aDirectory

AppSumo

aDirectory has received positive feedback from customers for its solid functionality, responsive customer support, and user-friendly interface. While some users initially faced activation and compatibility challenges, the overall consensus is positive. With a 4.0 overall rating from 33 reviews, aDirectory is a solid choice for those seeking a feature-rich directory plugin. Additionally, with a 60-day money-back guarantee, it's definitely worth giving it a try.

Visit product View on AppSumoJan 13, 2025

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Actual performance

36reviews

Did not reach leaderboard

Traction signals

Rating4.1 / 5

Purchases1,078

Launch Intel predictions

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AppSumoStrong fit for a featured deal · Strong signals: reviews, friendly, interface · 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.

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

71%71% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

56%56% 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

35%35% 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

28%28% 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

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

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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