Feedby

Filter user feedback from your comment section

Usage Based

Description

Feedby offers a solution for content creators overwhelmed by numerous comments on their YouTube videos. The platform utilizes artificial intelligence to sift through thousands of comments, distinguishing meaningful user insights from irrelevant noise. It efficiently identifies and categorizes user feedback, questions, bug reports, and feature requests.

This service enables creators to easily access consolidated insights directly in their inbox, transforming their comment sections into a prime source for understanding audience sentiment and product improvement ideas. By automating the comment analysis process, Feedby helps users focus on building content and products that resonate with their audience's needs and preferences.

Key Features

AI-Powered Comment Filtering: Automatically filters YouTube comment sections to remove meaningless or unhelpful comments.
Feedback Extraction: Identifies and extracts valuable user feedback and insights from comments.
Question Identification: Pinpoints user questions within comment threads for easy response or FAQ generation.
Bug Report Detection: Detects and gathers bug reports mentioned by users in the comments for product improvement.
Feature Request Collection: Collects feature requests and suggestions directly from the user comment base to guide development.
Inbox Delivery: Delivers curated user feedback, questions, bug reports, and feature requests directly to the user's inbox.
User Sentiment Indication: Helps understand how users feel about video content through the analysis of their comments.

Use Cases

Analyzing user feedback on YouTube videos to understand audience reception.
Identifying common user questions from comment sections.
Collecting bug reports mentioned in comments for software or product improvement.
Gathering feature requests from users to guide product development.
Streamlining community management by focusing on relevant comments.
Improving content strategy based on direct user feedback and sentiment.