Million Dollar Email Templates 2.0

AppSumo

The review summary should be: "Customers appreciate Million Dollar Email Templates 2.0 for its valuable email content, professional templates, and helpful insights. Some users expressed a desire for more variety and customization options. Despite this, the overall sentiment is positive, making it a solid choice for those seeking effective email templates."

Visit product View on AppSumoOct 17, 2022

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Actual performance

17reviews

Did not reach leaderboard

Traction signals

Rating3.7 / 5

Purchases23,518

Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

73%73% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

AppSumoStrong fit for a featured deal · Strong signals: users · Missing: plus, platform, intuitive

67%67% predicted probability of success on AppSumo, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntOn track for Day 1 leaderboard · Strong signals: user, email · Missing: mac, agents, macos

51%51% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: lua, io · Missing: https docs, excited, just released

37%37% predicted probability of success on Hacker News, based on ML models trained on real launch data.

TrustMRRLess likely to generate early MRR · Strong signals: users · Missing: mobile apps, ios, personal

32%32% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Missing: arr, mrr, revenue

19%19% 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, crypto

17%17% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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