DivMagic

AppSumo

DivMagic has received positive feedback for its seamless functionality, time-saving capabilities, and responsive customer support. Some users have reported minor issues with accuracy and integration, but the overall consensus is positive. With an impressive 4.4 rating from 40 reviews, DivMagic offers a 60-day money-back guarantee, making it a worthwhile investment for those seeking a reliable web design tool.

Visit product View on AppSumoJan 11, 2024

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Actual performance

40reviews

Did not reach leaderboard

Traction signals

Rating4.5 / 5

Purchases3,492

Launch Intel predictions

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

89%89% predicted probability of success on AppSumo, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

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

Product HuntUnlikely to reach the leaderboard · Strong signals: user · Missing: mac, agents, macos

49%49% 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: io · Missing: https docs, excited, just released

46%46% 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

37%37% 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

13%13% 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

8%8% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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