FormDesigner

AppSumo

FormDesigner is praised for its robust form building features, responsive customer service, and wide range of integrations. Some users have reported minor challenges with embed codes and interface design. With an overall rating of 4.8 and positive feedback, FormDesigner is a reliable choice for anyone seeking a versatile form creation tool. Given the 60-day money-back guarantee, it's definitely worth trying out FormDesigner.

Visit product View on AppSumoDec 13, 2021

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Actual performance

42reviews

Did not reach leaderboard

Traction signals

Rating4.9 / 5

Purchases1,114

Launch Intel predictions

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

84%84% 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, code · 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

53%53% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

47%47% predicted probability of success on TrustMRR, 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

39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.

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

14%14% 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

5%5% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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