Uploaded comments are only retained for 2 weeks in MLY back-end before deletion, for support and quality assurance purposes only. These comments are stored on Microsoft Azure infrastructure in the U.S.

Your data is your data. Comments uploaded to MLY are not used for any purpose beyond the requested analysis. In addition, customer data is only used in training MLY if the customer has provided written consent.

MLY analyzes qualitative data and summarizes that data into quantitative feedback and insights such as sentiments, alerts, recommendations, and topics. Using Natural Language Processing (NLP), MLY can recognize hidden patterns and correlations in the data, cluster and classify them, and by processing more and more data continuously learn and improve.

Explorance uses a supervised machine learning approach to train MLY. This ensures MLY is only trained with comments that have been formally approved via an in-house blind annotation process. This process has three annotators work independently of one another, where the resulting comment is only approved if all three unanimously agree upon the interpretation.

MLY’s primary strengths are its specialized categorization and actionable insights. Built to understand the student and employee experience, the analysis results in more targeted and relevant insights with themes and terminology specific to the topic. Additionally, MLY enables the actionability of the insights through its Recommendations and Alerts models, providing a starting point for the most critical themes that arise from the data.

Feedback sources you can have analyzed by MLY include engagement surveys, course evaluations, performance reviews, experience surveys, peer reviews, program evaluations, social media, review websites, discussions forums, and much more.

During an analysis, each comment is given an alert score between 0 and 100. That score is compared to the threshold set for the alert scores, and any scores that are equal to or above the threshold will be displayed as an alert in the results. The default threshold is set at 50 but can be adjusted higher or lower to match your organization's policies, tolerance or culture concerning what comments should be reviewed for potential follow-up.

Multiple glossaries can be created to accommodate the use of specialized acronyms or abbreviations in different departments, faculties, locations, etc. and then applied to those comments.