How ANZ uses data analytics to drive strategic next-best customer conversations

The banking group's customer insights strategy and design lead shares how its efforts to build a customer analytics engine is helping drive relevant and personalised conversations

Using data-driven customer insights to proactively engage with and lift the value of every customer interaction is the cornerstone of ANZ Bank’s ‘Strategic next best conversation’ program.

Speaking at the recent IBM Customer Engagement Forum, ANZ Bank strategy and design lead, customer understanding and insight, Sam Kline, detailed how the banking group has spent the last two years investing in data and analytics capabilities to drive better customer engagement. This is from a technology as well as people and skills point of view, he said.

Kline noted ANZ today has 6 million active retail customers, two-thirds of which it actively markets to. Across its customer base, more than 2 million people log in via digital devices today, he said. This is on top of the thousands of interactions in branches and via contact centres.

“These are all huge opportunities to get it wrong with the customer as well as right,” Kline said.

ANZ’s ‘Strategic next best conversation’ program is about tapping employing a raft of analytics techniques and technologies to a wide range of data in order to have the right conversations with customers at the right time. Kline identified four main capabilities underpinning its efforts: Listen, interpret, return, engage.

“It’s about having the right conversation, whether it’s a sales type, service or buyer conversation,” Kline said. “We want to enhance every interaction with the right thing to talk to the customer about, and empower bankers to have the right conversations as well as be relevant, timely and personalised.

“The other aspect to this is if we don’t compete, we risk becoming stale, slow, disintermediated and largely irrelevant.”

The first step to achieving this vision was how to transform data and into information, Kline said.

“A lot of the challenge at ANZ was that we hadn’t really understood the value of the data we had in our systems,” he said. “People using their credit cards and savings accounts is such rich information and from that we can accurately determining so much about a customer’s life –what they do in their lives, life stages, how spending patterns change, and behaviours that weren’t there before.”

To address this, ANZ has gone from monthly extracts into core systems to daily and near real-time data-driven insights, Kline explained.

“It’s getting timely data but importantly, getting data to a point where people can use it and use it in the right way,” he said. “Achieving that comes back to people, and giving them the tools, skills and expertise as well as access to data so they can bring it all together and genuinely understand the touchpoints customers have with us every day.”

ANZ also needed to invest time, money and effort into foundational information management tools, while leveraging sophisticated analytical capabilities and emerging technologies, Kline said.

“We spent a lot of time building that information management infrastructure, which we didn’t want to do, as we just wanted to get more of the threads through the value chain, but we had to,” he said. “It had to be done at a foundational level, because that then starts to drive volume.”

This required significant investment, and Kline said it was vital for teams to prove the value as they went along.

“It was a big program to get it right, so we looked for opportunities where we could get at the data in a timely fashion, get the right insight, and get it into the hands of the bankers and start to learn and get that feedback,” Kline continued. “That has been powerful, as these staff become evangelists for the program we’re building.”

The next part of the puzzle was identifying what to talk to customers about.

“We are coming from a product-centric, siloed approach, to really understanding customers, what’s happening in their lives, and therefore having relevant conversations with them,” Kline said. “Getting the segment, marketing and product teams all aligned on that is part of our ongoing challenge, but we’re making good ground on that.”

Ensuring all of this information is put into the hands of those staff having the conversation with the customer, or managing digital channels, was also vital to the program’s success, Kline said. He stressed the importance of taking an omni-channel approach to communication.

“It’s not just about sales either, but about service and customer experience. It’s capturing the data in that value chain, so we can track it and have that closed loop learning and feedback,” Kline said.

What it means for customer engagement

As an example of how ANZ is putting this into practice, Kline pointed to situations when customers travel. He estimated travel activities to be worth about $2.5 billion.

“Through data and analytics we can sense and respond from a banking context about how and when we should play in that space,” he said.

ANZ is diving into what happens around before, when and after people travel, in order to identify trends and patterns that can be used to then fuel communication strategies. This could be in the period from when customers purchase an airline ticket to when they travel, or what they do when they travel or return from a trip.

Overlaid with that transactional insight is data on the type of products they have, as well as behavioural and socioeconomic insights used to build a profile of different customer segments.

For instance, a customer using their ANZ credit card for the first time on a trip to Singapore could be sent a mobile message reminding them of the opportunities to use the card within the airport, then a notification in Singapore informing them that ANZ will waive the transaction fee on their first 10 transactions abroad.

“It’s a good opportunity to help the customer reiterate features in their products, or just have that dialogue helping them prepare for when they do actually travel,” Kline said. “Importantly, it also tells us what we should not be talking to them about when they’re travelling.

“There is so much more you can do with the data once you starting putting this kind of lens over it.”

Key learnings

Kline had five pieces of advice for others embarking on similar customer-led data analytics initiatives:

  • Have strong sponsorship: “Our managing director is passionate and proud of what we’re doing, and we’ve been able to have that kind of dialogue with the management board and many under that, including people at the coal-face,” he said. This requires defining a clear vision and plan for integrating: “This is a huge beast, so how do you start, what do you do, and what are the right things to test and learn are all big questions,” Kline said. “You need to have that vision of where you’re going. Take people on that journey – there are significantly new skills, thinking and expertise required to do this.”
  • Prove value along the way: Kline said it’s important to pick things from data in a timely manner and on a regular basis to prove the value of the work being done. “Have a few customer conversations and see what happens,” he said. In one ANZ example, branch managers used insights to call customers and further conversations. “What they learnt from that was that we have to learn to trust the leads and harness the power of all the data in our systems,” Kline said.
  • Get the big brass involved: “You are bringing complex concepts and architectures together,” Kline said. “Bring this to life with examples people can understand and that resonate with them.”
  • Combine analytic techniques: It’s also important to use a mix of propensity models, sophisticated techniques and basic data tools to achieve the best results, Kline said.
  • Focus on the people: Everyone, from the frontline to the teams in the back office, the analytics staff, and the people who know the systems and channels, must get on-board, Kline added.

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