A comprehensive view of contacts was developed for a leading asset management company in the UK, which served as inputs into Machine Learning models. Accurate predictions of disinvestments in 60% of the cases leading to retention of mandates worth £444 million. Better targeting of wholesale clients led to a potential investment uplift of £50 million.
A Machine Learning Rules & Notification engine was built for a leading transnational consumer goods company, which helped make timely market interventions in supply chain planning, reducing the stock-out rate by 5% and inventory costs by 2%.
Models were developed for a top global technology solution and service provider using Ridge regression, halo impact analysis, and S-Curves. The GUI-based tool helped allocate budget across various activities optimally, which influenced an uplift of ~ $200 Million in opportunity value annually.
For a leading global technology infrastructure provider, regularized random forests and ‘SVD + Univariate Forecasts’ were combined to improve forecast accuracy, enabling potential savings between $3 million -$5 million.
Baseline forecasts for 3000 SKU’s were generated for the world’s third-largest jewelry manufacturer. New SKUs were automated. This led to a 13% increase in overall demand forecast accuracy and reduced supply planning costs by 2%.
An intuitive, actionable visualization platform that provides insights across Headcount, Recruitment, Movements, Performance, and Talent metrics was built for a US-based retail chain, which led to decisions being taken at a much faster rate saving almost 30% in analysis and turn-around time.
Data was migrated from legacy systems into a cloud-based data lake with extended periodic data refresh for the largest food distributor in the US. This reduced the cost of ownership and increased the throughput of cloud-hosted analytics solutions.
Customer Lifetime Value was computed using SolveIT, an in-house product that has helped companies re-calibrate retention strategies and acquisition initiatives to reduce churn by 4% on average and improve customer acquisition by 5-8%.
A tool that aided the forecast of the most optimized bids for ‘first spots’ on travel aggregator websites was developed for a leading travel website. This resulted in a net annual financial impact of $532k with the aid of cost reduction (optimized bids) of $204k and an increase in transactions of $328k.
Process Mining was used to identify bottlenecks for a leading snack and beverage manufacturer, which resulted in savings of $1 million per packaging line.
