MonkeyLearn

Analyze customer feedback with ease using a no-code, AI-powered text analytics tool that offers instant insights and customizable visualizations.
Text Analytics No-Code Machine Learning Customer Feedback Analysis

MonkeyLearn is a no-code text analytics tool that makes it easier to process, label and visualize customer feedback with the latest artificial intelligence technology. It's an integrated text analysis and data visualization studio that offers immediate insights into your data so you can drill down into the details.

Among its features are the ability to create custom charts and visualizations on the fly, combine and filter data from multiple input sources like dates and custom fields. The service includes prebuilt and custom machine learning models you can use to categorize text into predefined categories or to extract specific information. You can also create and train your own models without having to write any programming code.

MonkeyLearn also comes with business templates for specific use cases, including sentiment analysis, entity extraction and topic classification. The templates include prebuilt models and dashboards so you can upload data and get results immediately. The service can be connected to apps and BI tools with native integration, SQL connections or APIs.

Among the services that integrate with MonkeyLearn are Zendesk, Freshdesk, SurveyMonkey, Google Sheets and Zapier, which offers connections to hundreds of other apps. That means you can easily integrate MonkeyLearn with your business workflow for customer support, surveys, productivity and communications.

With MonkeyLearn, businesses can automate text analysis and get more control over their data. One company, Freshly, found MonkeyLearn useful for analyzing data without having to rely on its data team. Another, MoxiWorks, used MonkeyLearn to automate ticket routing and create custom responses to high-impact issues.

Pricing isn't disclosed, but you can start with a free account to try MonkeyLearn's features and features.

Published on June 14, 2024

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