List of Explorance MLY Customers
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Since 2010, our global team of researchers has been studying Explorance MLY customers around the world, aggregating massive amounts of data points that form the basis of our forecast assumptions and perhaps the rise and fall of certain vendors and their products on a quarterly basis.
Each quarter our research team identifies companies that have purchased Explorance MLY for ML and Data Science Platforms from public (Press Releases, Customer References, Testimonials, Case Studies and Success Stories) and proprietary sources, including the customer size, industry, location, implementation status, partner involvement, LOB Key Stakeholders and related IT decision-makers contact details.
Companies using Explorance MLY for ML and Data Science Platforms include: Microsoft, a United States based Professional Services organisation with 221000 employees and revenues of $243.00 billion, University of Westminster, a United Kingdom based Education organisation with 5500 employees and revenues of $347.0 million, Heriot-Watt University, a United Kingdom based Education organisation with 2417 employees and revenues of $344.0 million and many others.
Contact us if you need a completed and verified list of companies using Explorance MLY, including the breakdown by industry (21 Verticals), Geography (Region, Country, State, City), Company Size (Revenue, Employees, Asset) and related IT Decision Makers, Key Stakeholders, business and technology executives responsible for the software purchases.
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight |
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Heriot-Watt University | Education | 2417 | $344M | United Kingdom | Explorance | Explorance MLY | ML and Data Science Platforms | 2024 | n/a |
In 2024 Heriot-Watt University implemented Explorance MLY to analyse qualitative feedback from a wide range of student surveys, including course and module evaluations, annual surveys and postgraduate surveys. The deployment leverages Explorance MLY within the ML and Data Science Platforms category to accelerate text analytics workflows and surface actionable insight for teaching and curriculum improvement.
Explorance MLY is configured to process large volumes of open comments, approximately c.100,000, applying natural language processing, topic extraction, thematic coding and sentiment classification to identify recurring issues and patterns. The implementation emphasizes automated grouping of qualitative feedback into configurable themes and evidence-backed recommendation generation to support local decision making.
Operational coverage targets academic departments and local leaders, with outputs routed to academic teams responsible for curriculum review and module-level teaching improvements. The scope explicitly covers course/module evaluations, annual student surveys and postgraduate feedback streams, positioning Explorance MLY to standardise qualitative analysis across those survey types and shorten time to insight for departmental stakeholders.
Governance changes focus on integrating MLY-derived themes into existing curriculum review processes and establishing review workflows for academic leads to validate system-generated recommendations. The university expects Explorance MLY to deliver faster, evidence-backed recommendations to local leaders and academic teams, and to handle the stated volume of open comments to inform teaching and curriculum improvement.
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Microsoft | Professional Services | 221000 | $243.0B | United States | Explorance | Explorance MLY | ML and Data Science Platforms | 2023 | n/a |
In 2023, Microsoft integrated Explorance MLY, an ML and Data Science Platforms application, into Microsoft Worldwide Learning to convert hundreds of thousands of learner comments per year into actionable insights. The deployment targeted global technical learning and certification offerings and directly supported learning and L&D teams as well as the Training Services Partner network.
Explorance MLY was configured to deliver feedback analytics capabilities including sentiment analysis, recommendation extraction and prioritization workflows, leveraging natural language processing pipelines for automated text classification and inference. The implementation emphasized automated ingestion and processing of unstructured learner feedback, producing curated outputs for operational dashboards and content review queues.
Operational coverage included Microsoft Worldwide Learning and partner-managed training programs globally, routing prioritized insights to course owners, curriculum managers and certification teams. Reporting and workflow handoffs from Explorance MLY were used to inform content updates and certification roadmap discussions across training services.
Governance established centralized analytics standards and a feedback-to-action process to ensure consistent prioritization across the Training Services Partner network. The program enabled faster learner-driven improvements by surfacing sentiment, recommendations and priorities at scale.
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University of Westminster | Education | 5500 | $347M | United Kingdom | Explorance | Explorance MLY | ML and Data Science Platforms | 2023 | n/a |
In 2023, University of Westminster implemented Explorance MLY to scale thematic analysis of open-ended responses across student and colleague surveys. Explorance MLY, categorized as ML and Data Science Platforms, was purchased at the end of 2022/23 to provide near-real-time qualitative insights for course and module evaluation and student experience programs in the United Kingdom higher education context.
The deployment emphasized automated thematic coding and pattern extraction to streamline manual coding workflows and increase qualitative output volume. Explorance MLY was configured to run topic extraction, clustering, tagging, and narrative summarization workflows across survey cycles, producing structured themes and qualitative summaries for academic stakeholders.
Operational scope covered academic quality teams, course and module leads, and student experience staff, with the platform ingesting open-ended responses from routine student and colleague surveys. Outputs were routed into existing review and action planning processes so teams could detect in-year issues and prioritize targeted interventions within the academic year.
Governance shifts included redefining analysis ownership toward analytics-enabled workflows and establishing regular review cadences for MLY-generated insights, reducing reliance on manual thematic coding. The University of Westminster cited streamlined manual coding, increased qualitative output volume, and faster identification of in-year issues to support targeted interventions as the primary drivers for the implementation.
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