List of SAP Customer Retention, powered by Leonardo ML Customers
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Since 2010, our global team of researchers has been studying SAP Customer Retention, powered by Leonardo ML 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 SAP Customer Retention, powered by Leonardo ML 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 SAP Customer Retention, powered by Leonardo ML for ML and Data Science Platforms include: Cargill, a United States based Consumer Packaged Goods organisation with 155000 employees and revenues of $154.00 billion, Philips, a Netherlands based Manufacturing organisation with 67247 employees and revenues of $18.02 billion, Queensland Treasury, a Australia based Government organisation with 1541 employees and revenues of $459.0 million and many others.
Contact us if you need a completed and verified list of companies using SAP Customer Retention, powered by Leonardo ML, 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 Machine Learning software purchases.
The SAP Customer Retention, powered by Leonardo ML 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 Machine Learning 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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Cargill | Consumer Packaged Goods | 155000 | $154.0B | United States | SAP | SAP Customer Retention, powered by Leonardo ML | ML and Data Science Platforms | 2016 | n/a | In 2016, Cargill implemented SAP Customer Retention, powered by Leonardo ML. The deployment is positioned as an ML and Data Science Platforms effort within Cargill's innovation lab to develop digital solutions that leverage SAP Leonardo IoT services and the Azure IoT Cloud platform, ingesting sensor data from agriculture farming to feed machine learning workflows for customer retention analytics for marketing and account management. The implementation includes typical ML and Data Science Platforms capabilities such as data ingestion and preprocessing, model training and scoring, customer segmentation, and predictive churn modeling to inform retention strategies. Integration points explicitly include SAP Leonardo IoT services and the Azure IoT Cloud platform, with sensor telemetry from agricultural sites routed into the analytics pipeline. Development and governance were coordinated through Cargill's innovation lab, using pilot deployments to validate models and operationalize workflows into customer engagement and retention processes. | |
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Philips | Manufacturing | 67247 | $18.0B | Netherlands | SAP | SAP Customer Retention, powered by Leonardo ML | ML and Data Science Platforms | 2016 | n/a | In 2016 Philips implemented SAP Customer Retention, powered by Leonardo ML as a targeted ML and analytics deployment within Philips Lighting Strategic Operations test factories. The implementation used SAP Customer Retention, powered by Leonardo ML as an ML and Data Science Platforms solution to design and validate machine learning and process mining use cases for manufacturing and operational retention analytics. Configuration centered on machine learning pipelines, model training and inference workflows, and process mining capabilities to surface operational inefficiencies in factory test lines. The deployment included data preparation and feature engineering routines typical of ML and Data Science Platforms, and the application was configured to support iterative model retraining and monitoring as part of continuous operational experimentation. Integrations were explicitly with SAP Leonardo and SAP data analytics applications, and the implementation consumed Industrial IoT sensor feeds and factory test data to feed models and process mining analyses. Data flow architecture emphasized SAP-native analytics components and Leonardo ML services working with on-premises test factory telemetry to enable near real time scoring and process visibility. Governance and rollout were organized by Philips Lighting Strategic Operations, operating as a pilot within selected test factories where consultants designed and deployed the use cases. Operational governance focused on model lifecycle controls, process mining driven workflow adjustments, and iterative deployment cycles within the test factory scope without broader regional rollout details provided. | |
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Queensland Treasury | Government | 1541 | $459M | Australia | SAP | SAP Customer Retention, powered by Leonardo ML | ML and Data Science Platforms | 2018 | n/a | In 2018, Queensland Treasury implemented SAP Customer Retention, powered by Leonardo ML. The initiative is categorized as ML and Data Science Platforms and leveraged SAP Leonardo machine learning capabilities alongside SAP HANA PAL to support analytics for taxpayer services. OSR partnered with SAP to develop a proof of concept focused on transforming taxpayer services, with explicit operational scope in revenue collection and debt management. The program concentrated on predictive modeling and retention analytics to inform revenue collection strategies and case prioritization within Queensland Treasury operations. Architecturally the proof of concept combined cloud native SAP Leonardo model development and orchestration with in-database analytics using SAP HANA PAL for scoring and analytic processing. Models and scoring outputs were positioned to feed operational workflows for revenue collection and debt management rather than standalone reporting, aligning machine learning outputs to caseworker decision support and prioritization. Governance for the POC emphasized iterative model validation with OSR stakeholders and staged operationalization, including testing data science outputs against existing taxpayer service processes. The engagement remained framed as a proof of concept between OSR and SAP with a focus on embedding ML driven insights into revenue collection and debt management workflows. |
Buyer Intent: Companies Evaluating SAP Customer Retention, powered by Leonardo ML
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