RBS wants to take banking CRM back to the 1970s with huge data investments

SUMMARY:

Speaking at PegaWorld in Orlando this week, RBS’ ‘data guy’ Christian Nelissen explains how data can be used to deliver a more personal customer relationship.

RBS_2588550kChristian Nelissen, The Data Guy at Royal Bank of Scotland (no, really, that’s his job title), is spearheading a data programme at the bank to try and take customer relationship management back to the 1970s. He wants CRM to go back to a time when banks knew their customers personally and not everything was a sales pitch. It was about doing what was right for the customer at that point in their life.

Long term investments in customers, as opposed to short term gains, is the aim of the game.

Obviously this is a much harder task today, where customer interactions aren’t just face-to-face, but come from a number of touch points and from across a number of channels, both online and offline. However, Nelissen believes that the key to getting this right is data – huge investments in data analytics. Whilst the programme started small some five or six years ago, Nelissen’s CRM data projects are now receiving hundreds of millions of Pounds in investment from the business.

It’s without a doubt that this plan is central to the bank’s plan for creating a more stable, reliable and trusted institution. For those of you unaware, RBS was hit particularly hard by the 2008 financial crisis and had to receive a £45 billion bail out from the taxpayer. And whilst the bank reported a £3.5 billion loss in 2014, that’s down from a £9 billion loss the previous year.

Nilessen was speaking at PegaWorld this week in Orlando, where he explained that he is using the Pega platform to create a ‘brain’ for the organisation that allows employees to establish a next best action for customers based on what’s best for the customer, not necessarily best for the bank.

It is doing this by building decision strategies, which combine predictive analytics with algorithms through mining large sets of data, and using adaptive analytics with self-learning algorithms that improve with each interaction with the customer. It is then using traditional business rules that help the bank’s staff prioritise between the possible decisions.

Nelissen said:

What I want to do is go back to the past. If you think about it, we used to have an always on customer brain, we had one for a really long time. One of the jobs that I have and that my team has is taking banking back to the ’70s. It’s a little counter intuitive, but in the ’70s there were two really important characteristics that we had in terms of our relationship with our customers.

christian nelissen
The Data Guy

Firstly, we knew our customers individually, we knew their families, knew their relationships, knew where they were in life. We knew them individually and customers want to be known individually.

The other thing is that when we talk to them, we talk to them about what’s right for them, with the idea that if we did that often enough, in the long term, we would make money out of them.

It’s a conversation, not a sales pitch

Nelissen was keen to assure us that this wasn’t just spin and the bank had actually been making decisions that were best for the customer, but were sometimes costing the bank. But he said that the results were paying off, providing two examples of improved interaction with customers.

The first was that because of this ‘brain’ and the resulting conversations with customers, 40% of all RBS appointments that now happen in branch are driving from some sort of prompt or prospect that was put into the system. That’s up from zero a few years ago, according to Nelissen. He also said that RBS has also managed to improve mortgage retention by 5% last year.

There’s always a point in the conversation where I have to explain that this isn’t about selling. People always give me a knowing look, but it’s really not about selling. If you think about what was important in the 70s, it was about having a conversation with the customer, where we are relevant, timely and we can deal with whatever is in the customer’s head. We actually actively discourage our people to push sales through these channels.

Nelissen provided another example, where prior to this project, RBS had prioritised sending hundreds of pegasystemsthousands of credit card mailings out the door every month, because that’s what was seen to make them money quickly. He said that that was “all the bank cared about”. But Nelissen argues that this isn’t the right approach.

You could be, as a customer, number 200,000 on the list, right at the bottom, and you could be the number one customer that needs a savings account and we would still send you the credit card mailing. Because that’s what we cared about, that’s what was important to us.

And the response rates were in tiny percentages. We would get excited about a 0.2% lift on a response rate, but if you subtract that response rate from 100, that’s the number of customers that we are letting know every time that they get something from us that we don’t care about them. That we see them as an opportunity to make money. It’s not a great model.

We now put things in the system, sometimes that cost us money, but that are the right thing to do for the customers.

Doing a lot with a little

Nelissen also explained, which may seem obvious, but is often a sore point when it comes to customer experience, that customers have no idea how complicated an organisation is. He said that customers don’t know that RBS has lots of different systems and that “the data is rubbish”. They think that they have a relationship with one, unified entity.

This forced Nelissen to collate all the business’ decision making and put it one place. He said:

What we have done is been really focused and been really careful so that we bring all that data in one place, we have a single decision layer that sits on top and pushes decisions out to all the channels we have. The customer gets the best of what we have. It’s always on, we don’t do campaigns, because it’s about getting the message to the customer when it’s right for them, not when it’s right for us.

moderncustomerOne of the biggest challenges for the data team at RBS has been convincing the business that a bank, with all its legacy, can be as incisive with its data use and decision making as some of the biggest internet giants. It’s a cultural challenge, requiring people to get to a place where they understand that the ‘what’s best for the customer’ approach is a good long term investment – because the gains may not be immediately seen.

However, Nelissen was keen to highlight that you don’t have to go all in from the beginning and you don’t need a huge investment to make it work. He said:

You can do a lot with a little. We got close to our customers, our stakeholders, tried to figure out what the problems were and then we found the best possible solution. It didn’t need to be perfect, it just needed to be better than the thing we had before. And the business likes that we are trying to help them, so when that’s a little better, it becomes easier to do the next thing.

We are now talking about serious amounts of investment with business support – I don’t think if someone had given us the money at the beginning, that that would have been the right thing to do, because we learnt a lot along the way.

When asked about the ROI, Nelissen said that the bank “wouldn’t make that scale of investment if it

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didn’t have the return”. He added that one of the things other businesses can do to make sure that they get the buy-in from the business, is to make sure that your data function, your data team, is closely coupled with the business and its needs.

One of the first things we did was get the analysts closely aligned to the business. Because what we had were people that were very smart, doing really interesting stuff, but only things that amused themselves and didn’t help the business at all. They were completely disconnected from any sort of value drivers, so getting them aligned to the business, working with them, makes a difference. That’s where the value is.