Several Indian banks are among the first to adopt to financial technology (Fintech). Axis bank`s thought factory, ICICI Bank`s innovation labs and YES bank`s YES Fintech are some initiatives by financial services providers to embrace the rapid technological innovations in this sector. CitiGPS`s study on digital disruption in banking shows that core revenue could dip by upto 30% unless banks place early and winning bets in fintech.
It is appropriate that banks also understand the presence of non-traditional players in the future financial services industry. Telecom companies and pure-play fintech companies have disrupted the mobile payments space in India. The success of these disruptors is tied to increasing consumer expectations.
A BCG- Google study on digital payment in India suggests that consumers tend to have a great digital experience and expect similar performance from their other service providers.
In this scenario, the marketing departments of banks are willing to adopt these new tools. Writing in the seminar magazine, Mr Rajiv Anand suggests that banks innovate using non-intrusive methods of collecting data. He also suggests that Artificial intelligence and machine learning will play an increasingly important role in bank marketing and service efforts.
Artificial intelligence and machine learning are still testing grounds and use cases are still being built in banking. A financial time study has suggested that there are unrealistic expectations built into the computation of return on investment. However, customer facing AI seems to be providing results.
One of the immediate use cases for AI is in the area of market segmentation. Segmentation is the task of dividing up a market into mutually exclusive parts. The underlying premise being that different groups of consumers will react in a different manner to marketing stimuli.
For example, a consumer with a moderate bank balance would be induced to take a personal loan or credit cards. However, a consumer with a high bank balance and large number of transactions could be interested in a high-end investment portfolio or a car loan for a luxurious BMW.
Banks already have databases of consumer financial transactions, collected using the core banking systems (CBS) like Finacle. Additionally, banks also collect information from borrowers in their loan management systems.Banks in India use the data collected to run loyalty programs on the credit card and savings accounts using a single view of the consumers’ transactions.
Banco Santander is able to effectively cross-sell to its customer base. Authors Gullen and Tschoegl find that the only two appropriate competitive tools with banks is to use marketing to enhance their brand awareness, segment the market and cross-sell their products. The other tool is to use technology to reduce costs and support marketing.
Today, banks hold data on the consumer from their inner records. These records exclude other data which could be more relevant to capture the customers actual behaviour. While traditional credit rating systems are able to capture frauds, loan delinquency post fact, today’s technology provides real time tracking of customers.
Recent research suggests that developing countries like India could be better of using social media scoring to assess credit worthiness of loan customers. A hybrid approach of using both traditional and social media information has also been suggested.These developments suggest that banks have to invest in technology that captures both the traditional transactional data as well as the external social media data. Such a database would have tremendous marketing potential. Today`s banks would at best use a predictive algorithm to predict a customer’s likelihood to take a vacation. However, we know that customers make vacation plans online by discussing with their friends and family on social media.
An intelligent database which captures this data will inform the relationship manager of potential customer needs. A personal loan to cover the costs, or a forex card and travel insurance are easily marketable when customer needs are known in advance.
Such intelligent market segmentation is the need of the hour, machine learning is an important tool to enable this. Machine learning is the process of training a computer to learn by recognizing patterns in a data set. Unlike manual manipulations, using present database alerts, the machine learning tool looks up customer social media handles to create an ongoing series of profiles for the customer. This dynamic segmentation is the need of the hour.
Dynamic segmentation involves using transactional data, social media data and digital marketing campaign data. Customers react differently to the same digital marketing campaigns. Tracking this data and incorporating in the database would improve the success of future marketing campaigns.
Customers increasingly want to be targeted with specific and tailor-made offers. The arrival of new technology that analyses the customer data, both external and internal holds the key to the customer delight.
The article is authored by Prof. Kartikeya Bolar, Prof. Srinivas Reddy & Prof. Jayanthi Thanigan
Prof. Srinivas Reddy, Faculty Associate, Marketing Management has 14 years of experience in Sales & Marketing and has worked in retail asset divisions of ICICI Bank, Axis Bank . He has extensive experience in sales management, team building & execution while being passionate about teaching and mentoring.
Prof. Jayanthi Thanigan, Associate Professor and Chairperson- Marketing and Gender Harassment, holds an Agricultural Engineering degree from Tamilnadu Agricultural University, Coimbatore.She has also completed PGDRM (Post Graduate Diploma in Rural Management), from IRMA, Anand (1994-96). She commenced her career with Gujarat Co-operative Milk Marketing Federation (GCMMF-AMUL) and worked with GCMMF for 10 years. She has worked in various capacities such as Senior executive (Marketing), becoming the first lady Branch Manager in the organization heading Chennai Branch. As a visiting faculty in many reputed B-Schools from 2006 to 2013, she has obtained Ph.D from Anna University, Chennai in the year 2013. Joining TAPMI in 2014 for commencing full time academic career, she has held administrative positions such as Associate Dean (Academics), Area Chair (Marketing) and Team leader for AACSB Accreditation (successfully getting AACSB reaccreditation for next five years).
Prof. Kartikeya Bolar, Associate professor & co-chair – Operations and Information Science Area, has an overall experience of 13 years of teaching and research. Prior to joining TAPMI, he has taught across various b-schools in the country like IBS Hyderabad and Xavier Institute of Management and Entrepreneurship Bangalore. He was a Visiting Scholar at the College of Business Innovation, University of Toledo, Toledo (OH). He was the best outgoing student of the School of Management, Manipal University.