List of IBM Watson Machine Learning Customers
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Since 2010, our global team of researchers has been studying IBM Watson Machine Learning 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 Watson Machine Learning 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 Watson Machine Learning for ML and Data Science Platforms include: TPBank, a Vietnam based Banking and Financial Services organisation with 7939 employees and revenues of $664.0 million, Capital Bank of Jordan, a Jordan based Banking and Financial Services organisation with 1200 employees and revenues of $300.0 million, Goodiebox, a Denmark based Retail organisation with 150 employees and revenues of $15.0 million and many others.
Contact us if you need a completed and verified list of companies using IBM Watson Machine Learning, 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 IBM Watson Machine Learning 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 |
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Capital Bank of Jordan | Banking and Financial Services | 1200 | $300M | Jordan | IBM | IBM Watson Machine Learning | ML and Data Science Platforms | 2021 | JBS Jordan |
In 2021, Capital Bank of Jordan implemented IBM Watson Machine Learning under the ML and Data Science Platforms category to operationalize predictive analytics across retail and corporate banking. The program modernized the bank's data hub with IBM Cloud Pak for Data and used IBM Watson Machine Learning via Watson Studio to build customer propensity, churn and fraud detection models, aligning analytics and data infrastructure within a unified platform.
The implementation focused on model development, training and deployment capabilities typical of ML and Data Science Platforms, using Watson Studio for experiment management and IBM Watson Machine Learning for model scoring and lifecycle management. Predictive models targeted customer propensity scoring, churn prediction and transactional fraud detection, with production scoring pipelines feeding marketing segmentation and risk monitoring workflows.
Integration work was centered on the modernized data hub built on IBM Cloud Pak for Data, and the rollout covered retail and corporate banking operations in Jordan, impacting marketing, customer experience and fraud operations. JBS Jordan served as the system integrator, orchestrating platform integration, data ingestion and operational handover to the bank's analytics teams.
Governance activities introduced model lifecycle controls and workflow standardization for model promotion and monitoring, and the deployment delivered stated operational outcomes, including reducing acquisition related data migration time by ~95% and accelerating detection of mobile experience issues by over 10x, which improved customer experience and marketing effectiveness.
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Goodiebox | Retail | 150 | $15M | Denmark | IBM | IBM Watson Machine Learning | ML and Data Science Platforms | 2024 | Integrator |
In 2024, Goodiebox deployed IBM Watson Machine Learning in the ML and Data Science Platforms category to optimize product-mix planning and inventory for its monthly subscription boxes. The implementation used IBM Watson Studio Heritage alongside IBM Watson Machine Learning and targeted supply-chain and merchandising processes across Denmark and broader Europe.
The project configured model development and production pipelines within IBM Watson Studio Heritage, covering data preparation, model training, validation, and operationalization through IBM Watson Machine Learning. Functional capabilities emphasized demand forecasting, product-mix optimization, scenario testing, and automated model scoring to accelerate monthly box planning workflows.
IBM and integrator partner Integrator executed the rollout with operationalization focused on merchandising and supply-chain teams, embedding model outputs into planning cycles for subscription box assortments and inventory decisions. The deployment scope covered Denmark and extended to European planning processes for subscription operations.
Governance combined IBM product stewardship with Integrator delivery oversight, aligning model governance, retraining cadence, and decisioning workflows with merchandising process owners. Reported outcomes included planning becoming roughly five times faster and improved inventory alignment with reduced costs.
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TPBank | Banking and Financial Services | 7939 | $664M | Vietnam | IBM | IBM Watson Machine Learning | ML and Data Science Platforms | 2021 | n/a |
In 2021, TPBank implemented IBM Watson Machine Learning as part of an IBM Cloud Pak for Data deployment to accelerate credit card propensity modeling and improve digital acquisition in Vietnam. The deployment used IBM Watson Studio together with IBM Watson Machine Learning within the ML and Data Science Platforms category to support marketing and CRM acquisition use cases and to centralize model development for the bank.
The implementation focused on building and operationalizing credit card propensity models, leveraging Watson Studio for experiment management and model development and IBM Watson Machine Learning for model training, versioning and deployment. Standard model lifecycle practices were applied, including model training pipelines, automated scoring endpoints and model registry artifacts to support repeatable production deployments.
Integrations targeted marketing and CRM workflows to drive digital acquisition and conversion optimization, with models deployed into the digital channel and campaign decisioning processes in Vietnam. Operational ownership rested with the bank’s data science teams, who used the platform to standardize modeling approaches and share assets across marketing initiatives.
Governance and process changes emphasized production model control and faster time to deploy, with the platform reducing model development and deployment times across the bank’s data science teams. The project delivered a 24% conversion uplift for digital credit card acquisition, demonstrating the application of IBM Watson Machine Learning to marketing and CRM functions at TPBank.
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