Flipkart, the poster child of Indian e-commerce, was an early entrant in the nascent Indian e-commerce market and quickly established itself as the leading company in this space. Flipkart has grown into an online retail giant, valued at over USD 15.2 billion as of 2015. Flipkart has been selling over 30 million products from more than 50,000 sellers in 70+ categories as well as has 30 exclusive brand associations with an in-a-day guarantee in 50 cities and a same-day guarantee in 13 cities. Flipkart was 33,000 people strong and had over 50 million registered users with over 10 million daily visits and 8 million shipments per month. Flipkart has been putting in much effort and emphasis on the use of Analytics in every aspect of decision making. Headed by Ravi Vijayaraghavan, the analytics team had over 100 data scientists in 2015. Customer churn is a major concern for Flipkart since it has direct impact on Customer Lifetime Value (CLV). CLV is an important measure to differentiate customers, which can further help the organization manage them effectively. The main challenge in calculating the lifetime value of customers of e-commerce companies such as Flipkart is that the exact life of the customer is unknown owing to data truncation, that is, the actual point in time of customer churn, which may not be identified in e-commerce since there would be no prior communication from the customer about the churn. Hence, traditional models of CLV calculation may not be appropriate for e-commerce companies such as Flipkart.
Flipkart, the poster child of Indian e-commerce, was an early entrant in the nascent Indian e-commerce market and quickly established itself as the leading company in this space. Flipkart has grown into an online retail giant, valued at over USD 15.2 billion as of 2015. Flipkart has been selling over 30 million products from more than 50,000 sellers in 70+ categories as well as has 30 exclusive brand associations with in-a-day guarantee in 50 cities and same-day guarantee in 13 cities. Flipkart was 33,000 people strong and had over 50 million registered users with over 10 million daily visits and 8 million shipments per month. Flipkart has been putting in much effort and emphasis on the use of Analytics in every aspect of decision making. Headed by Ravi Vijayaraghavan, the analytics team had over 100 data scientists in 2015. Customer churn is a major concern for Flipkart since it has direct impact on Customer Lifetime Value (CLV). CLV is an important measure to differentiate customers, which can further help the organization to manage them effectively. The main challenge in calculating the lifetime value of customers of e-commerce companies such as Flipkart is that the exact life of the customer is unknown owing to data truncation, that is, the actual point in time of customer churn, which may not be identified in e-commerce, since there would be no prior communication from the customer about the churn. Hence, traditional models of CLV calculation may not be appropriate for e-commerce companies such as Flipkart.
Providing affordable housing to a rapidly increasing low income group population in Urban-Semi Rural India remains one of the biggest challenges as well as opportunities faced by the Housing Finance Sector. Several new housing finance companies such as Shubham Housing Finance have pioneered a "small ticket" loan product to address the market gap. They recognize that these customers are not "high-risk" as perceived by conventional financiers, but "unknown risk". To assess this "unknown risk", they rely on detailed, field-based verification rather than on formal financial documentation. The primary objective of this case is to analyze the past data from these field level interactions and the eventual credit evaluation decision to determine the factors which result in a favorable decision. The application scoring model is expected to deliver a competitive edge to Shubham's operations by enabling faster decisions earlier in the assessment phase, targeting applicants more likely to pass through to the credit worthy status, standardize applicant evaluation across the nation and enable Shubham to offer competitive products. The objective of the case is to predict the probability of loan sanction using the socio-economic attributes of prospective loan applicants by employing techniques such as chi-squared automatic interaction detection (CHAID) and binomial logistic regression.