Proven.ly

AppSumo

Proven.ly has received positive feedback for its user-friendly interface, quick setup, and responsive customer support. Some users have noted the absence of certain customization options and occasional delays in support responses as minor drawbacks. With an overall rating of 4.6 and favorable feedback, Proven.ly appears to be a reliable option for those looking to enhance conversions and establish trust. Priced at $39.00 with a 60-day money-back guarantee, it's a product worth considering.

Visit product View on AppSumoJul 18, 2024

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Actual performance

28reviews

Did not reach leaderboard

Launch Intel predictions

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AppSumoStrong fit for a featured deal · Strong signals: friendly, occasional, interface · Missing: plus, platform, intuitive

81%81% 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 · Missing: mac, agents, macos

61%61% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

58%58% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

26%26% 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

24%24% 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

15%15% 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

14%14% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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