There is a lot of written text out there that pertains to your business and needs to be catalogued or analyzed. It might be emails, reviews, support tickets, comments on social media, feedback, news articles, etc.
Working through a high volume of text data can be challenging for a team, requiring a lot of time for manual and repetitive tasks, and with a high degree of errors.
MonkeyLearn is built to allow you and your team to analyze this overflow of data automatically. This translates into responding to clients more quickly, detecting problems before they snowball, and prioritizing your team's time to work on higher impact items.
Many of our clients are using MonkeyLearn in the following areas:
- Automatically routing new tickets to team members
- Detecting urgency and emotion in tickets, tweets, etc.
- Tagging tickets by category based on content
- Analyzing sentiment (positive / negative) in reviews or tweets
- Breaking down complex reviews into "opinion units" to categorize feedback by product or service
- Comparing product reviews with competitors to inform strategy
Sales & Marketing
- Analyzing news articles for mentions, topics or keywords
- Extracting data from documents or lists
- Detecting client interest in sales emails
How does text analysis with MonkeyLearn work?
MonkeyLearn offers access to machine learning models that analyzing incoming text and make predictions. Models can be trained to analyze sentiment, emotion, urgency, topics. For more on models, please see the "What is a model?" section in our FAQ.
Clients get the most out of MonkeyLearn by integrating models into the tools they currently use (see integrations). However, you don't need to be a technical profile to use MonkeyLearn. You can upload and download files so your data can be processed directly. We have even built an extension for Google Sheet and a suite of automated processes with Zapier.