Hyper-personalized, built-for-one user walkthroughs

Hacker News

Hey HN, I'm the founder of CommandBar and today we launched Magic Tour Links, which are a way to send hyper-personalized walkthroughs to individuals users. The premise of our company is that most tools that aim to help users are actually annoying (even if they lead to sugar-high-rushes on engagement metrics). We’ve all experienced pop-up hell on websites. CHECK OUT THIS NEW FEATURE followed by DO YOU HAVE FEEDBACK FOR US. Some ways we're seeing people use these tour links: (1) Embed them in empty state text. When users are first exploring, let them opt into a tour of the place. Like a welcome “Come on in” door mat that can talk. (2) Create them for sales prospects “Hey, you know that feature you said you were super excited about: try it here” (nice thing about this vs. a standalone loom is that it gets them back in the product). (3) Create “Help me” buttons on your most confusing pages that trigger the tour. Wait, you probably don’t have confusing pages. Ignore this one. (4) Include them in your drip campaigns and changelog entries. “Show, don’t tell” — your 6th grade english teacher and me

Visit product View on Hacker NewsAug 9, 2023

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

92%92% 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

87%87% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.

Hacker NewsStrong engagement from HN community · Strong signals: excited · Missing: https docs, just released, exist

59%59% 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: users · Missing: plus, platform, intuitive

49%49% predicted probability of success on AppSumo, based on ML models trained on real launch data.

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

44%44% 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

17%17% 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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