Does your organization deal with vast quantities of text data? Is your organization always seeking to answer these key questions?
b. What are their pain points in decision making?
c. What drivers improve customer satisfaction?
In the past, we would have an army of analysts go through customer comment, but that work is manual and tedious. It also introduces bias into the results; different analysts come up with different insights. Depending on the volume of text data that your organization is dealing with, this process can end up consuming months of your time.
At MAQ Software, we use NLP algorithms to mine text data and surface the hidden insights and trends for businesses to act upon. Since these insights are generated via unsupervised techniques, there is minimal maintenance cost with no human dependency or biases.
1. Unsupervised engine automates the review of customer feedback leveraging pre-trained ML models
2. Identify how sentiment changes upon new product launches
3. Generate topics and insights for any unstructured text dataset
4. Leverage pre-trained ML models to generate topics and insights for unstructured text datasets
5. Define new topics and keywords for your unstructured text data set or edit existing topics
6. Visualize keyword and topic distribution, and their individual contributions to the text
7. Export the insights generated as a PDF