When Big Data is not Big Understanding


Good article from Tim Harford (he of the enjoyable “Undercover Economist” books) in the FT last week called “Big data: are we making a big mistake“. Tim injects some healthy realism into the hype of Big Data without dismissing its importance and potential benefits. The article talks about the four claims often made when talking about Big Data: Continue reading

Omni-Channel Personalization in the Banking Industry


Data-driven personalization is the key to big gains in customer satisfaction, customer acquisition, and account growth. But the complexities involved in implementing a cross-channel personalization platform can be overwhelming. Here’s a break down of the pieces involved with crafting a personalized omni-channel and cross-channel experience.
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Leveraging Big Data for Community Banks


Community banks can take advantage of big data just like larger institutions by leveraging personal financial management platforms.

For some community bankers, big data brings to mind stacks of customer data pasted together like a kindergarten art project. Yes, your customer relationship management (CRM) system captures lots of information, but that information oftentimes sits unused. Is it important that you know Mary Smith comes into the bank at least once a week to deposit a check? Yes. Does it help to know that she uses mobile banking to check her balance? Yes. Is it valuable to know that she is carrying a high interest rate auto loan with your competitor? This is invaluable.
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Bridging over the Financial Services skills gap


”Big data” in the Financial Services industry has amplified the need for professionals capable of deriving value from it. In 2012, our research explored the impact of poor modelling; specifically how imperfect implementation of models can negatively affect institutions. The report found that in financial institutions, the real value of data is being lost from ineffective interpretation. As a result, we found that model-driven development paradigms are the key to the future growth of the Financial Services industry. Furthermore, this is driving the need for graduates skilled in Science, Technology, Engineering and Mathematics (STEM) subjects who, when hired, can use their skills to make a strong contribution to the business.
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Using voice of customer analytics for better insight


Attrition models typically consider only structured attributes and past behavior of customers to segment them based on their propensity to defect.  However, this methodology has its shortcomings. Specifically, it does not take into consideration the unstructured data, which is generated in customer conversations.  For instance, a customer, while interacting with a bank’s customer service agent might show his displeasure with the bank’s services and bring it to their notice through chats or messages in Facebook or Twitter.  Frequent incidents of this nature are a good indicator that the customer is ready to defect. Models based on text mining of unstructured data have clearly indicated a strong correlation between certain customer queries and intent to defect.
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Leveraging regulatory data to improve customer experience


We’re in the “Age of the Customer” –which means increasing challenges to meet evolving consumer demands, resolve problems rapidly, and comply with a changing regulatory environment. Customer experience is more important than ever to compete effectively and win business.

In a recent article published by DataInformed, Beyond the Arc CEO Steven Ramirez highlighted how companies can gain a big advantage by tapping into valuable sources of publically available customer feedback data, such as social media and the Consumer Financial Protection Bureau (CFPB) customer complaint database.
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