Is your bank powered by predictive analytics?
The problem with most analytics is that it can give you insight based only on what’s already happened. That’s a pretty good foundation for figuring out what might happen next, but as the banking industry knows, past performance is not necessarily indicative of future results.
That’s why predictive analytics is so important. As one of IBM’s latest white papers states, “traditional analytics can tell you what happened and why, but leading organizations use advanced predictive analytics to understand what could happen and to choose the next best action.” Predictive customer intelligence combines contextually related information with various types of analytical data to give you a unified view of customers, products, services and more across different channels. And that view is especially important for banks, which increasingly use multiple ways to interact with customers, including mobile devices, websites, branches and contact centers.
The foundation for this capability comes from new technologies that allow you to look at all forms of big data: structured data (in databases, data warehouses and other systems) and unstructured data (from social media, email, call-center transcripts and so on). Some people think big data just means “lots of data,” but it’s really much more than that. It encompasses numerous types of data that simply couldn’t be analyzed easily without these technologies.
Using predictive analytics, banks can develop better insight into customer interactions across all touchpoints and improve customer service. Imagine being able to track single customers’ activities as that person traverses the website, calls a contact center and then visits a branch. Chances are, all this movement indicates a level of frustration: they didn’t find what they wanted online, and didn’t get the right answers from a customer service representative. Finally, they’re turning to the most expensive resource possible: the branch staff.
With a higher level of analytics, not only can you reach out to that individual to try and solve his or her problem, but you can track the pattern of customers like that one to predict future outcomes and adjust your responses accordingly. Maybe that means empowering the contact center employees to offer a different service to customers who come in after visiting the website; perhaps it involves identifying an up-selling opportunity based on the predicted next best offer; or maybe it requires improving the bank’s website to better serve a particular high-value but high-churn customer demographic.
That’s just one scenario. Predictive analytics can help you delve into almost any query you have for improving your customer service and bottom line.
IBM's Behavior Based Customer Insight for Banking uses predictive analytics to help banks personalize customer engagement and deliver customized actions. And it employs advanced, pre-built predictive models to analyze customer transactions and spending behavior to deeply understand customer needs and propensities, anticipate life events and help provide a unique customer experience.
For more information on predictive analytics, download the IBM white paper on predictive customer intelligence in banking.