Question

Big data is the collection and cross-referencing of large numbers and varieties of data sets that allows organizations to identify patterns and categories of cardholders through a multitude of attributes and variables. Every time customers use their cards, big data suggests the products that can be offered to the customers. These days many credit card users receive calls from different companies offering them new credit cards as per their needs and expenses on the existing cards. This information is gathered on the basis of available data provided by vendors. There are quite a few option available to customers to choose from. Sometimes customers even switch their existing credit card companies. But competition may not always work in the best interests of consumers. It also involves bank’s profit. Competition may also be focused on particular features of credit cards that may not represent long-term value or sustainability. Those paying interest on balances may be paying more than they realize or expect. Some consumers use up their credit limits quickly or repeatedly make minimum payments without considering how they will repay their credit card debt. A proportion of consumers may also be over-borrowing and taking on too much debt, and there are signs that some issuers may profit more from higher risk borrowers (by which we mean customers at greater risk of credit default). With the launch of this credit card market study, we intend to build up a detailed picture of the market and assess the potential identified issues. We plan to focus on credit card services offered to retail consumers by credit card providers, including banks, mono-line issuers and their affinity and co-brand partners. While mass marketing continues to dominate most retailers’ advertising budgets, one-to-one marketing is growing rapidly too. In this case study, you will learn how to improve performance by communicating directly with customers and delighting them with relevant offers. Personalised communication is becoming a norm. Shoppers now expect retailers to provide them with product information and promotional offers that match their needs and desires. They count on you to know their likes, dislikes and preferred communication method-mobile device, email or print media. On the surface, generating customer-specific offers and communications seems like an unnerving task for many retailers, but like many business problems, when broken into manageable pieces, each process step or analytical procedure is attainable. First, let’s assume you have assembled promotions that you intend to extend as a group of offers (commonly called “offer bank”) to individual customers. Each offer should have a business goal or objective, such as: • Category void for cross or up-selling of a particular product or product group • Basket builder to increase the customer’s basket size • Trip builder to create an additional trip or visit to the store or an additional e-commerce session • Reward to offer an incentive to loyal customers

Questions

1. How Big data used in this case study- Define? (20)

Answer 1

Big data is a collection of large volumes of data - both structured or unstructured - which can be analyzed computationally and used to reveal trends, patterns, and relationships. In this case study, Big Data is used to identify the relationship between customers and their usage of credit cards. Companies have information about their customers about their income levels, credit score, risk score, credit limit on their cards, number of credit cards they own etc. These variables with large amounts of data can be processed and used to predict, let's say, the credit line a company wishes to provide to its customer. Companies can hence distinguish between a high risk, medium risk and low risk customer.

Companies can also use the data they have to target customers with a particular interest/taste. Studying the patterns of spending by a customer, companies can provide him/her offers that is most suitable to the customer. They can also broadcast targeted advertisements to their customers based on their likes, clicks etc on social media/digital platforms.