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TargetBite, Automating Customer Interviews

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TargetBite, Automating Customer Interviews

Hey fellow Hackers, I would love to get your valuable feedback on my latest project. Introducing TargetBite.com, a powerful tool designed for websites that offers a free, user-friendly, and fully customizable widget. This widget allows website users to easily opt-in for engaging in one-on-one customer interviews, and as a token of appreciation, they receive an Amazon Gift. With TargetBite, companies can create targeted campaigns to uncover valuable insights and validate their assumptions. Some examples of campaign topics include Product Pricing, Post COVID needs, Competitor opinions, and Future expectations. The campaigns on TargetBite can be directed towards specific segments of users who have opted-in, such as paying customers and monthly active users. This ensures that the feedback received is highly relevant and actionable. I'm currently in the process of building the Minimum Viable Product (MVP) for TargetBite. I would greatly appreciate hearing your thoughts on this product. Your feedback will be instrumental in shaping its development. Thank you!

Visit productView on Hacker NewsMay 21, 2023
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Launch Intel predictions

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Indie HackersFits the IH revenue-focused audience · Missing: supports, reddit linkedin, podcasting
88%88% 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
85%85% predicted probability of success on Product Hunt, based on ML models trained on real launch data.
AppSumoStrong fit for a featured deal · Strong signals: friendly, users · Missing: plus, platform, intuitive
51%51% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, monthly, users · Missing: mobile apps, ios, personal
39%39% predicted probability of success on TrustMRR, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: lua, io · Missing: https docs, excited, just released
36%36% 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 · Strong signals: active · 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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