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A clean directory of affiliate programs

Hacker News

A clean directory of affiliate programs

I’ve noticed that many businesses struggle to find effective affiliate marketers, while affiliate marketers have a hard time discovering high-quality programs. To help with this, I created AffiliList, a directory that connects businesses with affiliate marketers. Businesses can submit their affiliate programs to the directory, and affiliates can browse curated listings to find programs that fit their niche. We’re currently offering free submissions for businesses as part of our early bird offer. I’d love to hear any feedback from the community on this initiative!

Visit product View on Hacker NewsJul 29, 2024

Actual performance

1points

Did not reach leaderboard

Launch Intel predictions

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

82%82% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

36%36% predicted probability of success on AppSumo, 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

21%21% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Product HuntUnlikely to reach the leaderboard · Missing: mac, agents, macos

20%20% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

16%16% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

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

15%15% predicted probability of success on TrustMRR, 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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