We Built Our Own Simple CRM Tool

Hacker News

After my son and I struggled with the complexity and costs of existing CRM solutions, we decided to develop our own tool. Our goal was to create a simple, customizable, and cost-effective CRM system that is precisely tailored to the needs of our small business. The result of our efforts is a CRM tool that effectively covers the core functionalities: It stores contact details, tracks interactions, and assists in creating simple reports. The user interface is deliberately kept simple to enable intuitive use. Check out our CRM on our website and try it out for yourself. https://demo.cybx-crm.com E-Mail: demo@cybx-crm.com Password: Demo#24StartNow! We appreciate every piece of feedback. If you try out our CRM, please let us know what we can improve. Please be aware that you should not enter any real data, as other users also have access to the demo.

Visit product View on Hacker NewsApr 26, 2024

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Actual performance

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Launch Intel predictions

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AppSumoStrong fit for a featured deal · Strong signals: intuitive, interface, users · Missing: plus, platform, reviews

70%70% predicted probability of success on AppSumo, based on ML models trained on real launch data.

best fitHighest predicted score across all platforms for this description.

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

60%60% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

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

47%47% 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: user · Missing: mac, agents, macos

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

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

36%36% 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

18%18% 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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