Afficone

AppSumo

Customers appreciate Afficone for its user-friendly interface, responsive support, and easy setup for Stripe, Shopify, and WooCommerce. While there have been some reports of slow support response and a lack of documentation, these are minor issues. Overall, Afficone is a solid choice for those seeking all-in-one affiliate software, especially considering its refund policy and 60-day money-back guarantee.

Visit product View on AppSumoJul 23, 2024

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Actual performance

17reviews

Did not reach leaderboard

Traction signals

Rating2.1 / 5

Purchases722

Launch Intel predictions

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

67%67% 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

65%65% 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, stripe · Missing: mac, agents, macos

62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

49%49% 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

24%24% predicted probability of success on Hacker News, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Missing: web3, chat, crypto

18%18% predicted probability of success on BetaList, based on ML models trained on real launch data.

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

17%17% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

Incorrect prediction on native model

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