To say the term “big data” is getting thrown around in banking these days is nearly as great an understatement as “Usain Bolt is a sprinter.”
The fact is “big data,” or more specifically the use of massive loads of data to extract meaningful and/or actionable insights, is approaching mythical status in bank customer acquisition, as in, “If we can master all the data we’ve got, we can increase share dramatically,” bankers say.
That’s true. If banks could do that, there is share to be had. American Express, as an example, has been at the forefront of this. Way back in 2009, American Express Co. launched an analytics and consulting business that, as of late last year, now draws on the purchasing behavior of its 90 million credit card holders across 127 countries. The unit, American Express Business Insights, sells Amex proprietary data to direct marketers to enhance customer acquisition and retention programs. Big data is also at the core of Movenbank’s Cred initiative, a video which the company released this week.
But what about the rank-and-file banks? To them, big data is something of a mythical land to which they will arrive in the not-too-distant-future. Better then viewing it mystically, bankers should be looking for tangible first steps to get the process rolling, and that means identifying the data that will help banks identify, solicit and secure not just new customers, but the right new customers.
I’ve been in a LinkedIn discussion this week on the Bank Innovation group there (see the discussion here) that has been centered on this question (thanks specifically to Joe Gregory of Orbograph for his insights on this), and one data set that might have value is check data. In check data, you can gather names and addresses of banks and bank customers of certain geographic areas. The check data offers two actionable data sets:
Geographic data. Where are potential customers in relation to your bank?
Demographic data. Who are the customers near you, and why should they come to your bank?
Once the addresses from the checks have been geocoded, you can select a radius around select locations to mail incentives and “bank switcher” mailings.
Big data obviously is all about scale, particularly when it comes to social media, the mother of all big data sources. I came across an interesting blog from EMC, the software provider, on big data, and buried in a post from April was an interesting example of how big data can be leveraged. The example should spark some ideas for leveraging social media data:
… So let me give you an example of a bank that uses social media data to improve customer acquisition, retention, and maturation. This bank has a brokerage firm so the bank’s customers are the brokers who are servicing clients like you and me. The bank’s key business challenge is retaining their brokers since when a broker leaves they tend to take their clients with them. Therefore, the bank has to make sure that they’re doing everything possible to make their brokers successful.
First, the bank expects to help brokers optimize upsell/cross sell opportunities by leveraging social media to understand more about customers and their interests. For example, let’s say I am a broker monitoring a customer’s Facebook news feed and the customer posts that his daughter just got engaged. The broker concludes that the customer will have a major financial event in the next year and can take action by offering the customer a relevant product or service that will help with the event.
Second, the bank expects a 20X improvement with customer acquisition via improved targeting by knowing the Facebook friends of high value customers and leveraging cohorts analysis.
Last, the bank hopes to identify “white spaces” in the market by using social media to monitor customer sentiment and market trends across Facebook, LinkedIn, Twitter, Foursquare, etc. Recently when the markets collapsed, customers were using Facebook and Twitter to complain about their 401k programs. The bank can now identify these unhappy customers more quickly and offer an alternative financial product/service.
It’s the idea of “white spaces” that struck me as the right way to think about it. Where are the gaps? Where are the opportunities? Where are the “white spaces”? It’s a great framework for crafting a big data goal.
And those framework needs parameters. These are some high-level considerations, courtesy of Accenture. If you are looking for the “white spaces,” consider these parameters for the data first:
- Information scale. Do we have enough information to differentiate ourselves in the marketplace? (Having a customer base in the hundreds of thousands is not enough in an age where some companies count heads in the tens of millions.)
- Data scarcity. Do we have data elements that are difficult to replicate in the marketplace? (Leaders should determine if the company’s core business creates effective barriers to third-party data collection and whether other businesses can collect alternative data that would serve as a substitute of comparable value.)
- Data blending and analytics. Can we combine our data with information from others and then use sophisticated data analysis to create differentiated products? (No single organization has all of the data it needs to meet the demand for information services products; as a result, the ability to take your own information, combine it with other data and make it uniquely valuable via robust analytics will be critical to success.)
We are a practical bunch here at Bank Innovation, so I don’t want to get too caught up in the mechanics of this. Suffice it to say, there are clearly opportunities here, and they are not too far from reach for most banks. The starting point matters, as no doubt Usain Bolt would agree.