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Banking

MAKE MORE THAN A STATEMENT: BIG DATA AND THE BANKS

BIG DATA AND THE BANKS

Paul Alexander, CEO & Co-Founder of Beyond Analysis

BIG DATA AND THE BANKS

BIG DATA AND THE BANKS

There have been big changes since I first opened a bank account. Internet banking on a smartphone was barely a sparkle in a software developer’s eye as we didn’t have access to the World Wide Web, and our smartest phones were BT payphones into which you could slot 10p pieces. Back then, cheques were in everyday use; we can now pay for small items by touching a bank card or mobile phone onto a till sensor.

One area of banking has, however, remained pretty much unchanged: the humble bank statement. Whether delivered through your post box or arriving in an email inbox, the layout and content of a bank statement has barely changed since the introduction of computer programmes back in the 1960s. Name, address, account number, sort code and the transactions carried out by your account in the last month or so. The bank’s logo usually sits somewhere in the corner.

Paul Alexander

Paul Alexander

It seems almost inconceivable that the bank statement has not received a makeover when so many other aspects of banking are almost unrecognisable from 30 years ago, especially given that it belongs to an industry that it fed by data, statistics and numbers. Where statements are concerned, banks have been guilty of failing to capitalise on the valuable insight that could be gained from their data and used to enhance customer experience for the greater good.

Germany-based Wüstenrot Bank recently announced that it is trialling a cashback loyalty programme for retail banks, which will see offers appearing alongside customers’ online bank statements. These can be redeemed by following the links and paying with a credit or debit card, opening up a new revenue stream for banks and providing small cashback incentives for account holders.

Red Zebra Analytics, the firm behind the project, plans to begin signing up retailers to the scheme as soon as a decent chunk of transactional data is generated by the trial. The organisation’s CEO boasts that closer relationships will be built between banks, customers and retailers, making it easier for retailers to quickly put together highly targeted marketing campaigns in the process.

Not far enough

The trial launch of this project represents a good first step in the right direction, but in my opinion banks should be going much further. Financial services organisations are currently going through a period of unprecedented change: banks need to start increasing their revenue streams by doing whatever they can to fight for customer retention in an increasingly flooded marketplace.

Competition has certainly heated up in recent months; however some of the best-known banks and building societies still resort to dangling the carrot of a cash incentive in front of competitors’ account holders, promising them payment for switching across and closing their existing account. Many banks have also been guilty of depending on customer apathy instead of formulating a genuine customer retention strategy; as it is now easier than ever to switch banks many are now desperately running to catch up. I would argue that, for many customers, loyalty does not come into the equation when talking about their bank; banks therefore need to be realistic and focus on customer engagement rather than equating the longevity of bank accounts, mainly due to apathy, to loyalty. Will a few targeted cashback offers make much of a difference to customer engagement in the battle for accounts? Probably not for everyone, no.

Banks have always had access to a whole raft of data but the majority have never chiselled through enough layers to realise what a goldmine they are sitting on. Retail banks could use their customer transaction data sets much more effectively by providing recommendations compared to the spending habits of similar account holders. For example, take comparison and review websites such as TripAdvisor. It can take an age to sift through page after page of posts and even when you’ve done that, it can be hard to work out which glowing references you can trust. This is where, in my opinion, the banks are missing a trick. With the data it already possesses, a retail bank would be able to tell an account holder where people matching a similar profile to them have been, thereby providing them with a recommendation based on data-led analysis – surely a far more trustworthy solution.

Assume the role of the attentive shopkeeper

Historically when a customer purchased some paint from their local hardware store and needed some more, they would simply return to the same place – and ten to one the local shopkeeper would remember their purchase. The shift from yesterday’s retail model to today’s hypermarkets and out-of-town retail parks has resulted in that relationship being lost along the way, but this represents an incredible opportunity for banks to step behind the counter and assume the role of the man in the big green apron.

By making online bank statements easier to navigate, search and manipulate, banks could empower account holders to use their statement as an enabler for smarter, quicker purchases – rather than simply a reminder of what they’ve spent. That’s the kind of service that would be a serious competitive differentiator and a true customer engagement tool.

Big Data is a relatively new term in the banking world, but it is something financial institutions have always had at their disposal. The sector is in a great position to reap the rewards a focus on Big Data can bring, it just needs to get its house in order first.

Here’s a six point plan to help retail banks make the most of their own Big Data:

  1. Be clear on your business objectives, and those of your customers
  2. Understand what data you have right now (and what you don’t)
  3. Align points one & two and then create your own Big Data plan
  4. This should NOT be a three year, all-singing all-dancing plan; plan in detail for the first 12 months, then in high level for years two and three
  5. Only include actions that can be tested and measured – so you can learn and develop proof points
  6. Be committed to your data; if you’re in it for the long haul so will be your customers

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