WP Login Lockdown

AppSumo

WP Login Lockdown has received positive feedback for its robust security features, user-friendly interface, and responsive support. While some users have reported minor issues with licensing limitations and support response times, the overall positive experience overshadows these concerns. With an impressive 4.7 overall rating and a 60-day money-back guarantee, WP Login Lockdown is a solid choice for those seeking top-notch WordPress security.

Visit product View on AppSumoDec 4, 2023

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Actual performance

37reviews

Did not reach leaderboard

Traction signals

Rating4.8 / 5

Purchases3,609

Launch Intel predictions

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

85%85% 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

66%66% 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

64%64% 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

40%40% predicted probability of success on TrustMRR, 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

34%34% 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

12%12% 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

11%11% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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