Synthetic User Interviews

Hacker News

Recent research by Stanford and NYU shows that AI-generated responses correlate up to 85% with real human reactions in social science studies (link - https://docsend.com/view/qeeccuggec56k9hd ) We’ve seen similar results in our experiments at Askpot, where AI analytics often match expert-level human answers. That’s what inspired us to develop Synthetic User Interviews - tool that helps you run User Interviews almost instantly, saving months on recruiting respondents, creating questionnaire, conducting user interviews and analyzing results. How does it work? 1. Define the focus – Choose a product, audience segment, and interview goal. 2. Generate synthetic personas – We create realistic personas and craft a tailored questionnaire. 3. Analyze responses – Each persona provides AI-driven answers, and you get a detailed analysis of their responses.

Visit product View on Hacker NewsDec 13, 2024

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting

85%85% 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 · Missing: mac, agents, macos

74%74% predicted probability of success on Product Hunt, based on ML models trained on real launch data.

TrustMRRFits verified-revenue profile · Strong signals: month, answers · Missing: mobile apps, ios, personal

51%51% predicted probability of success on TrustMRR, based on ML models trained on real launch data.

AppSumoMay struggle as an AppSumo deal · Missing: plus, platform, intuitive

35%35% predicted probability of success on AppSumo, based on ML models trained on real launch data.

Hacker NewsMay not resonate with HN audience · Strong signals: ide, io · Missing: https docs, excited, just released

35%35% predicted probability of success on Hacker News, based on ML models trained on real launch data.

nativeThis product was originally launched on this platform.

Acquire.comPre-revenue stage for this audience · 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 · 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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