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GuideLab – Guide your users through their entire journey

Hacker News

GuideLab – Guide your users through their entire journey

Hi HN, I’m James and I’m excited to share GuideLab, on-demand in-app guides to reduce support load and make your users happier. After a decade of working on products with small (20k) and large (millions) user bases, one thing remained clear - level 1 support takes up a lot of time. From universal usability (’how do I reset my password?’, ’how do I invite team members?’) to product specific problems, the answer is sometimes in a chat bot or knowledge base, but it’s cumbersome for the user and often gets lost in translation. Using GuideLab, you create in-app guides (think a chain of tooltips) which can then be shared with your users via a link (yourapp.com/?guidelab=xxx-xxx-xxx) over any platform: email, Zendesk, social media, pre-existing knowledge base, Intercom etc. As soon as a user clicks the link, they’re taken to your app and instantly see the guide. There’s also our search widget that you can embed directly in your web app; your users can then click the ‘Help Guides’ tab, search and follow any guide you’ve created. In that way GuideLab is fundamentally different to other guiding software like AppCues or userpilot. They focus on opting users into guides based on cohorts/attributes whereas GuideLab lets users view guides when they need them most. There’s a quick (<1m) video on the homepage ( https://guidelab.io ) walking you through how GuideLab works. If you have any other questions, or if there’s anything you’d love to see in this space, please share (there’s a heap of product growth ahead for GuideLab… its a very exciting time!).

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, email, using · Missing: mac, agents, macos
89%89% 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 · Strong signals: created · Missing: supports, reddit linkedin, podcasting
84%84% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
TrustMRRFits verified-revenue profile · Strong signals: video, users, way · Missing: mobile apps, ios, personal
65%65% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, exist, existing · Missing: https docs, just released, lua
62%62% 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, soon, users · Missing: plus, intuitive, reviews
39%39% predicted probability of success on AppSumo, based on ML models trained on real launch data.
Acquire.comPre-revenue stage for this audience · Strong signals: growth · Missing: arr, mrr, revenue
13%13% 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.

Incorrect prediction on native model

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