Sprouted – Intimately know your users

Hacker News

Hi HN! I'm Erik, founder of Sprouted (https://sprouted.app). We make it easy for startups and growing companies to intimately know their users & customers. Our API can be used to populate Sprouted with user data and user events. Examples of user events that may lead to striking up a conversation are plan upgrades, discovery of new features, abandonment, inviting co-workers, and customer support questions. These conversations lead to better understanding the customer's needs which results in a better product and less churn. Once in Sprouted, each user is represented as a timeline of events. Every event can be used as a trigger to start a conversation via email. Sprouted is half API, half user interface. Its API is out in the open, down to `curl` examples right in the UI to make it easy for developers to start integrating with Sprouted. The user interface is fast and nimble. Concerning privacy and the understandable hesitation to share your customer data with Sprouted: we offer a unique way of dealing with this. You can encrypt the user's name on your server before sending it to the Sprouted API. You don't give the key to us, but instead you enter this key in the Sprouted browser extension which performs client-side decryption of customer data on every page change. I got the idea for Sprouted after mentoring dozens of startups who could not afford expensive user management solutions nor wanted to send plaintext customer info to databases they don't own. Please let me know if you have questions or feedback! Thanks!

Visit product View on Hacker NewsFeb 26, 2020

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Product HuntOn track for Day 1 leaderboard · Strong signals: user, new, email · 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 · 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: ide, io · Missing: https docs, excited, just released

56%56% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

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

45%45% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Strong signals: interface, users · Missing: plus, platform, intuitive

42%42% 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

14%14% 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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