Appsedia is a web-based analytics tool that turns social chatter into actionable insights.
We realized that there was a gap between what customers were looking for and the information they were actually getting from their apps. Our team decided to start building an app that would solve these problems and provide the right solutions.
Our core principles:
- Our vision and goal for Appsedia is Quality over Quantity. That's why we slowly grow our internal app library to ensure a high-quality result for our machine learning models.
- We strongly believe that happier customers equals more growth and revenue for our clients. By providing knowledge to our customers, they can invest their money in other areas to grow faster.
- Our philosophy is Your Success is our Success. Appsedia grows and becomes more accurate over time, which in turn helps our clients become even more successful.
One of our two main focuses at Appsedia is to better understand the app market. Knowing which apps users like, dislike, or generally show high interest in can be an important metric for any business decision. Appsedia measures several key metrics from app reviews and from the social media platforms, Twitter and Reddit. We turn these key metrics into our Social Health Score which can be further filtered and broken down.
In addition, the app analysis gives our clients a complete understanding of what is happening with their selected app and can compare it to up to 4 other apps on the market. Appsedia gives our customers the ability to perform app analytics that are normally only available to companies with their own data science team, giving them a competitive advantage.
Appsedia runs on the basis of two core functionalities:
1. Sentiment Analysis gives you the ability to know if your users are talking positively or negatively about an app. Understanding this and being able to dive deeper into each of these reviews opens up a whole new and deeper understanding of how an app is received in the market.
2. Natural Language Processing is a very complex challenge that Appsedia has to overcome. To match our customers' opinions and feelings about a review, they have the ability to delete, add, or change the sentiment and topic classification. Appsedia's machine learning algorithm learns about these changes over time to adjust newly incoming reviews and produce more accurate results.
Appsedia comes with a 14-day free trial, and no credit card is required.
Location: Vancouver, CanadaVisit Website Visit Twitter Page
AI User Research Software for SaaS
Collectif is an AI user research platform for SaaS that automates your research and feedback analysis. Discover insights buried in support tickets, feedback forms, sales calls, and more — in minutes, not days.
What types of data does Collectif analyze?
- support tickets,
- sales calls,
- deal reasons,
- churns reasons.
How does it work?
1. Connect the tools used across your company or upload interviews
2. Get automatic transcripts, topic & sentiment labels, summaries, and insights generated by GPT-4
3. Quickly discover issues and opportunities, or even easier — ask your data questions!
How are insights generated?
- Each piece of data is analyzed and turned into Highlights (synthesis of information).
- Highlights are assigned to pre-defined Topics, like specific features or more universal things like Pricing or Customer Support.
- Once a week, Collectif analyzes highlights, identifying Insights (recurring themes).
- Prioritize bug fixes based on frequency in support tickets.
- Speed up interview analysis with transcriptions, summaries, and auto-tagging.
- Identify usability issues mentioned in support tickets and feedback forms.
- Review sales call summaries to pinpoint product-related objections.
- Gain deeper insight into new users’ challenges thanks to sales call analysis.
- Organize your research and share with other teams with ease.
- Identify gaps in your customer education program and help users solve their problems.
- Assess the general experience using sentiment from reviews and feedback forms.
- Define messaging and information hierarchy based on topic frequency.
- Find feature-specific feedback and research in a few clicks.
- Use feedback from lost deals to influence product direction.
Location: Tallinn, EstoniaVisit Website Visit Twitter Page
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