Data Science

Data Science offerings allow customers to derive value, improve operational performance and tap into new market segments through advanced statistical analysis and automation. These programs lead to direct operational benefit for the client and help realize better value from data within the organization.

Business Intelligence

Business Intelligence (BI) solutions help identify and track key metrics across all aspects of an organization.

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Cloud Analytics

Integrate business intelligence (BI) solutions on cloud to create faster analytics pipelines for Insights Dashboards.

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Machine Learning

Machine Learning (ML)-based segmentation helps identify the most profitable products, customers or markets in client portfolio.

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Data Mining

Data mining helps reveal previously unknown or unsuspected insights in the data. Such as anomalies, patterns and correlations within large data sets.

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Demand Prediction

Demand prediction using machine learning methods help provide far more accurate predictions compared to traditional forecasting methods.

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Extracting insights from various data sources that serve as the foundation for effective human actions and decision making

Predictive Monitoring

Real-time, predictive anomaly detection models based on operational metrics.

Applications - Network monitoring, asset monitoring, operations management and others.

Automate and identify data points that can be risks to the system and software applications.

Predictive Maintenance

Dynamic maintenance schedule based on historical performance.

Predict large scale system/machine failure.

Help reduce costs and increase reliable assets.

Reduce downtimes for automated machines.

Segmentation

Provide digital product, customer, inventory solutions among various others to identify the most profitable or loss making assets and focus on specific path to increase quality of the solution.

Help identify repeat customers, target ad campaigns, help focus on churn and predict such churn before they happen.

Experimentation

Conduct hypothesis-driven statistical tests to identify differences between key metrics(inventory costs, customer lifetime value, marketing attribution, e.t.c) and if certain implementations are actually deriving results.

Customer experience optimization especially in retail and marketing segments.

Optimization

Provide diverse optimization modeling solutions to enable efficient routing, inventory, schedule management among various others.

Work with different constraints to optimize revenue impacting metrics.

Applications- Retail, Supply chain.

Forecasting

Provide time-series forecasting models to better see metric performance and identify triggers before they happen.

Examples can include sales or project estimate forecasting, throughput predictions and metrics where time stamp related data is available.

Text Based Analytics

Use text based data to identify sentiments and help derive insights to drive efficiency across different customer profiles.

Natural Language Processing based text clustering to identify segments across process and help derive research scope/insights.

Customer Analytics

Predict and identify key customer engagement metrics, evaluate churn, increase return ratio of customers and produce products designed for specific group of customers.

Bucket customers to provide specific recommendations for product usage, discounts and increase overall satisfaction levels.