- Segment customer data
- Perform sentiment analysis
- Perform intent analysis
- Suggest contextual responses
- Route incoming leads
Basically the text analytics concepts will help to categorize the text data into some ore-defined categories and tag them accordingly to be used for the above use cases.
Data cleansing and text preprocessing have to be performed before applying the text classification. It normally involves replacing special characters and punctuation marks with spaces, normalizing case, removing duplicate characters, removing user-defined or built-in stop-words and word stemming.
For binary classification cases such as sentiment analysis, where you may want to tag an incoming email from the customer as either positive or negative, Two-Class Logistic Regression or Two-Class Support Vector Machine algorithms can be tried.
Based on our experience at Kreato on applying ML techniques, we see that Text analytics or automatic text classification can be utilized on CRM for the below use cases.
To improve accuracy, we could better validate the result yielded with at least two algorithms for the use cases that we would like to try.