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FounderPhone – make customer support personal with SMS

Hacker News

FounderPhone – make customer support personal with SMS

Hi HN! We're Parthi and Kunal from FounderPhone ( https://founderphone.com ). FounderPhone is a shared customer support inbox for SMS and calls in Slack. We've built and shipped 7 products recently and if there's one thing we've learned, it's that your personal relationship with your customer is your secret weapon as a startup. Having customers email support@company.com or message a bot via Intercom doesn't feel personal. People are skeptical they will ever get a response. We've had a lot of success giving out our phone number to the top customers we really care about and telling them to text us whenever something comes up. Apparently, lots of great founders like Patrick from Stripe did this for their VIP customers while growing their startups. The problem is that a single SMS inbox isn't sustainable at scale. So we hacked together a solution for ourselves where we made Slack a shared inbox. When a customer texts or calls me, our team can also see the messages and incoming calls. We can discuss how best to handle the issue in Slack and then anyone can respond via text. For calls, anyone available can redirect calls to their own number. From the customer's perspective, they're just texting a single number. They're not frustrated with messy tickets or being routed to 3 different people. They will always read your responses because it's in their SMS inbox instead of being lost amongst their 20,000 unread emails. This is just the start! We're looking into building a whole suite of software to make customer support feel both personal and immediate. We're making an integration with Segment and Sentry to alert you when a customer has an issue so you can reach out to them about it before they complain to you. Text our FounderPhone (510) 756-2522 with your name or email founders@founderphone.com if you have any questions. Thanks for checking us out!

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Actual performance

106points
40comments
Made the leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: slack, stripe, inbox · Missing: mac, agents, macos
84%84% predicted probability of success on Product Hunt, 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
81%81% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: 000, io · Missing: https docs, excited, just released
65%65% predicted probability of success on Hacker News, based on ML models trained on real launch data.
nativeThis product was originally launched on this platform.
TrustMRRFits verified-revenue profile · Strong signals: personal, way · Missing: mobile apps, ios, entrepreneurs
51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
AppSumoMay struggle as an AppSumo deal · Strong signals: calls · Missing: plus, platform, intuitive
35%35% predicted probability of success on AppSumo, 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
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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