List of IBM SPSS Modeler Customers
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Since 2010, our global team of researchers has been studying IBM SPSS 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 IBM SPSS 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 IBM SPSS Modeler for ML and Data Science Platforms include: Kyocera Japan, a Japan based Manufacturing organisation with 77136 employees and revenues of $13.17 billion, ABL Life Insurance Korea, a South Korea based Insurance organisation with 900 employees and revenues of $400.0 million, Banca Alpi Marittime Italy, a Italy based Banking and Financial Services organisation with 201 employees and revenues of $130.0 million and many others.
Contact us if you need a completed and verified list of companies using IBM SPSS 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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ABL Life Insurance Korea | Insurance | 900 | $400M | South Korea | IBM | IBM SPSS Modeler | ML and Data Science Platforms | 2010 | n/a |
In 2010, ABL Life Insurance Korea implemented IBM SPSS Modeler. The IBM SPSS Modeler deployment entered the ML and Data Science Platforms layer of the insurer's analytics stack, supporting data mining and predictive modeling efforts across finance and risk.
The implementation included configuration of predictive modeling workflows, node based data transformations, model training and evaluation pipelines, and model export for scoring. The deployment leveraged IBM SPSS Modeler capabilities for supervised and unsupervised learning, feature engineering, and automated model operationalization aligned with ML and Data Science Platforms functional expectations.
The solution was integrated with existing data orchestration and reporting systems, explicitly using IBM Data-stage for ETL feeds and IBM Cognos for OLAP reporting, and it consumed source datasets from SAP FI CO and SAP Loan Module. Campaign and customer analytics workflows were connected to IBM Unica where applicable, enabling scored outputs to feed campaign segmentation and reporting pipelines.
Governance and operational ownership were managed by internal IT teams with documented IBM SPSS Modeler and IBM stack experience, including staff who held IT PL, IT PM, and PMO roles on IFRS9, IFRS17 Debt, and IFRS16 Lease Accounting related projects. Operational coverage targeted finance, actuarial, risk, and investment functions within South Korea, with model governance, version control, and scoring workflows embedded into SAP centric data processes.
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Banca Alpi Marittime Italy | Banking and Financial Services | 201 | $130M | Italy | IBM | IBM SPSS Modeler | ML and Data Science Platforms | 2019 | VAR Group |
In 2019, Banca Alpi Marittime Italy deployed an AI-driven Credit Scorecard approvals engine using IBM SPSS Modeler within its ML and Data Science Platforms environment to automate consumer and small business credit line renewals and applications in the Piedmont region. The implementation used IBM SPSS Modeler as the primary modeling and scoring runtime and targeted the bank's retail and small business lending workflows to accelerate decisioning and improve customer service.
The technical implementation centered on scorecard development, automated decision rules and production model scoring pipelines built with IBM SPSS Modeler, with model deployment and inference orchestrated to support automated approvals. The solution was configured to escalate higher risk or complex cases for manual review while automatically resolving standard renewals, enabling the engine to vet roughly 50% of credit requests automatically.
Delivery and operationalization were executed with IBM partner Analytics Network S.r.l. and supported by VAR Group, ensuring transfer of models into daily lending operations. Operational coverage included credit underwriting, retail operations and front office customer service teams across the Piedmont region, with scoring outputs embedded into the bank's approval workflows and case handling processes to drive straight through processing for eligible requests.
Governance was organized around model monitoring and approval workflows overseen by credit risk and compliance stakeholders, with procedural changes to routing and exception handling to reflect automated decision thresholds. The deployment freed approximately 10 full time equivalents for other tasks and achieved rapid return on investment within months as stated by the bank, while maintaining explicit human review for nonstandard cases.
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Kyocera Japan | Manufacturing | 77136 | $13.2B | Japan | IBM | IBM SPSS Modeler | ML and Data Science Platforms | 2018 | n/a |
In 2018, Kyocera Japan implemented IBM SPSS Modeler as part of a factory analytics platform, using ML and Data Science Platforms to detect root causes of defects and optimize production planning across its Japanese plants. IBM SPSS Modeler was embedded as the primary model design and scoring engine within the initiative to drive manufacturing and process improvement use cases.
The deployment architecture integrated IBM SPSS Modeler with IBM Cloud Pak for Data and GIView, enabling a managed model lifecycle and operational scoring inside the factory analytics stack. Functional capabilities implemented included root cause analysis, predictive defect detection, and demand driven production planning models, with model training and validation workflows centralized in the Cloud Pak for Data environment.
Operational scope covered multiple Kyocera manufacturing sites in Japan, with the system supporting quality engineering, production planning, and plant operations teams. The implementation linked predictive model outputs into planning workflows to prioritize inspections and adjust production sequencing, embedding analytics into day to day manufacturing decision making.
Governance and rollout established model monitoring and iterative retraining processes to support continuous production goals, and operational procedures were adjusted to act on model recommendations. Outcomes reported include reduced defect related losses and a yield increase of about 6 percent, which enabled higher equipment utilization and steps toward autonomous, continuous production.
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Buyer Intent: Companies Evaluating IBM SPSS Modeler
ARTW Buyer Intent uncovers actionable customer signals, identifying software buyers actively evaluating IBM SPSS Modeler. Gain ongoing access to real-time prospects and uncover hidden opportunities. Companies Actively Evaluating IBM SPSS Modeler for ML and Data Science Platforms include:
- University of Naples Federico, a Italy based Education organization with 4910 Employees
Discover Software Buyers actively Evaluating Enterprise Applications
| Logo | Company | Industry | Employees | Revenue | Country | Evaluated | ||
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FAQ - APPS RUN THE WORLD IBM SPSS Modeler Coverage
IBM SPSS Modeler is a ML and Data Science Platforms solution from IBM.
Companies worldwide use IBM SPSS Modeler, from small firms to large enterprises across 21+ industries.
Organizations such as Kyocera Japan, ABL Life Insurance Korea and Banca Alpi Marittime Italy are recorded users of
IBM SPSS Modeler for ML and Data Science Platforms.
Companies using IBM SPSS Modeler are most concentrated in Manufacturing, Insurance and Banking and Financial Services, with adoption spanning over 21 industries.
Companies using IBM SPSS Modeler are most concentrated in Japan, South Korea and Italy, with adoption tracked across 195 countries worldwide. This global distribution highlights the popularity of IBM SPSS Modeler across Americas, EMEA, and APAC.
Companies using IBM SPSS Modeler range from small businesses with 0-100 employees - 0%, to mid-sized firms with 101-1,000 employees - 66.67%, large organizations with 1,001-10,000 employees - 0%, and global enterprises with 10,000+ employees - 33.33%.
Customers of IBM SPSS Modeler include firms across all revenue levels — from $0-100M, to $101M-$1B, $1B-$10B, and $10B+ global corporations.
Contact APPS RUN THE WORLD to access the full verified IBM SPSS Modeler customer database with detailed Firmographics such as industry, geography, revenue, and employee breakdowns as well as key decision makers in charge of ML and Data Science Platforms.