An AI that pings you on your phone when it finds customers for you

Hacker News

Because I struggled with consistency in my marketing efforts, I wrote a new tool that sends a "ping" to my mobile phone whenever some people on the Net are discussing the problem I solve in my business. This "ping" is now the main trigger for me to jump into the conversation they are having, and help them solve their problem. The most ethical form of marketing that I know. CustomerPing solves the consistency problem elegantly and sends new potential customers your way, every day. People enjoy your help, begin to trust you, and will eventually buy your products. How it works: - CustomerPing listens to RSS feeds (e.g. from Reddit, Mastodon, etc.) and runs the articles through a LLM that ranks each article vs. the relevance criteria that you specified. - This means that you get only relevant articles where a future customer of yours is in pain. The AI filters out all the bragging, know-it-all's, "read my blog post"s, etc. - It publishes the filtered articles via RSS again, each with an AI-generated summary and a relevance-percentage between 0 and 100%. It also contains a simple keyword filter so that the AI doesn't have to deal with totally off-topic stuff. - The output is a new RSS feed that you can subscribe on your phone, in Slack, or whatever other tool you want. Try this huge time-saver as a customer development tool for your startup as well, and give me some feedback about it. Every new user gets 200 articles rated for free, so you can calmly try it out. Click this link to see it: [CustomerPing.ai]( https://customerping.ai ) – so curious what you'll say! Matthias Founder, CustomerPing

Visit product View on Hacker NewsSep 25, 2023

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Product HuntOn track for Day 1 leaderboard · Strong signals: slack, user, new · Missing: mac, agents, macos

82%82% predicted probability of success on Product Hunt, 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

41%41% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

30%30% 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

21%21% 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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