ShopMe – Combine all of your e-commerce wishlists into one

Hacker News

Hey everyone! My name is Jerome and I'm the Founder of ShopMe (www.getshopme.com). Originally, ShopMe was an app that let you scan items in physical stores and it would tell you if you can find that item somewhere else more cheaply. After some feedback though, we found out that the biggest problem of our target users is managing their wishlists. 74% of people who buy online maintain some kind of wishlist to keep track of things that they want to buy but they end up creating too many lists across multiple sites. There's no easy way for them to consolidate it so they forget items and lose out on deals. We (re)built ShopMe to address just that. You can create a universal wishlist - add individual items via product links or barcodes and even import WHOLE wishlists from most major ecommerce sites (just make sure they're public). Get price drop notifications and when you're ready to order, just put it in your ShopMe cart and order with one click (even with multiple items across different sites). We were in private beta for a few months but we just launched on Product Hunt. If you're the type that has multiple wishlists, do try it out!

Visit product View on Hacker NewsOct 1, 2022

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

85%85% 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.

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

66%66% 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

50%50% 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: month, users, way · Missing: mobile apps, ios, personal

40%40% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: users · 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 · 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

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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