Find as many Substack newsletters as possible where the author publicly documented meaningful subscriber growth — before/after counts, timeframe, and the specific tactics used. Attribute subscribers to each tactic where evidence exists, separate free from paid growth, note repeatability versus one-off virality, source every claim, then aggregate: which methods most consistently produce substantial growth, which work best under 1,000 subscribers, and which look overrated.
A live-web scan surfaced 34 named newsletters with publicly documented growth, yielding 130 newsletter-tactic instances across 18 tactic categories, sourced from 57 pages on 32 independent hosts. This is a broad scan, not a census of Substack; results are self-reported and one growth period can credit several tactics at once. Counts are subscribers unless a row says paid or free.
Each cell is a documented use of a tactic by a newsletter. Fill shows evidence strength; an orange ring marks a one-off or viral instance. Newsletters sorted by final audience size. Hover any cell for the exact tactic, numbers and source.
From the precomputed aggregates, all documented cases. Bar length = distinct newsletters documenting the tactic; the dark segment is newsletters that also reached 1,000+ overall growth. Attributed-subscriber sums can overlap across tactics and periods — a directional evidence measure, not an additive decomposition.
Every row links its source. Search by newsletter, tactic, or niche.
| Newsletter | Tactic category | Type | Start → end | Timeframe | Attributed | Evidence | Repeat | Source |
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Method: live-web scan of publicly documented Substack growth accounts — 130 newsletter-tactic rows across 34 newsletters, 57 source pages on 32 independent hosts; the most-cited single page supports 10 rows (7.7%). Counts are self-reported subscriber numbers (free, paid, total, or unspecified as stated by the author); attribution figures are the author's own estimates and can overlap across tactics within one growth period. Aggregates come from a precomputed rollup, not recomputed from detail rows. Evidence text and publication dates were trimmed in the table for space; hover the matrix for full detail. "Ineffective" means weak or inconsistent evidence in this dataset, not proof a channel never works.