Written by Victoria Zagorsky
To sustain an advantage in a consumer-empowered economy, financial institutions must develop an in-depth understanding of customers they service and markets they operate in. Many marketing and product teams continue to rely on consumer surveys to obtain important information that guides decision making process in developing new products and launching advertising campaigns. However, banks have access to more customer information than businesses in any other sector, and it is vital to effectively leverage information assets.
Currently, transactional data remains one of the keys areas of focus for financial institutions. Analyzing transactions can uncover powerful insights into customer needs, preferences and behaviors. However, transactional data represent only one type of information assets that banks possess. Other important types of information that reside within an organization include both structured data (demographic profiles, website browsing activity) and unstructured data (call center logs, correspondence). In addition to these rich internal data sources, banks can take advantage of the external data.
Social media represents an important source of the data that banks can integrate with their existing information to develop a holistic view of their customers. Social media also offers many opportunities for developing personalized campaigns and offers. One of the areas of interest to the leading banks is utilizing social media data to target customers and leads with personalized offers and campaigns based on the recent life events, such as birthday, graduation or marriage. As the volume, velocity and variety of the internal and external data continues to increase, banks need to equip their employees with tools and skills to glean powerful insights from the data to drive business forward.
Research reveals that over 60% of financial services institutions in North America consider big data analytics to be a source of a significant competitive advantage. Over 90% believe that “successful big data initiatives will determine the winners of the future.” 
While the value of big data initiatives is universally recognized, the key area of focus for many banks continues to be the risk management to comply with the regulatory requirements, while customer analytics, receives less attention. As fraud has significant cost implications to the banking industry globally, it is understandable that banks increasingly use big data analytics to address fraud threats. This typically includes monitoring various types of transactions in real time across multiple channels to identify and take action against suspicious activity. When checking customers’ names against a sanctions blacklist, it is important to avoid the possibility of identifying a false positive, which may have a significant negative impact on the strong existing relationship with the legitimate customer. Using big data techniques enables banks to manage such reputational risks.
Importance of big data analytics in risk management cannot be overestimated; however, customer analytics can potentially become a key source of a competitive advantage for financial institutions. Research reveals that banks applying analytics to understand customer data and to gain insights into attrition have a lead in market share over banks that don’t. 
To maximize profitability, banks focus on driving top-line growth while managing costs and ensuring efficiency of operations. Top-line growth can be achieved via customer acquisition (attracting new customers) and customer life-time value maximization (cross-selling and up-selling to the existing customers). To ensure profitable business, it is also critical to prevent customer attrition. All of these initiatives need to deliver positive ROI, and work best when personalized to individual customer needs. This is the area where big data analytics comes into play.
Personalization is critical to successfully converting leads to acquire new customers as well as deepening the relationship with each customer to maximize their life-time value. Big data analytics allows banks to target specific micro customer segments by combining various data points such as transactions, demographics and sentiment analysis from social media
The US bank, the fifth largest commercial bank in the US used big data analytics to analyze a wealth of data from online and offline channels to develop a unified view of the customer and to identify the most relevant leads for the call center to contact. Additionally, analysis revealed interesting insights into how the customer engagement on the bank’s website could be optimized. These initiatives ultimately resulted in a 100% lead conversion rate improvement. 
Similar big data solutions were deployed by the Commonwealth Bank of Australia. One of their objectives was to personalize offers on the website to visitors based on their browsing activities. Previously, standard banners were displayed to website visitors irrespective of their demographic profile or online behavior patterns. For instance, an offer for a money travel card could be displayed to all website visitors. As only a few prospects were likely to be interested in this offer, the conversion rate was less as compared to the more targeted offers. Now the big data solutions enable the website to analyze prospects’ web searches and generate tailored offers in real time. For instance, knowing that a customer has been looking at properties, the website displays relevant offers, such as home loans. 
Customer experience optimization & customer service
Big data solutions also play a critical role in delivering customer centricity, which enables banks to attract and retain customers. There are two primary areas, where big data solutions are used to optimize customer experience.
1) Customer experience journey. Designing an optimal customer journey requires an in-depth understanding of how customer interacts with an organization through a variety of channels. Big data analytics improves organization’s ability to garner deeper customer insights used to build its business around customer needs.
2) Customer service. Ability to resolve customer complaints or address queries is an important driver of customer satisfaction and loyalty. Every interaction with the call center creates or undermines customer loyalty, and needs to be carefully managed. Big data solutions integrate data from a number of channels and give call center staff access to the unified view of each customer that can help them address customer queries more effectively and service them better. Additionally, issue resolution can be enhanced through analysis of unstructured data (voice recordings) to study sentiment and to determine trends and patterns.
According to Ernst & Young Global Consumer Survey, in 2012, 50% of customers, globally, either changed their banks or were planning to change. As acquiring new customers or re-acquiring deflected customers costs significantly more than keeping the existing ones, banks place a great deal of importance on preventing churn. Big data solutions prove to be highly effective as the improved understanding of customer needs that they create helps achieve two goals 1) predict churn by understanding its early signs and 2) design effective personalized offers to prevent customers from deflecting. Banks need to analyze data from a variety of channels including branch (bank visits), contact center calls, online and mobile banking, as well as social media interactions to develop a holistic view of their customers. Studying customer behaviors may reveal that declining account balance, reduced credit card spending or negative feedback received via any of the channels may indicate high-risk churn targets. Similarly, analyzing customer data can help uncover drivers of loyalty and determine what offers are to likely work best for each customer segment. Developing targeted and personalized retention offers produces significant reduction in churn.
Another area that is currently largely neglected is innovation. In order to sustain a competitive edge, banks need to introduce new products and develop innovative solutions to achieve greater customer centricity. Customer insights can become an important source of new ideas for how to drive value for the customer and to stay competitive. Data may reveal different patterns in a credit card usage of a particular segment, and if launching a credit card to cater to the needs of this group proves to be a viable option, insights into their preferences towards features, benefits and privileges can help develop a highly competitive product.
In summary, risk management is one of the high-priority areas for banks using big data analytics. It will continue to remain so, however, as big data analytics already provide powerful customer insights that help banks drive top-line growth, maximize marketing ROI through micro-segmentation and personalization, achieve greater customer centricity, improve loyalty and prevent churn, we will see an increasing number of financial institutions taking advantage of the big data solutions to grow their businesses and to gain a sustainable competitive advantage.
 Big Data Alchemy: How can Banks Maximize the Value of their Customer Data? Capgemini Consulting, 2014.
 World Retail Banking Report 2013 from Capgemini and Efma. http://www.capgemini.com/resources/world-retail-banking-report-2013-from-capgemini-and-efma
 Big Data: Profitability, Potential and Problems in Banking. 2014. http://thefinancialbrand.com/38801/big-data-profitability-strategy-analytics-banking/
 More Personalized Banking Through Big Data and Analytics http://www.sap.com/bin/sapcom/en_us/downloadasset.2013-09-sep-22-21.more-personalized-banking-through-big-data-and-analytics-bloomberg-2013-pdf.html