Industry Use Cases

Promotional emails and digital ads

On a typical day, people are contacted by several promotional email messages, SMS messages, or ads for things such as hotel discounts, airline vouchers, and shopping discounts. Sometimes, these promotional messages are targeted explicitly to specific customers. With the advent of the internet, companies have been gathering and storing extensive data related to their customers, such as customer demographics, past purchases, browsing patterns, and affluence level.

They use this data to understand the characteristics of their customers and build machine learning models based on those characteristics to identify the likelihood of a customer responding to a particular ad at a given moment. Depending on this likelihood, customers are contacted with the right offers and in many cases even personalized offers.

Recommendation engines

Everyday, we come across various recommendation engines, such as for friends on Facebook, videos on YouTube, products on Amazon, and movies on Netflix. These systems show personalized offerings to people based on factors like their search histories and preferences. It is widely known that Amazon uses recommendation algorithms to personalize the shopping experience for each customer.

Customers see recommended items that are based on their previous browsing behavior, product ratings, similar product purchases and other factors. Along similar lines, Netflix recommends personalized content (movies and TV shows) based on a user's viewing history and ratings, the viewing history of similar users, and so forth. Furthermore, these recommendations are diverse, include new releases, and adapt with time in response to changing user preferences.

Risk management

Banks and credit card companies have used credit scores to evaluate loan or credit card applications for many years. Credit scores typically indicate the risk level of a customer, such as the likelihood of a customer to default. The model takes into consideration several parameters, such as payment history, length of credit history, inquiries, and income, to predict a customer’s likelihood to default by comparing them to similar characteristics of other customers who have defaulted in the past.

Fraud detection

Fraud does not have a constant pattern. Fraud detection model is an evolving process, and by investigating and flagging more and more frequent cases, cases of fraud can be identified. In the case of credit card companies, investigating a large number of transactions one by one is not possible. Therefore, fraud detection models are used that can auto-approve legitimate transactions and raise alerts in case of any transactions that appear fraudulent. Events such as a sudden big purchase after many small purchases, purchases that do not fit the cardholder’s profile, and unusual geographical locations can raise suspicion and block the transaction or card automatically

What is the Data Science?

  • Data science is an interdisciplinary field that uses the concepts of statistics, mathematics, computer science, and domain expertise to extract meaningful insights from data that can generate some business value.
  • A data scientist is someone who is a specialist in these three domains and extracts meaning from the data by performing data and statistical analysis; building and applying machine learning models and algorithms; and visualizing, summarizing and communicating the results.
  • The role of a business/data analyst involves using a lot of domain expertise and data analysis to generate insights from the data, whereas the role of a data scientist is slightly broader and includes machine learning modeling and programming.