Growth Hacking with AI How to Find, Test, and Scale Growth Channels 10x Faster Using AI-Powered Experimentation and Data Analysis
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Sprache:Englisch
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Beschreibung
Produktdetails
Format
ePUB 3
Kopierschutz
Nein
Family Sharing
Ja
Text-to-Speech
Ja
Erscheinungsdatum
02.09.2026
Verlag
BGB LearnSeitenzahl
122 (Printausgabe)
Dateigröße
1538 KB
Sprache
Englisch
EAN
6610001355265
You picked up this book because your funnel has 12 possible tests and you have time for 2, while competitors ship weekly. Maybe your paid cost per click is up 40% in 24 months and every hack gets copied in 2 weeks. Maybe you own data from Amplitude, Mixpanel, and GA4 but your report still says traffic is steady while revenue stalls. Whatever brought you here, you know growth is not a campaign, it is a system that compounds, and AI compresses weeks into days when plumbing is right. This book is not a list of prompts for subject lines, and it is not a theory deck about AI. What you actually need is an experimentation operating system, 5 infrastructure pieces, a 7-phase loop, and decision rules that turn data into 15 experiments per month without losing rigor. The pages that follow teach value-based bidding that lifts ROAS by 30% to 100%, behavioral lookalikes that beat demographics, and creative rotation before fatigue costs 50% performance in 30 days. Inside, you'll discover: . The AARRR funnel analysis with AI that finds 1 high leverage metric among 5 stages . Hypothesis generation at 10x velocity, using the If we change X for Y then Z framework . ICE scoring that blends impact, confidence, and ease with historical win rates . Bullseye channel discovery across 19 channels, with kill criteria under $3000 in 4 weeks . High velocity experiment design, 8 steps, guardrails, and pre-launch checklists that protect validity . The experimentation loop, weekly 30-minute reviews that raise kill rate and learning velocity . Churn, LTV, and conversion forecasting, plus referral loops that scale without spam Treat every campaign as an experiment with a primary metric, minimum detectable effect, and end date you do not move. Build a learning repo in Notion or Sheets, feed outcomes into hypothesis generation, and watch next month's tests get sharper.
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