Analytics is one of the most transformational technologies today, which finds application in multiple industries like FMCG, hospitality and services, manufacturing, financial and banking, retail etc. There has been a rapid transformation in the way the technology works. Analytics is embedded in most of the technologies, and provides competitive analysis of business performance. Technology-driven analytics such as AI, automation, robotics with analytics, are proving to be increasingly disruptive.
Gartner predicts that natural language generation and AI capabilities will be standard fare in 90 percent of modern analytics platforms by 2020, and that at least half the analytic queries will be generated or automated using those technologies in the same time frame.
Machine learning with analytics is embedded in digital banking like internet banking and mobile banking, where there a large number of customer interactions take place. With machine learning and natural language processing, customers can search in their natural language or interact with chat bots to address their queries. In the process, the analytics engine embedded in the application is used to analyze and understand the customers’ behavioral patterns, their preferences etc. in order to provide the right products. Big data is used for predictive and prescriptive analysis to understand the customers or help to make important decisions and strategies based on the outcome.
Banks widely use analytics for various processes including loan application processing to gather customer data and present it to the credit officer to make the right decisions, predict the longevity of the customers’ relationship with the bank, understand the products relevant to the customers, and understand which customer type or line of business to target in order to maximize revenues.
Banks using analytics can provide an array of customized products and personalized services to their customers. A reality check needs to be in place to understand if this brings value to the customers. Analytics used by banks – is it a boon or a bane for the end customer? Advertisements for deposits, credit cards, and loans bombard customers on every digital channel, in the process customers may end up choosing a product or service not required by them.
In order to gather customer feedback, market research techniques which involves surveys are used. In-app feedback on digital channels enables personalized feedback from the customers and helps to offer customer-focused products and services. Analytics is used to analyze customer feedback and provide relevant advertisements to them. Analytics should also be used to determine the products / services which are least interesting to the customers and remove them from their immediate view. Say for example, if a customer does not click an ad related to credit cards then a bank can refrain from presenting to the customer again for a stipulated time period. Instead, the bank can show ads or information about the products / services which are relevant based on the customer’s day to day transactions. In case a customer has huge balance in the savings account, ads related to term deposit products can be posted to the customer to help them maximize their return. Providing different and competitive products with attractive interest rates and terms is not sufficient, banks need to provide products relevant to the customer for higher customer retention.
While most products and technologies come with embedded analytics, it is the need of the hour to invest in dedicated infrastructure for analytics in order to reap higher benefits. A survey conducted by Bank of America in 2016 shows that 62% of Americans cite digital as their primary method of banking. Thus it is clear that more and more customers are opting for digital banking and investment in digital technologies is indeed a competitive advantage.