (by size, use case, plan or channel), and can your data tell them apart?
- Which single number, if it rose, would best show customers getting more value? Do you win on time spent, completed transactions, or jobs done faster?
- What is the main benefit that users who love the product say they get, and what would make it better for those who only somewhat like it?
Limits
What we don’t claim
- The Hook Model is a practical design tool, not a validated or complete theory of behavior.Hooked, Introduction
- The threshold is a heuristic, not a measured cut-off.
- Seasons are defaults for the cell. Evidence from your own product can move a technique in or out of season.
- A playbook names candidate moves; which one goes first depends on the binding constraint in your product.
Sources
Hooked · Nir Eyal
Ch.8 · Ch.3
How Superhuman built an engine to find product-market fit · First Round Review (Rahul Vohra), 2018
Benchmark confidence B. A reputable dataset.Attractive quality and must-be quality · Kano, Seraku, Takahashi and Tsuji, Journal of the Japanese Society for Quality Control, 1984
Evidence grade C. Limited evidence: few or small studies.
Source IDs: F19HK-MEA03HK-MEA04HK-T13
Book content is paraphrased, never quoted at length. Last verified October 24, 2026.
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