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 Chuck Schaeffer How Financial Services Leaders Are Succeeding with Big Data

 

Achieving the Elusive 360 Degree Customer View

Financial services big data projects often stall with the initial question of where to get started.

In research published by the IBM Institute for Business Value and the Said Business School at the University of Oxford, 55 percent of banking and financial services industry respondents with active big data projects identified customer centric objectives as their organization's top priority when asked to prioritize their top three big data objectives.

The below chart shows how financial services executives rank their big data objectives and how they compare to other industries.

Big Data Objectives
Source: IBM Institute for Business Value

The research makes sense. These financial services institutions are focusing on the customer in order to achieve revenue and profit objectives. So once you understand the top big data priority among FSIs, you then want to understand how to go about achieving that objective.

Achieving customer centricity and engaging customers in a one to one fashion at scale is generally best accomplished by creating finely tuned customer segments and then appending customer profiles with demographic, transaction, environmental, behavioral and social data. Big data plays a big role in at least four of these data types.

360 degree customer view

Customer Demographic Data

Financial services companies are fairly routine in customer onboarding, but far from routine in extending the data acquired for compliance to other purposes.

Demographic data such as age, gender, occupation, some lifestyle (i.e. urban or rural) and social class (education and income) is captured during the KYC process. But this customer data has a way of stalling after KYC.

Demographic data provides the initial basis for customer segmentation – an essential best practice in customer relationship management. However, the most common mistake in customer segmentation is to group customers by their upside potential to the financial services institution without regard to what these customers want from their FSIs. While demographics are a common starting point for segmentation, they are relatively stagnant and not good predictors of customer behaviors or contribution to key performance measures such as revenues, costs, profits and lifetime value.

To better engage customers for these business purposes, it's extremely helpful to take the next steps of further appending customer profiles with transaction, environmental, behavioral and social data.

Next: 360 Degree View Customer Data Types >>

Financial Services Big Data360 Degree Customer View360 Degree View Data TypesSocial Customer DataBig Data For SalesBig Data For MarketingBig Data For Customer Service

 

 

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The most common mistake in customer segmentation is to group customers by their upside potential to the financial services institution without regard to what these customers want from their FSIs.

 

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