List of Minitab Salford Predictive Modeler Customers
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United States
Since 2010, our global team of researchers has been studying Minitab Salford Predictive Modeler 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 Minitab Salford Predictive Modeler 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 Minitab Salford Predictive Modeler for ML and Data Science Platforms include: Bank of America, a United States based Banking and Financial Services organisation with 213000 employees and revenues of $101.89 billion, Tate & Lyle, a United Kingdom based Consumer Packaged Goods organisation with 4971 employees and revenues of $2.38 billion, DataLab USA, a United States based Professional Services organisation with 130 employees and revenues of $27.0 million and many others.
Contact us if you need a completed and verified list of companies using Minitab Salford Predictive Modeler, 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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Bank of America | Banking and Financial Services | 213000 | $101.9B | United States | Minitab | Minitab Salford Predictive Modeler | ML and Data Science Platforms | 2017 | n/a |
In 2017, Bank of America implemented Minitab Salford Predictive Modeler within its analytics tooling to support finance, risk, and consumer-behavior analytics, and the vendor acquisition materials identify SPM as a key tool for predictive modeling and scoring at the bank. The record of usage highlights Minitab Salford Predictive Modeler as part of the bank's ML and Data Science Platforms footprint for consumer behavior and modeling tasks.
Minitab Salford Predictive Modeler was applied to consumer behavior and modeling tasks, supporting classification and regression workflows, model validation, and production scoring pipelines. The implementation relied on SPM capabilities common to ML and Data Science Platforms, including tree based and ensemble modeling, variable selection, automated model fitting and scoring to generate deployable predictive models and scorecards for downstream processes.
Operationally the solution was owned by analytics and risk modeling functions, where models fed consumer analytics and risk scoring workflows used across financial services use cases. Governance focused on model validation, iterative feature engineering and operational scoring, aligning outputs from Minitab Salford Predictive Modeler with existing risk and analytics processes.
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DataLab USA | Professional Services | 130 | $27M | United States | Minitab | Minitab Salford Predictive Modeler | ML and Data Science Platforms | 2016 | n/a |
In 2016, DataLab USA implemented Minitab Salford Predictive Modeler as part of its ML and Data Science Platforms toolkit for marketing analytics. The team incorporated the Salford Predictive Modeler TreeNet algorithm into their predictive toolkit to support direct-marketing and customer value modeling for CRM driven campaigns.
Deployment centered on embedding Minitab Salford Predictive Modeler into existing analytic workflows used by the marketing and CRM teams, delivering ensemble tree based predictive modeling, model selection and scoring capabilities aligned with ML and Data Science Platforms. The implementation was explicitly marketing and CRM focused, producing models for direct-marketing targeting and customer lifetime value segmentation. DataLab USA credited SPM TreeNet for superior predictive power and speed in competition models and the work contributed to winning the 2016 DMA Analytic Challenge.
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Tate & Lyle | Consumer Packaged Goods | 4971 | $2.4B | United Kingdom | Minitab | Minitab Salford Predictive Modeler | ML and Data Science Platforms | 2020 | n/a |
In 2020 Tate & Lyle deployed Minitab Salford Predictive Modeler within its UK manufacturing organization to address particle size variation in corn sugar crystallization, using the application as an ML and Data Science Platforms solution for manufacturing process engineering and process analytics. The deployment placed predictive modeling capability into the hands of the corn sugar crystallization team to support root cause analysis and process insight generation.
The project leveraged the TreeNet ensemble algorithm in Salford Predictive Modeler together with Minitab Statistical Software to perform high dimensional feature selection and model building. Analysis narrowed an initial set of more than 1,000 candidate predictors to eight key variables, with modeling workstreams emphasizing variable importance ranking and partial dependence style interpretation. Minitab Salford Predictive Modeler was used for tree based predictive modeling while Minitab Statistical Software was used for exploratory data analysis and statistical validation.
Operational scope was focused on the UK corn sugar crystallization team, with activity at plant process level and involvement from manufacturing and process engineering stakeholders. Integrations were implemented between Salford Predictive Modeler workflows and Minitab Statistical Software to create a pipeline from statistical exploration to predictive model production. Data inputs supported plant operational decision making and process control analytics.
Governance and rollout emphasized a model led root cause analysis process and a variable reduction governance workflow to ensure model interpretability and hand off to process engineers. According to the vendor case study created prior to 2021 the initiative delivered measurable process insights that reduced product variation.
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