End-to-end customer intelligence frameworks, drawn up specifically for banks which take advantage of the available data is a rarity.
The journey towards obtaining customer intelligence is always extremely tricky. Customer Intelligence is a qualitative subject, and a grey area for several enterprises.
Since obtaining customer intelligence is not a one-stage process, banks in India struggle at different stages of the process and feel confused and elated at the same time. Elated, because they feel that customer intelligence has been implemented. Confused, since the application through the customer intelligence process isn’t responding as well as the predictive analysts foretold it to be.
Each customer intelligence framework focusses on a particular concentration point or a combination of points such as data ingestion or extraction, machine learning algorithms or data cleansing. However, end-to-end customer intelligence frameworks, drawn up specifically for banks which take advantage of the available data and the advancement in technology is a rarity. But, that shouldn’t be the case.
So, what’s been going wrong and what is the appropriate corrective measure?
Experts have agreed that it’s important to obtain quality data. But it doesn’t stop there. There needs to be a particular data management process that cleanses your data of incorrect data and values – data that might alter the course of action from the best advised action. Also there needs to be a machine learning process that feeds data into its algorithm to detect patterns among magnanimous amounts of data. These can be achieved through a customer intelligence framework that includes an iterated three or four stage process.
Banks and financial institutions in India have a lot to benefit from the implementation of a customer intelligence framework.
Customer retention, acquisition and increased profitability are some of the major benefits from customer intelligence for banks which contribute to your ROI in the long run. As these benefits pile on, it’s also important to note that the rate of implementation for this platform among Indian banks has been low.
Hence before stepping out into obtaining quality customer intelligence, Indian bankers and decision scientists should evaluate the major issues which prevent the use of quality data for higher purposes. A comprehensive evaluation or analysis would provide a deeper understanding on the different stages that are hampered by bottlenecks.
One of the biggest challenges in the Indian banking industry is the presence of multiple internal issues which restrict the flow of data or prevent the opportunity to get actionable insights at each stage of the process. Once these bottlenecks are watered down, it would help the bank to deliver services to customer in a manner which benefits both the parties.
Author: Anand Subramaniam is the Head of Artificial Intelligence Practice at Aspire Systems. The views expressed in this article are that of the author and not of moneycontrol.com or its management.