Roveb, a simple mobile loyalty program for businesses

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Hi guys, We launched! Checkout our startup, Roveb, a simple mobile loyalty program for small-medium businesses. Roveb will help businesses easily create a fast, simple and engaging mobile presence with built-in provision for deals, rewards and virality. Businesses can inventivize mobile visitors who participate with social sharing/recommendations and personal visits; creating brand awareness and new customers for the business. http://roveb.com (Invitation Code: SHOWHN) --- I'll really appreciate your feedback (both good and not so good). Your thoughts around these areas would be helpful too: 1. What is your initial impression of our landing page? What improvements would you suggest? 2. How can we improve the mobile loyalty program? Any features or ideas we should focus on more than other things? 3. What type of person or organization do you think would benefit most from Roveb? 4. Any marketing advice or suggestions are most welcome. 5. We'll be grateful if you could kindly spare a few minutes to browse through a Roveb-powered demo mobile site (please visit http://roveb.com) on your smartphone and share some brief feedback about its performance, looks and usability. Cheers.

Visit product View on Hacker NewsFeb 1, 2012

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Actual performance

3points

4comments

Did not reach leaderboard

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

65%65% 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: new, code · Missing: mac, agents, macos

62%62% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

AppSumoStrong fit for a featured deal · Missing: plus, platform, intuitive

61%61% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

44%44% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

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

24%24% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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 · Strong signals: reward, smart · 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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