2017 Analytics 50 Honorees

SimpleTire

Chiranjoy Das, Chief Information Officer
SimpleTire
Industry: E-Commerce

Business Challenge

The project began with a goal to understand these challenges: (a) fund allocation & usefulness of marketing campaigns, (b) if these campaigns help retain customers, and (c) creation of new leads. IT took on this challenge to create solutions not only to solve these issues, but also create ‘futuristic’ guiding solutions (predictive/prescriptive modeling).

Analytics Solution

The IT team utilized artificial intelligence, machine learning, and big data to provide foresighted analytics that enable decision makers to better position themselves vis-a-vis an uncertain future. Through the deployment of a suitable BI architecture, coupled with appropriate home-grown analysis tools, IT provided a framework for advanced statistical analytics.

This futuristic framework also enabled business leaders to accurately predict future business outcomes. The artificial intelligence data models also contributed to budget allocation decisions, and ways in which customer retention can be improved through targeted outreach campaigns in a retail environment.

Impact

This project is an example where IT drove the business. IT helped in reallocating the funds (as suggested by the predictive model created by this project using Naive Bayes Probabilistic Classifier, among other algorithms) and directed the business toward opportunities hitherto unknown. This project elevated the IT team from taking orders to acting as a strategic partner of the business, and finally to a level where IT was driving business strategies by creating areas of opportunities.

The operations team redirected their resources to attend customers, which would generate higher revenue. They eliminated many suppliers who were providing poor customer experience based on the ‘what-if’ scenarios and decision tree of the model generated from the ERP data.

The customer care team created various targeted campaigns to upsell and cross-sell to different categories of customers, as per the segmentation and neural network (associative analysis) created by the model from the CRM data.