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Using big data analytics to win back customer confidence

 

5 ways banks can use big data analytics to win back customer confidence

The entire financial sector is teetering on the brink. Or so it is said.

It almost seems that no matter where you are, you look at your favourite newspaper and you are assailed by successive stories about problems in the banking world.

The seemingly interminable fallout from Brexit, another case of fraud – this veritable litany of financial crises and misdemeanours drops like ticker-tape smeared with rumour and innuendo, influencing the public’s notion of what banks actually do.

Locked bank vault door in retail store

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-night stands.

The entire financial sector is teetering on the brink. Or so it is said.

It almost seems that no matter where you are, you look at your favourite newspaper and you are assailed by successive stories about problems in the banking world.

The seemingly interminable fallout from Brexit, another case of fraud – this veritable litany of financial crises and misdemeanours drops like ticker-tape smeared with rumour and innuendo, influencing the public’s notion of what banks actually do.

It comes as little surprise therefore, that despite the industry’s healthy growth and the integral role played by banks in society for hundreds of years, the public regard the banking community with suspicion.

It is the security breaches, lack of service development and poor customer service that are at the forefront of the public mind, while those within the industry cast a glance skyward and play down all such concerns.

Winning them over

To regain customer confidence and maintain their place in the face of radical digital disruption, individual banks (as well as the industry as a whole) need to conduct a thorough examination of their traditional business models and operational practices.

Banks need to take some practical steps towards turning into data-driven business opportunities the obstacles that distort consumer understanding.

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.

1. 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 2 to 3 product portfolios is already enough to get started.

 

2. Look more closely 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?

3. 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.

4. Service experience

In the bricks-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.

5. 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.

Okay, these 5 things won’t turnaround troubled relationships on their own but they could be the first, tentative, steps towards reconciliation.

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-night stands.

This article was originally published on www.information-age.com and can be viewed in full here

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