I combined customer support and customer success tools for PLG SaaS

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I’m an indie hacker who previously built a customer communication platform specifically for travel agencies. Initially, I couldn’t rely on existing live chat software to support my product because it already included a built-in live chat feature (even though it was meant for travel agencies at first). Over time, I started building tools to solve my own problems. Every weekend, I’d hack together a new feature—not just to make my product better, but also to scratch that coding itch (yeah, I know, classic indie hacker move). What started as small experiments snowballed into building KnowledgeBase Portals, Feature Request Boards, Bug Report Boards, Changelogs, Email Drip Campaigns, Product Tours, Checklists, NPS tools, and more. The funny part? Travel agencies didn’t really care about all these extra tools—they didn’t need them. I was the only user who actually used everything I built. For the past few weeks, I’ve been reworking the positioning of my product—shifting from targeting travel agencies to SaaS companies. I also rolled out a freemium version to lower the barrier for early adopters. Being a solo indie hacker, I obviously can’t compete head-to-head with giants like Intercom. That’s why I’m here, hoping to get feedback from other SaaS founders. Does this new direction make sense? Should I keep doubling down on it, or am I missing something? PS: My current paying customers are still travel agencies. About a year ago, I ran a lifetime deal (LTD) hoping to break into a new niche—like e-commerce or marketing agencies—but it didn’t pan out the way I’d hoped.

Visit product View on Hacker NewsDec 13, 2024

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

81%81% 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: user, new, email · Missing: mac, agents, macos

76%76% 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: exist, existing, io · Missing: https docs, excited, just released

39%39% 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 · Strong signals: platform · Missing: plus, intuitive, reviews

30%30% 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

27%27% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

Acquire.comPre-revenue stage for this audience · Strong signals: arr, saas · Missing: mrr, revenue, profit

22%22% predicted probability of success on Acquire.com, based on ML models trained on real launch data.

BetaListMay not resonate with beta-testers · Strong signals: chat · Missing: web3, crypto, cryptocurrency

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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