Welcome to MLY
Overview
MLY is a revolutionary advancement in the field of feedback analytics. Pronounced as "mi-lee", MLY represents the intersection of machine learning (ML) and the pursuit of answers (Y), where AI-powered feedback analysis becomes a powerful tool to understand and amplify the Voices of Employees and Students in Learning & Development (L&D), Human Resources (HR), and Higher Education (HE) domains.
MLY enables organizations to gather and interpret text feedback (comments) from students and employees throughout their academic and employment journeys. The analysis of these comments reveals key insights that empower leaders to address challenges in areas such as engagement, inclusion, performance, attrition, learning enhancement, organizational agility, etc.
Customers have shared valuable insights into the advantages of using MLY, including:
- Time-saving efficiency: MLY processes thousands of comments in just minutes, dramatically reducing analysis time.
- Uncovering hidden insights: It helps organizations identify valuable perspectives they may have previously overlooked.
- Driving impactful decisions: These insights empower teams to develop action plans that enhance performance across their organization.
MLY produces insights using the following analysis types:
| Analysis type | Name | Includes | Includes | Includes |
|---|---|---|---|---|
| EEI | Employee Experience Intelligence | Sentiments | Recommendations | Alerts |
| ELI | Employee Learning Intelligence | Sentiments | Recommendations | Alerts |
| SEI | Student Experience Intelligence | Sentiments | Recommendations | Alerts |
Each model focuses on a specific set of themes and topics. To learn more: Introduction to insights
Introduction to analyzing feedback
Text comments provide some of the most valuable feedback available to academic and business leaders. MLY analyzes this feedback in a quick and scalable way so that you can measure what matters most to your students and employees.
MLY is extremely easy to use:
- Determine which survey data you wish to analyze and upload or drag and drop your data file into the MLY homepage.
- Select an analysis type:
- Employee Experience Intelligence (EEI)
- Employee Learning Intelligence (ELI)
- Student Experience Intelligence (SEI)
- Within minutes, a high-level Overview provides a graphical display allowing you to view data-driven insights that you can further explore in the Widgets, Topics explorer and Comments explorer,
- You can also share your analysis with colleagues across the organization or export the results to use with other data or in other systems.
Suggested MLY users and feedback sources
MLY can be used to analyze any comments captured within Blue, Blue X, Bluepulse, and MTM experience management platforms or from other platforms. The following is a list of a few potential sources of feedback you might want to consider for your next MLY analysis:
| Who could benefit from using MLY's analytical powers | Potential feedback sources |
|---|---|
| Enterprise | - Recruitment experience surveys |
| - Talent leaders | - Onboarding feedback surveys |
| - HR leaders | - Skills assessment |
| - Recruitment specialists | - 360 evaluation |
| - CLO | - Role change assessment |
| - Learning and Development directors | - Learning measurement surveys |
| - Organizational development leaders | - Performance reviews |
| - IT analytics director | - Engagement surveys |
| - Diversity, equity, and inclusion managers | - DEI surveys |
| - Continuous listening surveys | |
| Higher Ed | - Wellness checks |
| - University and college deans and provosts | - Ad hoc surveys |
| - Office of Institutional Research | - Exit / stay surveys |
| - Academic unit leaders | - Course evaluations |
| - Office of Assessment | - Campus climate surveys |
| - Student Affairs / Association | - Formative feedback surveys |
| - Student-Staff Liaison Committees (SSLC) | - Midterm reviews |
| - Data insights managers | - Peer reviews |
| - Diversity, equity, and inclusion managers | - Self evaluations |
| - Applicant surveys | |
| - Institutional services check-ins | |
| - DEI surveys | |
| -Student exit surveys | |
| - Alumni surveys | |
| - Advisors assessments |
How MLY works behind the scenes
MLY uses Neural Networks (computer systems with interconnected nodes that work much like neurons in the human brain) that analyze qualitative data (feedback collected in the form of free-form text comments from employees and students) and summarizes that data into:
- quantitative feedback (such as the total percentage of positive and negative comments and,
- insights such as sentiments, recommendations, alerts, 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.
To provide the best possible analysis, Explorance uses supervised training to ensure that high quality insights are produced using the following steps:
- Explorance consults with experts in each field to select the initial topics for each analysis type.
- Hundreds of thousands of comments are processed by MLY and the results are reviewed by human annotators using a very rigorous process to check for accuracy. During this process some topics are removed and other are added.
- To ensure high quality results, we monitor precision (what is the likelihood that we detected something with accuracy) and recall (how many times did we detect something that was wrong, did we get everything, and did we not make mistakes). To learn more see: F-measure and Fbeta-measure for the machine learning models.
Who owns customer data processed and created in MLY?
Customers retain ownership of their data once uploaded and analyzed by MLY. In addition, customer data is only used in training MLY if the customer has provided written consent. For addition information, contact Explorance.
Video: HR specialist uncovers insights using MLY to analyze 360-evaluation feedback
What do MLY results look like and how do they affect an organization? This video provides a sample project where BLU’s HR specialist uploads and analyzes comments data from a 360-evaluation process to discover what insights are buried in open text responses.