Ne

Nectar, Survey that can ask deep followup questions

Hacker News

Nectar, Survey that can ask deep followup questions

Hi everyone, Shri and Robin here, cofounders of Nectar. Excited to share our v1: surveys that ask smart followup questions, with zero effort. Here’s how it works: 1. You give us your email address and one sentence that describes what you’d like to test, i.e. your hypothesis 2. You get a personal survey link sent to your email within 3 hours that you can review and share with others. This survey will include a opener question and three followups (that will be dynamically created based on previous responses. 3. Get survey responses in real-time sent to the same email address. Why we decided to built this: As a former Product Manager and Designer, and now both of us founders we have struggled with user research, both the time it takes and the poor quality of insights. There are two main workflows we had: 1) sending out surveys, and 2) conducting user interviews. 1) Our problems using surveys: they take a long time to create (and even more with any complex logic), they typically only return high level/quantitative data, and their completion rates are low — nobody likes to fill them out, they are irrelevant and static. 2) Our problems with user interviews: scheduling takes a lot of time (not everyone can offer that level of time commitment), and conducting the interview itself is so so hard. We’ve consumed every book, article, or podcast on how to get better at this, and yet fail all the time. Just last week while listening to an interview I realized just how many missed opportunities I had to dig deeper into what was said. I was prob nervous and decided to follow the script instead. The outcome of all of this: inability to make fast confident decisions, which can be a killer for most early stage startups, including ours (current runway is May). Our solution: We decided to build a tool that can ask smart followup questions, dive deeper into what the responder said, and create for them a more personal and engaging user experience. In fact we’re using it ourselves. And our completion rate so far is 90%. How to get started: - The first 20 people get to use our service for free, which includes 200 responses for the first month - If you’d like us to build around your use case (not just discovery), email us here: robin@nectar.run - To join our beta go here: https://nectar.run - To see a demo go here: https://survey-app-neon.vercel.app/survey/2a564248e4ea408481... We’re super curious to hear what you think. Feedback welcome!

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Actual performance

6points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Indie HackersFits the IH revenue-focused audience · Strong signals: created, started, including · Missing: supports, reddit linkedin, podcasting
94%94% 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, email, using · Missing: mac, agents, macos
85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
Hacker NewsStrong engagement from HN community · Strong signals: excited, ide, io · Missing: https docs, just released, exist
58%58% 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 · Missing: plus, platform, intuitive
46%46% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: personal, month, way · Missing: mobile apps, ios, entrepreneurs
42%42% 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
11%11% predicted probability of success on Acquire.com, based on ML models trained on real launch data.
BetaListMay not resonate with beta-testers · Strong signals: smart · Missing: web3, chat, crypto
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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