Winning hearts and minds using Big Data Analytics

Dominic Vincent Ligot, industry consultant, Teradata, discusses how banks can win back public confidence with rampant data breaches and perceived indifference

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Winning hearts and minds using Big Data Analytics Ligot: Banks need to take some practical steps towards turning consumer-perception obstacles into data-driven business opportunities.
By  Dominic Vincent Ligot Published  December 13, 2016

Every morning when we open our newspapers or log on to our trusted news websites- we’re hit by a storm of stories of the stumbling world of banking and finance. The word is that we’re in a financial meltdown- fraud, the massive security breach at Qatar National Bank, the fallout from UK’s Brexit from the EU, a veritable roll call of financial crises and misdemeanours – factual concoction spiced with conjecture, colouring the public perception of what banks really do. Even in Saudi Arabia, we often come across news about the banking sector going through a challenging period.

It doesn’t come as a surprise then that the public’s perspective of the banking fraternity is shrouded in scepticism, despite the essential role that banks have played in influencing civilisation and development for centuries. News concerning security breaches, lack of service expansion and poor customer service seem to get the attention of readers, creating an air of unease while banks choose to downplay their apprehensions and put on a brave face.     

Win back customer confidence

To win back customer confidence and maintain their place in the face of revolutionary digital disruption, individual banks (as well as the industry as a whole) need to take a long hard look at their traditional business models and operational practices. Some banks have already begun the digital transformation journey – adopting new technologies and tapping existing data resources to develop better products and services. Big Data and Analytics are the key but largely, their full potential still remains unrealised. Banks need to take some practical steps towards turning consumer-perception obstacles into data-driven business opportunities.

Payments data

Start with the most under-appreciated dataset. Payments reveal a great deal about each user – how much they’ve paid, what they paid for, who was paid, the banks involved, transaction time and location, and so on. In fact, a customer’s payment profile says much more about her, or him, than any social media metric or record. Payments data is highly accessible and can pinpoint lifestyles, detect which companies make up a supply chain, and plot spending trends by time or place. At the same time, although customer data is not as dynamic as payments data, in banking systems it can be attached to other profiles such as payments and credit history to enhance analytics and create successful “Next-Best-Offers”.

Fintech sensibilities

Should banks be worried about the Fintech boom? Not necessarily. Banks have both the resources and the ability to retain their position in a way that start-ups really don’t. They just need to adopt a bit of Fintech thinking. Banks can try some of these simple and practical things in the short term that could make a significant difference:

- Play with some data around a recommendation engine – It can be done as an experiment with a few people. Group customers by preference, products by customer, and transactions by pattern similarity. Everyone’s always looking for the elusive ‘Single Customer View’, but guess what? A ‘Partial Customer View’ linking two to three product portfolios is already enough to get started.

- Look closer at payment and behaviour data – Payments can help banks understand the sequence of events that leads to somebody leaving the bank. Payments can reveal hidden social networks within a bank’s portfolio. Customer-to-customer, customer-to-merchant, company-to-company, product-to-product – what could you do if you knew these relationships?

- Fraud and compliance – As mentioned before, banks are incredibly adept at regulatory compliance and fraud mitigation. But the industry needs to start getting better at text analytics and using web behaviour to detect high-risk patterns. Insights such as ‘who clicked on what before fraud happened’ can be very enlightening. These days, companies can match weblog data with branch data and check the difference between web and in-branch behaviour.

- Service experience – In the brick-and-mortar era it was ‘Location, Location, Location’. Now, in the digital era it’s ‘Customer, Customer, Customer’. Use event data to spot processes that are causing problems for your customers and fix them. Contact Centre logs are a hidden source of insight. It doesn’t take much to parse them for sentiment and recurring patterns. There could be new products hiding behind these complaint logs, if only banks were inclined to look.

- Improve the mobile experience – Many banks have mobile apps but they usually concentrate on facilitating payments, fund transfers, and account management. What if a local bank’s app could act like Mint and provide the user with cool ways to manage budgets, see financial profiles at a glance, and even offer helpful advice? You can parse those mobile servers for hidden patterns in data (location profiles, IP addresses, mobile browsing, etc.) – the ‘fingerprints’ of customer satisfaction.

It takes considerable amount of effort to fix troubled relationships but these ideas could be the first, tentative, steps towards reconciliation. And once the ‘relevance’ and ‘confidence’ fences have been mended and an enterprise-wide digital transformation strategy embedded, banks can get back to developing meaningful, long-term, data-driven customer relationships instead of settling for a diminishing series of ad hoc, one-time associations.

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