Big Data Analytics and Customer Service in Banking

January 13, 2015
Vijai Shankar, Sr. Director Industry Marketing

Retail banking has been undergoing a fundament structural shift since the financial crisis, and, five years later, the repercussions continue. Initially, banks were focused on managing risk and directed most of their IT investments towards meeting new regulatory requirements. Now banks are moving to new priorities that focus on building customer trust, transparency, and business capabilities for higher profitability. According to a recent Bloomberg survey of banking executives around the world, more than 70 percent said that customer centricity is very important to them. But, according to a recent Forbes article, only eight percent of the customers believe that banks offer their customers superior customer service.

The bottom line: understanding your customers' needs, preferences, sentiments, behavior, and propensity to switch has become paramount for banks in order to improve customer service. But how do you do that? There’s clearly a lot of work to be done.

Knowing the customer and predicting the intent of your customer:

Banks have plenty of data from ATMs, websites, phone calls, emails, and mobile transactions, but have done little to leverage it. All of this data can be linked back to the individual customers and that's where the power of individual-level marketing comes in. By getting this data out of individual silos and mining it, banks can better anticipate what customers are trying to do and personalize their experience.

Banks need to organize the knowledge they have about their customers, aggregate it, make effective use of customer data from multiple sources, and then act on it. Using big data and prediction to analyze and better understand customers allows banks to understand their customers and make the right offer at the right time to the right customer. Banks that excel in these areas and engage with their customers by becoming the key custodian of all of their value transactions and empowering them based on their role, location, and context will be the real winners in the customer acquisition and retention battle.

How Predictive Analytics can help with personalized customer experience

Banks can also utilize the massive amounts of data on their customers and prediction to introduce new offerings that target predefined segments based on location, demographics, psychographics, and other factors while also increasing revenues and deepening customer relationships. When a customer contacts a bank about a change of address, for example, predictive analytics can be used to identify the move to a different city as a potential mortgage client and make an offer to chat with a mortgage specialist.

In a highly competitive banking market, leveraging big data represents a growth opportunity for banks. Combining big data and predictive analytics with existing customer service technologies is redefining the future of customer centricity in banking and will unlock the value of customer data. This provides banks with the ability to serve customers through the right channel and at the right time with superior overall customer experience. Leveraging big data predictive analytics with customer service can help banks focus on building this customer trust, thus leading to overall higher profitability.

Vijai Shankar, Sr. Director Industry Marketing
Vijai Shankar, Sr. Director Industry Marketing

Add new comment