We help enterprises across all industries to develop competitive advantage from their data and analytics assets and improve decision making by generating insights that yield better and faster decisions.
We have implemented advanced analytics use cases in the following areas using R and Python
- Segmentation and Cohort Analysis
- What-If and Scenario Analysis
- Time-Series Analysis
- Predictive Analysis
- Sales Forecasting
- Customer Churn Prediction
We also use machine learning algorithms to describe data, improve data and predict outcomes. Our team of data scientists, engineers, data stewards and analysts possess deep industry and technical expertise to drive actionable business outcomes for your organization.
When you define customer segments, you profile the customers into groups that have similar demand characteristics. Clustering helps marketers to improve their customer base, work on target areas and segment customers based on purchase history, interests, or activity monitoring.
We use machine learning (k-means algorithm) to help our clients target specific clusters of customers for specific campaigns.
Customer Churn Prediction
Churn prediction models predict a customer’s tendency to churn by using information about the customer such as household and financial data, transactional data and behavioral data.
We use machine learning (logistic regression and random forest algorithms) to help our clients target specific customers for proactively working on churn reduction.
We use machine learning (time series analysis using ARIMA – Auto Regressive Integrated Moving Average) for forecasting and explaining historical patterns.
Some examples of such use cases are
- Explaining seasonal patterns in sales
- Estimating the effect of a newly launched product on number of sold units