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Case Study: Attracting My First 1000 Subscribers - Thoughts?
Hacker NewsCase Study: Attracting My First 1000 Subscribers - Thoughts?
Hi guys, I recently wrote up this article examining how I used inbound and email marketing to attract 1000 subscribers for a product I am building. Thought the people here may be interested in the case study. Case Study: Attracting My First 1000 Subscribers http://www.swiftarcher.com/case-study-attractive-my-first-1000-subscribers/ I would love to hear your thoughts.
Share cardActual performance
6points
6comments
Did not reach leaderboard
Launch Intel predictions
Analyze your own launch →Hacker NewsMay not resonate with HN audience · Strong signals: 000 · Missing: https docs, excited, just released44%44% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRLess likely to generate early MRR · Missing: mobile apps, ios, personal44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Product HuntUnlikely to reach the leaderboard · Strong signals: email · Missing: mac, agents, macos42%42% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Indie HackersIH features products with proven revenue · Missing: supports, reddit linkedin, podcasting41%41% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive31%31% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: subscribers, active · Missing: arr, mrr, revenue22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto4%4% predicted probability of success on BetaList, based on ML models trained on real launch data.
Correct prediction on native model
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