Feedback Analysis
Can you trust AI when you don't understand the way it makes decisions? Or do you need control? Learn how AI and people can collaborate to get the best insights from feedback faster.
We understand if you have some concerns about how Thematic can work for you, but we’re keen to put your fears to rest! Here are some of the biggest concerns our prospective customers bring to us, and how we eliminate each and every one.
When you have a lot of text feedback, how do you begin to make sense of it all? One of the best approaches is thematic analysis.
Building a story from customer feedback is a critical skill you need to learn, especially if you're responsible for delivering insights across your company.
There's an avalanche of data out there, and it's fun for us here at Thematic to put our platform through its paces and see what turns up. In this case, we analyzed reviews of the Apple Music app on the Google Play store.
A game-changing system that product ops can (and should!) implement is a Voice of the Customer (VOC) program for the product team. Done right, a VOC program will improve decision-making, clarify priorities and validate product roadmaps.
Customer intelligence is crucial to building better products and experiences. It's the system of collecting and analyzing customer data to access the deep understanding and actionable insights your product team need.
When it comes to analyzing feedback, AI can be your best friend. But can you trust text analytics software if you don’t understand how it works?
An essential part of product development, product validation utilizes feedback analysis to eliminate dead ends and false starts. Here’s what you need to know.
Product reviews are one of the most comprehensive and useful sources of insider info available, anywhere. These often have gold nuggets that can guide development, troubleshoot new initiatives, and improve customer experience. They provide a helpful benchmark to compare your offerings to the competition, showing both your strong points and
When we conduct research, need to explain changes in metrics or understand people's opinions, we always turn to qualitative data. Qualitative data is typically generated through: * Interview transcripts * Surveys with open-ended questions * Contact center transcripts * Texts and documents * Audio and video recordings * Observational notes Compared to quantitative data, which captures
NPS, CSAT, CES...it doesn’t matter how many letters you throw out there or how many customer experience (CX) scores you get. If you don’t know what’s driving your CX metrics, it’s hard to replicate success, let alone find ways to improve. Through comments in customer