Tags: Data Analytics

Ask any corporate financial professional and they’ll tell you - their research and analysis isn’t limited to just financial data. The best analyses is done with richer data sets from a variety of sources in order to uncover new insights. And as much as we at idaciti love the power of usable, accessible financial data, we realize that it doesn’t tell the whole story without additional data. Combining disparate data sources provide more context, depth and the kind of analyses that leads to breaking new ground.

In today’s blog, we’re going to look at how combining data from disparate data sources, both financial and non-financial, can tell a contextual story, with deeper insights and more power.

A cross-section of cross-selling.
Let’s dig deeper into an analyses of cross-selling in retail banking, for example. This type of selling has recently risen to the public discourse, but it’s a long-standing method for generating revenue within an existing customer set without the pain of lead generation. In retail banking, cross-selling can also deepen a customer’s relationship with the institution and provide for a loyal customer base. A customer will enter a bank’s ecosystem and multiple products provide the opportunity for the bank to further entrench the customer relationship. Bob Hedges, global head of A.T. Kearney’s financial-services practice, said "Cross-selling at a bank is “kind of like at McDonald’s, where they ask ‘Would you like french fries with your cheeseburger?’ ”“It’s what they’ve been talking about for decades.” With that in mind, let's look at how cross-referencing data better helps us understanding cross-selling within the banking industry.

A quick search of cross-selling in idaciti across the banking industry yields a listing of a number of financial institutions that mention cross-selling in their financial statement disclosure, with one bank discussing customer refunds related to cross-selling activity and a fine paid for cross-selling activity:

Cross Selling

It seems apparent that is common practice in the financial services and banking industry. More specifically, how do we better understand what this cross-selling activity means to these businesses? Here is an example of one bank’s response to a comment letter from the SEC asking for further clarification on how cross-selling is measured.

Cross-selling is measured

There seems to be ample information here which spells out exactly what this bank includes in its cross-sell metrics and for which lines of business. This information is potentially particularly useful to investors, regulators and customers.

So...why combine other data sources?
Simply because you’ll get the bigger picture. In this case, combining customer complaints data from the consumer financial protection bureau yields a greater insight. Here is the complaint data from four of the largest retail banks, and here are the credit card complains from one:

Number of credit card complaints 2014

Customer complaints related to the number of unsolicited issuances of credit cards varied in number from 14-33 (between 1-3% of all customer complaints). In fact, the number one customer complaint is related to billing disputes across all the major banks (ranging between 12-19% of all complaints). In many cases, it can be disputed that this rate of customer complaints is almost immaterial.

So what do we learn?

 

Given the recent controversy in the banking industry surrounding cross-selling activities and unauthorized opening of accounts, based on the data from the various sources, we can say that this is an isolated incident. The complaints do not reflect a widespread issue in the banking industry and for a small number of banks, there is a lot of detail there for regulators, investors and other stakeholders to learn more about cross-selling activities. This runs counter to the public perception that cross-selling is a rampant, scandalous behavior in the industry, and it tells a deeper story about the insight that multiple data sources can yield.

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