Foreword: From Hack to System

Chapter 1: Product–Market Fit Still Wins

Chapter 2: Data-Driven Foundations in a Privacy-First World

Chapter 3: Growth Loops and the Funnels Inside Them

Chapter 4: Retention Is the Engine

  • Alistair Croll and Benjamin Yoskovitz, Lean Analytics (O’Reilly, 2013). Cohort analysis and stage-appropriate measurement. https://leananalyticsbook.com/

Chapter 5: Pricing Is a Lever, Not a Label

Chapter 6: Positioning Is the Decision Underneath Everything

Chapter 7: Content Worth Finding and Citing

Chapter 8: Working With Social Discovery

  • For example, TikTok describes recommendations using interactions, content information, and user information, and Instagram describes separate ranking systems for Feed, Stories, Explore, and Reels. TikTok Support, “How TikTok recommends content,” https://support.tiktok.com/en/using-tiktok/exploring-videos/how-tiktok-recommends-content ; Instagram, “Instagram Ranking Explained” (2023), https://about.instagram.com/blog/announcements/instagram-ranking-explained . Product descriptions change and do not disclose a complete ranking formula.

  • Beginning March 31, 2025, YouTube Shorts views count starts and replays without a minimum watch time; the earlier measure became “engaged views,” and YouTube separately reports whether viewers stayed to watch or swiped away. YouTube, “A Change to How We Count Views on Shorts” (March 26, 2025), https://support.google.com/youtube/thread/333869549?hl=en ; YouTube Help, “Content tab analytics tips — Shorts,” https://support.google.com/youtube/answer/12942217?co=YOUTUBE._YTVideoType%3Dshorts&hl=en .

  • e.l.f. Beauty, “e.l.f. Turns Up the Volume on Beauty with Release of ‘Eyes. Lips. Face.’ Song” (October 11, 2019). Counts as reported by the company at that date. https://investor.elfbeauty.com/stock-and-financial/press-releases/landing-news/2019/10-11-2019-073719954

  • Instagram, “Instagram Ranking Explained” (2023): Reels are built for discovery and “the majority of what you see is from accounts you don’t follow”; “the most important predictions we make are how likely you are to reshare a reel, watch a reel all the way through, like it, and go to the audio page”; Instagram also surveys people on whether they find a reel entertaining. Unlike TikTok’s description, Instagram lists information about the poster, including follower count, among its inputs. https://about.instagram.com/blog/announcements/instagram-ranking-explained

  • Instagram, “Instagram Ranking Explained,” cited earlier in this chapter: watching a reel all the way through is among its most important predictions.

  • Instagram, “Instagram Ranking Explained,” cited earlier in this chapter: Instagram surveys people on whether they find a reel entertaining.

  • TikTok Newsroom, “How TikTok recommends videos #ForYou” (June 18, 2020): “neither follower count nor whether the account has had previous high-performing videos are direct factors in the recommendation system,” while “a video is likely to receive more views if posted by an account that has more followers”; and “a strong indicator of interest, such as whether a user finishes watching a longer video from beginning to end, would receive greater weight than a weak indicator.” https://newsroom.tiktok.com/en-us/how-tiktok-recommends-videos-for-you

  • TikTok Newsroom, “How TikTok recommends videos #ForYou,” cited earlier in this chapter: a strong indicator such as finishing a longer video receives greater weight.

  • YouTube Creators, “Video content creation strategy” (How Things Work), checked October 2026: “our algorithm doesn’t pay attention to videos, it pays attention to viewers.” YouTube Help, “Search & discovery tips — Shorts,” describes viewers being matched to the Shorts they are most likely to watch and enjoy. https://www.youtube.com/intl/en/creators/how-things-work/content-creation-strategy/ ; https://support.google.com/youtube/answer/11914225?hl=en&co=YOUTUBE._YTVideoType%3Dshorts

  • Growth Hacking 101, content section (“So now you have a video – what good is that?”): transcribe a recorded talk, then cut snippets and “audiograms” for Facebook, Instagram and TikTok; Instagram pods are in the same book’s social section. The quoted line is from the author’s Growth Hacking 101 talk, which listed funny, controversial, helpful and amazing as what makes something go viral.

