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Support widget to help your product get better customer feedback

Hacker News

Support widget to help your product get better customer feedback

We’re so excited to share our product today. About 6 months ago, we launched our first version and since then we have been working on the concept to merge Feedback and Tickets (Fee - kets) together and bring the best of both and today we are about to launch it. *Problem * When it comes to helping your customers we are left with options like Contact Form, Email Support with help desk integration or ChatBots, or Help Articles. When you want your product to be scalable you will want something more than just having a help desk solution for replying to all the queries either through Emails or Live Chat. Most of the Issues raised are always repeatable. It is also not possible to write all separate articles for all the common issues that happen on your platform. *Solution * With Due.work we have merged the concept of Feedback and Tickets (Fee - Kets). Using Due.work a ticket can be raised in form of private feedback which can be later made public by admin to let other users solve the same problems themselves or comment on the same issue if they need further clarification. Slowly your roadmap with public feedbacks starts building along with your community. *How we integrate with your product?* Due.work provides a non-distracting widget that lives within your product through which your customer can create Tickets, Feedbacks, or read Self-Help articles. With Due.work you will eventually have a community of your own product through which your customer can do several things like subscribing to an issue to sharing feedback or raising a ticket and all of this will be achieved within your product through our widget. We have tried to create the most convenient and enjoyable way to create a great customer support & experience tool and would love to hear your feedback on how we can make it better.

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Actual performance

3points
Did not reach leaderboard

Launch Intel predictions

Analyze your own launch →
Product HuntOn track for Day 1 leaderboard · Strong signals: user, email, using · Missing: mac, agents, macos
79%79% 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 · Strong signals: para · Missing: supports, reddit linkedin, podcasting
77%77% predicted probability of success on Indie Hackers, based on ML models trained on real launch data.
Hacker NewsMay not resonate with HN audience · Strong signals: excited, ide, io · Missing: https docs, just released, exist
48%48% 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: platform, users · Missing: plus, intuitive, reviews
45%45% predicted probability of success on AppSumo, based on ML models trained on real launch data.
TrustMRRLess likely to generate early MRR · Strong signals: month, users, way · Missing: mobile apps, ios, personal
35%35% 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
14%14% 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.

Correct prediction on native model

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