We specialize in creating statistical models that formalize relationship between multiple variables in the form of mathematical equations. It is our value proposition in the field of Statistical modeling that allows our clients across multiple verticals to accurately forecast business critical & enables them with data driven strategic decision.
Our highly talented professionals deploy advanced Statistical techniques coupled with deep domain expertise to solve complex business problems. The approach of Stochastic & Causative Modeling ensures accuracy & dependability on results for future planning. Our expert team comprises of professionals from wide range of Industries such as Telecommunication & Outsourcing, Pharmaceuticals and Healthcare, BFSI and Retail.
Businesses managing final customer touch points, customer relationships and owning customer data adds maximum influence to building successful strategies. Our Statistical models help these extremely dynamic Industry segment in maintaining optimized capacity & predicting customer behavior.
|Capacity planning||Using stochastic methods like exponential smoothening, Winter holt’s, ARIMA to accurately forecast business critical impacting the bottom line|
|Demand forecasting & Inventory management||Using Multiple Regression, Logistics Regression & other Causative models to optimize inventory|
|Customer Behavior analysis||Using Statistical models to decide which target groups of customers should be encouraged to spend more, what credit line to assign and whether to promote new products to particular groups of customers or not|
|Developing Employee Retention strategy & controlling attrition||Building statistical models by formalizing personal characteristics like gender, education, age with job characteristics like self esteem, participation, growth prospects, work compatibility into a mathematical equation|
Our models are based on both exploratory data analysis and confirmatory data analysis techniques. In an exploratory analysis, we formulate all models possible, and see which describes the data best. In a confirmatory analysis we test which of our models described before the data was collected fits the data best.