  • Heather Wilde, “How to Ensure You’re Getting the Most Out of Your Influencer Marketing Strategy,” Inc. (September 28, 2018). The same column closed: “if your business plan is just ‘Become a YouTube Star,’ I suggest you start working on your resume.” https://www.inc.com/heather-wilde/how-to-ensure-youre-getting-most-out-of-your-influencer-marketing-strategy.html

  • Cristos Goodrow, “On YouTube’s recommendation system,” YouTube Official Blog (September 15, 2021): signals include clicks, watch time, survey responses, sharing, likes and dislikes; “valued watchtime” is measured with surveys asking viewers to rate a video from one to five stars, and only four- and five-star ratings count. YouTube Help, “Search & discovery tips — Shorts”: signals include whether viewers choose to watch a recommended Short, ignore it, or select “not interested,” along with the share who chose to view and average percentage viewed. https://blog.youtube/inside-youtube/on-youtubes-recommendation-system/ ; https://support.google.com/youtube/answer/11914225?hl=en&co=YOUTUBE._YTVideoType%3Dshorts

  • Goodrow, “On YouTube’s recommendation system,” cited earlier: valued watch time counts only four- and five-star survey ratings.

  • Goodrow, “On YouTube’s recommendation system,” and YouTube Help, “Search & discovery tips — Shorts,” cited earlier: dislikes and “not interested” are among the signals.

  • TikTok lists videos you like or share among the user interactions it uses (“How TikTok recommends videos #ForYou”); YouTube lists sharing among its satisfaction signals (Goodrow, “On YouTube’s recommendation system”); Instagram lists the likelihood you’ll reshare a reel (“Instagram Ranking Explained”). Full citations are in the notes above.

  • The feature is called trial reels. Meta, “Test Content With Non-Followers Using Trial Reels” (December 10, 2024): trial reels are shown to non-followers first, and creators can then share them with followers manually or automatically. https://about.fb.com/news/2024/12/trial-reels-try-content-non-followers-first-see-what-perfoms-best/

  • The feature is called Test & Compare. YouTube Help, “A/B test titles & thumbnails” (checked October 2026): up to three thumbnails, titles, or title-and-thumbnail combinations per test; the result is reported as “Winner,” “Performed same,” or “Inconclusive,” judged on watch time share rather than clicks. Title testing was added in 2025. https://support.google.com/youtube/answer/13861714

  • YouTube Help, “Measure key moments for audience retention”: “Intro” shows the share of viewers still watching after the first 30 seconds; spikes mark parts that were rewatched or shared; dips mark parts skipped or abandoned. https://support.google.com/youtube/answer/9314415 . For the Shorts “stayed to watch” versus “swiped away” measure, see YouTube Help, “Content tab analytics tips — Shorts,” https://support.google.com/youtube/answer/12942217 .

  • YouTube Help, “Measure key moments for audience retention,” cited in the previous note: spikes appear when more viewers are watching, rewatching, or sharing part of a video.

  • From the author’s own channel, as of October 2026. Her agents had been posting to it since the previous December, through a registered TikTok app that uses TikTok’s Content Posting API and a scheduler. Counts at that date: 21 followers and 152 likes in total; individual videos drew roughly 80 to 290 views, most around 100 to 150. No video broke out.

Chapter 9: Creators, Community, and Recommendations

Chapter 10: Direct Channels You Can Maintain

Chapter 11: AI-Powered Growth That Earns Its Place

Chapter 12: Trust in the Age of Synthetic Media

Chapter 13: Continuous Growth

Chapter 14: Case Studies Without the Victory Lap

Chapter 15: Measuring Growth When the Click Is Missing

Chapter 17: AI as an Experimentation Multiplier

  • Lei Huang et al., “A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questions,” arXiv:2311.05232 (2023; revised 2024). https://arxiv.org/abs/2311.05232

Chapter 18: The Four-Layer Distribution Stack

Chapter 22: From Prompt to Product

Chapter 23: The Anti-Playbook

Appendix Q: Niche Radar Project Brief

Appendix R: Crawler Access, Citation Readiness and the Weekly AI Visibility Log

Appendix S: Optional LLM-Readable Documentation

  • Jeremy Howard, “The /llms.txt file, v2,” originally published September 3, 2024; modified August 10, 2026. https://llmstxt.org/

  • Google says no special AI text file is needed for its AI search features. John Mueller (Google) also said publicly that, as far as he knew, none of the AI services had said they use the file, and that server logs showed they didn’t request it; reported in “Google Says LLMs.Txt Comparable To Keywords Meta Tag,” Search Engine Journal (2025), https://www.searchenginejournal.com/google-says-llms-txt-comparable-to-keywords-meta-tag/544804/ . A public comment, not formal documentation; check your own server logs.