We love data!

Every customer is unique and demands personalization. Customer management system past decade has created Petabytes of data. Our data scientists help you get the best insights from this data.

Our Methodology

Business Understanding

Business Objectives

Data Understanding

Data Preparation

Feature Selection

Machine Learning

Sand Box Evalution


Popular Data Science Models

Customer Lifetime Value Modeling

These models predict the future revenue that an individual customer will bring to your business in a given period.

Customer Churn Modeling

Customer churn modeling can help you identify which of your customers are likely to stop engaging with your business and why

Customer Segmentation

We can use clustering and classification algorithms to group customers into personas based on specific variations among them

Dynamic Pricing

Also known as demand pricing, is the practice of flexibly pricing items based on factors like the level of interest of the target customer

Image Classification

Image classification uses machine learning algorithms to assign a labels. It is used in the retail and medical imagining industry

Recommendation Engine

Recommendation engines sift through large quantities of data to predict how likely any given customer is to purchase an item

Markdown Optimisation

Using data, optimize markdown value and timing to achieve inventory objectives and increase revenue and profits.

Employee Churn Modeling

Companies spend $20K - $50K per year. Accurately predict which of your employee is going leave and when and prevent churn