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Anonymous Feedback Tool for Teams

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Anonymous Feedback Tool for Teams

Hey there! Signals is a survey system that collects feedback from staff (mostly) but also clients and stakeholders in a business. It does this via SMS on a weekly schedule (the cadence can be changed but it works best when done weekly). My co-founder and I started working on this nearly a year ago, having run similar small builds for over four years. This time we’ve tried to do it properly. The execution is relatively simple - similar to an eNPS (Net Promoter Score - a common way of measuring how consumers like your product or service), but we wanted a way to anonymously pass candid feedback from employees to managers and executives about their workplace experience, their jobs, new ideas... anything. And for it to be regular and easy to do. We maintain the anonymity of the staff member (and let them know how many people are in the team receiving the question being asked, so they understand their level of safety). We show the team leads and managers a sentiment score and verbatim comments), but we do not associate the respondent details with those in the backend. This is somewhat different to currently existing tools that tend to use the term ‘confidential’, which means you're not anonymous if the admin permissions are high enough. Even those systems which claim to be anonymous can often have ways of twisting the data to unmask the users. One of our team is working on natural language processing to understand, summarise and report on sentiment, comment themes and trends. We’re making it easy to add AMAs, poll clients and partner businesses, and we’re experimenting with sports organisations, unions, and within schools and education. It runs over SMS (the highest response rate of any method we tested). Unfortunately, it does need a signup and confirmation (apologies) to try, but it’s free to test, and there’s no credit card required. Unfortunately, we only support the US, Canada, Australia and the U.K. at the moment but are looking to expand support as soon as possible. We have only really been live for eight weeks now. We would love any feedback you have for us and hope you find it useful! You can email us at hello@runsignals.com if you have feedback or want to chat.

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Indie HackersFits the IH revenue-focused audience · Strong signals: started · Missing: supports, reddit linkedin, podcasting
89%89% 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
58%58% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: users, way, education · Missing: mobile apps, ios, personal
46%46% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: exist, existing, ide · Missing: https docs, excited, just released
45%45% 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: soon, users · Missing: plus, platform, intuitive
39%39% 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
17%17% 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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