The client had plenty of historical data but lacked information. It was very difficult to prove that early intervention programs work and outcomes had to be tracked manually. There was no way to link program outcomes to the interventions to understand which interventions and/or staff actions are leading to better outcomes.

eMoksha was approached by a leading Career Tech Center to address challenges related to manual and inefficient data entry for in-program student data, post-program follow-up data, and struggles in gaining valuable insights from multi-year historical data.

eMoksha was approached by a leading steel distribution company which was growing rapidly. The company was facing several challenges in the demand planning processes and customer order fulfilment.

A leading B2B distribution company underwent a sales strategy transformation by utilizing predictive analytics and data science. The client had a wealth of customer and product data, but the challenge was to extract actionable insights from the scattered information. To address this, they deployed a customer value-based model, employing predictive algorithms to discern high-value customers and anticipate potential customer churn.