Score your e-com product pages on appeal

Hacker News

I built a little tool which integrates with Shopify to rate your product pages on appeal. The logic is as follows - people associate certain ideas with certain keywords. E.g., 'travel' is strongly associated with 'passport'. This tool looks at your product title, finds strongly associated ideas and scores your product description on whether those ideas are referenced or not. link = https://apps.chicshop.in/shopify-app?localStore=chicshop-dev-store.myshopify.com I'd love HN's thoughts on whether this idea has legs. What you do differently if you were in this space?

Visit product View on Hacker NewsJul 22, 2019

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Actual performance

3points

Did not reach leaderboard

Launch Intel predictions

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

51%51% 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.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

39%39% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

33%33% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

31%31% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

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

29%29% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

22%22% 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

0%0% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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