List of Oracle Data Mining Customers
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Since 2010, our global team of researchers has been studying Oracle Data Mining 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 Oracle Data Mining 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 Oracle Data Mining for ML and Data Science Platforms include: Fiserv, a United States based Professional Services organisation with 38000 employees and revenues of $20.46 billion, Darden, a United States based Leisure and Hospitality organisation with 187384 employees and revenues of $10.49 billion, StubHub, a United States based Professional Services organisation with 1500 employees and revenues of $650.0 million and many others.
Contact us if you need a completed and verified list of companies using Oracle Data Mining, 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.
The Oracle Data Mining customer wins are being incorporated in our Enterprise Applications Buyer Insight and Technographics Customer Database which has over 100 data fields that detail company usage of software systems and their digital transformation initiatives. Apps Run The World wants to become your No. 1 technographic data source!
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| Logo | Customer | Industry | Empl. | Revenue | Country | Vendor | Application | Category | When | SI | Insight | Insight Source |
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Darden | Leisure and Hospitality | 187384 | $10.5B | United States | Oracle | Oracle Data Mining | ML and Data Science Platforms | 2012 | n/a | In 2012, Darden implemented an enterprise analytics program using Oracle Data Mining. The deployment combined Oracle Data Mining with Oracle R Enterprise as part of the Oracle Advanced Analytics option, placing the effort squarely within the ML and Data Science Platforms category to deliver check-level analytics across Darden brands in the United States. The implementation emphasized analytics for guest experience, menu mix, and restaurant operations. Teams developed predictive modeling, segmentation and scoring workflows with Oracle Data Mining and Oracle R Enterprise to support guest insights, menu mix analysis and operational decisioning at the check level. Architecturally the solution was delivered through the Oracle Advanced Analytics option within the Oracle Database, integrated into Darden's enterprise analytics environment and applied across brands including Olive Garden. Operational coverage targeted restaurant operations, marketing analytics and menu strategy functions across U.S. sites. Governance was organized as an enterprise Check-Level Analytics program to centralize analytics methodology and standardize workflows across brands. The initiative aimed to improve guest insights and operations and was reported to target projected savings of more than $20M, with press coverage explicitly citing use of Oracle Data Miner. | |
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Fiserv | Professional Services | 38000 | $20.5B | United States | Oracle | Oracle Data Mining | ML and Data Science Platforms | 2015 | n/a | In 2015, Fiserv implemented Oracle Data Mining as part of an Oracle Advanced Analytics engagement. The deployment used Oracle Data Mining within the ML and Data Science Platforms category to address payments fraud detection and risk use cases in the financial services region, and Fiserv presented the customer case at Oracle BIWA events. Implementation centered on embedding Oracle Data Mining capabilities directly into database analytics to enable predictive modeling and operational scoring against transaction level payment data. Functional capabilities implemented included supervised classification for fraud scoring, anomaly detection workflows, feature engineering pipelines, and model training and scoring executed in database to minimize data movement and support near real time risk decisions. Operational coverage focused on risk and payments fraud teams, with model scoring operationalized into payments risk workflows and governance processes managed by those business functions. Architecture emphasized in database analytics and a model lifecycle approach for periodic retraining and validation to maintain fraud detection effectiveness within Fiserv payment operations. | |
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StubHub | Professional Services | 1500 | $650M | United States | Oracle | Oracle Data Mining | ML and Data Science Platforms | 2014 | n/a | In 2014, StubHub implemented Oracle Data Mining as part of an Oracle Advanced Analytics deployment built on Oracle Database options. The deployment centralized customer data and enabled in-database analytics to support recommendation models, churn prediction, and detection of fraudulent transactions within the companys customer analytics environment. Oracle Data Mining and Oracle Advanced Analytics were used to train and score models inside the database, providing pattern detection, classification, and scoring capabilities typical of ML and Data Science Platforms. Oracle Advanced Analytics is cited on the vendor case page, and Oracle Data Mining usage is inferred as a component of that Advanced Analytics implementation, aligning database-resident model training with operational scoring workflows. The implementation focused on embedding analytics into operational systems to reduce data movement and to surface risk and engagement signals for customer-facing and fraud operations teams. The engagement explicitly reduced a known fraud issue by up to 90 percent, demonstrating the deployment of Oracle Data Mining within the ML and Data Science Platforms category to support recommendations, retention analytics, and fraud detection. |
Buyer Intent: Companies Evaluating Oracle Data Mining
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