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Themes and Plugins for Zendesk

Hacker News

Themes and Plugins for Zendesk

We have been creating themes and plugins for Zendesk Help-Centers at http://www.diziana.com and http://www.zendesktheme.com. I wanted to seek some feedback on the idea and current business model. We offer bunch of themes and a few plugins for free (attribution). We see a lot of free downloads, and only a few premium sales. I would like to know if we can tweak business model, does yearly subscription work for this kind of stuff? We are working to improve our themes, create plugins and apps to solve problems and fill gaps. Any feedback would be appreciated. Thank you. Disclaimer: I work for company behind Diziana.

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2points
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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
83%83% 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
63%63% 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: model, apps · Missing: mac, agents, macos
63%63% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: apps · Missing: mobile apps, ios, personal
51%51% predicted probability of success on TrustMRR, 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: subscription · 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
1%1% predicted probability of success on BetaList, based on ML models trained on real launch data.

Incorrect prediction on native model

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