Affiliatable

AppSumo

Customers appreciate Affiliatable for its user-friendly interface, customizable templates, and time-saving integrations. Some users have expressed concerns about the post limits and the need for more up-to-date tutorials. However, with an overall rating of 4.9 and positive feedback, Affiliatable is a solid buy for those in need of high-converting product comparison tables and review boxes. With a 60-day money-back guarantee, it's worth giving it a try.

Visit product View on AppSumoMar 13, 2025

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Actual performance

62reviews

Made the leaderboard

Traction signals

Rating4.9 / 5

Purchases326

Launch Intel predictions

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

83%83% 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

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

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

32%32% 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

28%28% 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

19%19% 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

1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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