Big data is a buzzword in a myriad of industries. In fact, analyst firm IDC recently predicted the market will grow to $16.9 billion by 2015, up from just $3.2 billion in 2010. However, the customer-centric model of financial services firms gives data unprecedented opportunities to further market research and boost customer satisfaction.
In an internet-enabled world, key learning about customers can be gained everywhere from mortgage applications to Twitter, providing financial services firms with an unprecedented amount of data. While this data offers valuable insight for financial services firms, it also has challenged the industry with new areas to monitor for feedback.
As an industry still plagued by high churn rates and customers having numerous options for where they bank, financial services companies should be looking to emerging big data tools as the answer to finding hidden consumer sentiment on a real-time basis.
The importance of big data relies on the right format for analysis. Emerging big data tools provide companies with the ability to analyze far greater quantities and types of data in a shorter span of time. For example, both structured and unstructured data sets such as RSS feeds, text messages, and e-mails can be analyzed to uncover insights. Retailers and other technology companies have already made huge strides in this area, but the doors are now open for financial services companies to improve customer segmentation, product development, and customer service.
Sentiment analysis and predictive analytics are two of the best techniques financial services companies can implement to address the retention challenges of the industry. These tools create economic value by providing the technology to tailor products to customer wants as well as understand fraud patterns, reduce credit risk, and build strategy according to customer expectation.
Capturing Customer Feedback Through Sentiment Analysis
Consumers today share their thoughts through social media channels just as often as to customer service representatives. When captured and managed, this information provides valuable and unfiltered insights into what customers are thinking.
Moving away from traditional sentiment analysis methods of survey research and focus groups, these sentiment analysis tools can give companies innovative ways to improve their products and predict consumer behavior. They also provide analysis on a real-time basis, allowing fast decision-making and reactions to any negative opinions that have appeared.
For example, in 2011, when one of the leading financial institutions in the U.S. announced its decision to charge customers a debit card fee, it formed its decision to retract the fees based on the immediate negative uproar from customers.
Sentiment analysis tools capture customer feedback from social media platforms, customer service interactions among other platforms to help banks evaluate the potential impact of their decisions. They work by tying words and unstructured communications to consumer emotions, serving as key inputs for strategic decision-making.
Sentiment analysis tools also play a large role in bank’s loyalty and rewards programs. This has become extremely important across the industry as loyalty and rewards programs have become commonplace for attracting and retaining customers. By examining customer confidence indices that are driven by specific data elements, banks are able to judge the mood of the market and decide how to best reward customers.
While these technologies are still maturing, many are advanced enough to drive real value by allowing financial services companies to understand customer likes, dislikes and preferences for product and service improvements, and help them gain a competitive advantage in a crowded market.
Using Predictive Analytics to Capitalize on Customer Insights
One trend infiltrating this industry is customer demand for simple, fast and inexpensive means to conduct both financial and purchasing transactions. However, this becomes a challenge as consumer needs are becoming more diverse and unpredictable.
Predictive analytic techniques are a great way to mine large amounts of historical data to determine the likely occurrence of events in the future. By querying, visualizing and reporting these datasets, financial services companies can generate actionable insight—illuminating behavior and transactional patterns—to move forward with decisions on product and service strategies.
These tools can also help banks build models based on customer spending behavior and product usage to pinpoint which products and services customers find most useful and what they can deliver more effectively. Such a model can help companies increase their share of wallet, garner loyalty, and most importantly, increase profitability.
Financial services companies can also find value in predictive analytics ability to help in fraud detection. This increase in data allows for a more complete profile of customers, helping banks and brokers detect fraud earlier than existing approaches.
“Predictive Scorecards”, which help determine the likelihood of customers defaulting on payments, are also enabled by these emerging analytics tools, helping banks to mitigate the risk they take on.
Integrating Big Data in Your Operations
Sentiment analysis and predictive analytics are two of the biggest big data tools that are proving their worth in the industry. The beauty of these solutions comes from their ability to be easily integrated into the operating model in a manageable way. The biggest challenge the industry has faced has been in integrating big data in a strategic way and that benefits their business and impacts the bottom line.
By starting small, growing organically and striking the right balance between gaining the desired insight and becoming overloaded with data, financial services firms will truly recognize the gains they can make in customer satisfaction, retention and expansion, boosting profitability and finding the economic value.
Karthik Krishnamurthy is the Vice President of Enterprise Information Management for Cognizant