Daze – Online returns from your home

Hacker News

Disclaimer: we’re currently live in Finland (Helsinki, Espoo, Vantaa). We hope to open other cities soon. Hi HN! Both me and my co-founder have had some terrible returns experiences in the past. There’s little to no customer interface in online returns. You have to do variations of: Finding boxes, taping, printing labels, Googling obscure logistics partners and commuting to a dropoff and back. Why is this? Well, you can’t do reverse logistics through forward logistics infrastructure. Especially the ”first-mile” part of the return journey is traditionally viewed as a cost sink rather than a value driver. Daze picks up returns from your front door. We handle everything from that point onwards. You don’t even need packaging or labels. You'll receive tracking information as soon as we have shipped the item. Let me know what you think, whether you’re in Finland or not. Happy to hear from potential issues you encounter. And those of you in or near Helsinki, you can use promo code HN50 at checkout for -50% off. Try us out!

Visit product View on Hacker NewsNov 22, 2023

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Did not reach leaderboard

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

63%63% 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 · Strong signals: interface, soon · Missing: plus, platform, intuitive

47%47% 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

36%36% 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 · Strong signals: code, open · Missing: mac, agents, macos

33%33% 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

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

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

15%15% 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

2%2% predicted probability of success on BetaList, based on ML models trained on real launch data.

Correct prediction on native model

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