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MARQ – Turn your product returns into a growth engine

Hacker News

MARQ – Turn your product returns into a growth engine

Hi HN! My name is Mark and I am the co-founder of MARQ (https://www.marqvision.com). Usually when online brands start scaling, reducing product returns becomes a priority. According to Shopify, e-commerce returns will cost an average online retailer more than 30% of its sales by 2020. That's why we thought retailers might be interested in finding out how MARQ can help reduce product returns and protect their products. MARQ is a startup founded by a small team of engineers. We built a software tool that automatically records the packing process, sends recorded videos to customers, and expedites the return dispute process. Taking videos of the packing process can prevent customers from filing false product returns or abusing generous return policy. Using MARQ, online brands can reduce product returns by 95% and resolve each return dispute in 30 seconds. We designed this software after we realized that online retailers are spending too much time and money dealing with product returns. We strongly believe in the benefits of sending packing videos to customers. We would love everyone, or more specifically, online retailers to give MARQ a try. (https://www.marqvision.com)

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
89%89% 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 NewsStrong engagement from HN community · Strong signals: ide, io · Missing: https docs, excited, just released
51%51% 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 HuntOn track for Day 1 leaderboard · Strong signals: using · Missing: mac, agents, macos
50%50% 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
37%37% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: video · Missing: mobile apps, ios, personal
27%27% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: growth, shopify · Missing: arr, mrr, revenue
23%23% 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.

Incorrect prediction on native model

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