We use a wide range of analytics technologies and techniques to help companies solve business challenges across functional areas, applying more than a decade of domain business expertise in Financial Services, Insurance, CPG ,Retail and Technology, as well as new sectors supporting healthcare and government.
Technologies & Techniques
Supply Chain, Channels
Visual Story Telling
Reporting, Dashboards & Visualization
To cleanse and structure the data: Involves evaluation of Big Data and harmonization across data sets for integrity and accuracy. Includes transformation, parsing and summarization of standard elements to integrate data from disparate sources. Includes database structure requirements, parameter loading into database, documentation, data quality checks and exploratory data analysis.
For business insights: To enable executives and business users to understand the state of the business. User interface is fully customized to facilitate interactive navigation of Big Data across the enterprise by function, business unit, region, and any other segmentation required.
Method Used: Univariate and multivariate techniques
Identify homogeneous groups/clusters: Identify segments or clusters that are homogeneous (similar) within themselves and heterogeneous (different) across each other are identified using various statistical techniques.
Identify heterogeneous groups/clusters and outliers: Identify unique profiles and behavior for specialized treatment or consideration.
Statistical techniques: Cluster analysis techniques(K-Means, hierarchical clustering), factor analysis, principal component techniques, segmentation techniques, Discriminant Analysis Techniques, Bayesian Belief Network, Random Forest and Decision Tree
Estimate predicted outcomes: Assess the likely probabilities of a particular outcome based on historical data. Typically includes out of time validation to demonstrate usability across alternative data time periods and populations.
Statistical techniques: Linear regression, logistic regression, generalized regression, conjoint and discrete analysis, hierarchical modeling, semi-parametric techniques, Bayesian techniques, neural network, gradient boosting, Random Forest, Support Vector Machine, Monte Carlo Simulations
Identify likely best action sets: Provides the set of best action based on scientific predictions given constraints to maximize or minimize a specific function/problem by systematically choosing input values among specified parameters.