DTNA’s aftermarket division was operating with limited foresight in a fast-moving, margin-sensitive environment. Sales and pricing teams relied heavily on manual analysis and siloed data, making it difficult to identify churn risks or respond competitively to bids in time.

With a high bid loss rate, rising customer churn and pricing inefficiencies impacting millions in revenue annually, traditional business intelligence tools provided only retrospective insights. Manual bid preparation took hours, pricing optimization covered only a fraction of SKUs and churn signals surfaced only after contracts were lost—putting both revenue and long-term customer relationships at risk.

DTNA set out to transform its aftermarket operations from reactive reporting to predictive, AI-driven intelligence. The goal was to enable data-backed decisions across the entire customer lifecycle proactively identifying churn risks, improving bid competitiveness and optimizing pricing strategies.

A key objective was to integrate multiple advanced analytics use cases into a unified platform that could scale across thousands of fleet relationships, support real-time scenario modelling and provide both executive visibility and self-service insights for business teams.

Three AI-driven use cases formed the foundation of the solution:

SAP Analytics Cloud served as the unified insights and planning layer, embedding machine learning directly into dashboards and enabling real-time what-if simulations. Native integration with SAP systems and Azure ML allowed predictive models, scenario analysis and executive planning to operate seamlessly in one governed environment—delivering insights that were actionable, scalable and collaborative.

Beyond financial outcomes, DTNA empowered business users with self-service analytics, reduced reliance on spreadsheets and established a scalable AI foundation that competitors using traditional BI tools could not replicate.

With predictive intelligence embedded into everyday decision-making, DTNA is building a more resilient and future-ready aftermarket ecosystem. By continuously expanding AI-driven use cases across sales, pricing and planning, the organization is positioned to deepen customer partnerships, adapt faster to market shifts and turn data into a long-term competitive advantage—mile after mile.

“The recent integration of SAP Analytics Cloud with Azure Machine Learning—leveraging HTTP APIs and OData-based simulations—has significantly transformed our analytical capabilities. By enabling real-time dashboards and advanced scenario modeling, we can now assess how customer bid strategies influence revenue, margins and inventory planning with far greater precision. This interactive environment allows teams to test assumptions, explore alternative outcomes and visualize the impact of each decision. As a result, leadership gains stronger confidence not only in the quality of the insights being generated but also in the systems supporting our strategic growth.”

DTNA-Director of Strategic Pricing and parts optimization