Datansh helped a U.S.-based light manufacturer transform long, unstructured CRM conversation histories into predictive next-action intelligence, giving sales reps and managers faster clarity on how to move enterprise deals forward.
+9% revenue increase by helping sales teams focus on high-potential opportunities and recommended actions.
~60% lower manual effort in reading long lead histories to determine the next step.
~2.5x faster lead-action identification for sales reps and managers.
The client's Oracle CRM held the full story of each lead inside long-form comment fields built up over months or years of follow-ups. Sales reps had to read large volumes of unstructured text manually, which slowed decisions, made next actions unclear, and contributed to missed opportunities and lower conversion efficiency.
Datansh ingested CRM data through a reusable ETL framework consuming CRM APIs, labeled and trained Azure ML models on the text-based interaction history, predicted likely sales opportunities and next actions, and surfaced the outputs in interactive Power BI dashboards for reps, managers, and leadership.
More work in manufacturing and adjacent operational environments.
Datansh helped a multinational tyre manufacturing organization move from disparate, semi-structured data flows to a centralized batch-and-streaming platform that treated data as a product and improved enterprise reporting reliability.
Datansh helped a U.S.-based multinational manufacturer replace static, number-heavy ERP reporting with a secure Azure-based analytics foundation that unified legacy ERP data, connected additional enterprise sources, and turned reporting into interactive business visibility.
Datansh helped a distributed manufacturing organization replace fragmented Power BI development and slow deployments with a governed CoE model built around standardized semantic models, tenant monitoring, and CI/CD-driven release control.