There’s so much choice available that customers can pick and choose who they buy from and where, when, and how it happens. They want to discover, research, evaluate, and purchase on their preferred channel. Give them that option, and they’re more likely to choose you. That’s the whole point behind the multi-channel approach.
Fostering a data-driven culture that can successfully inform an organisation’s strategy requires an ability to relate insights back to revenue, business and customer value.
Speaking on a panel at the inaugural Chief Analytics Office Forum in Sydney, ANZ New Zealand head of information and insight, Tina MacLean, said one of the key ways she’s brought executives to the data side is to talk about how analytics supports revenue opportunities.
“Analytics helps identify customer opportunities that can be turned into revenue, and that was the conversation we had to have at an executive level,” she said. “People had talked about the fact that data was untidy, or that the quality wasn’t there… But the CEO isn’t interested in where data is scattered, he gets the data he wants.
“What we had to do is say what does that means in terms of how much revenue we could generate from the data before us.”
Up until about three years ago, ANZ New Zealand didn’t have much of an analytics capability, MacLean explained, and was producing mainly lists for marketing. There was less focus on targeting customers, and more emphasis on excluding customers the group didn’t want to talk to.
The merger of ANZ New Zealand and National Bank and the need to bring together two brands and two sets of customers provided an opportunity to launch analytical thinking in the business, MacLean said.
“When we integrated the systems… we used our data to firstly inform the strategy around how to manage customers, and who was more at risk of defection during that time, who was of highest value and also then to inform a direct, detailed customer communication program,” she said.
“That was a massive change for us. We used our data well at the time, with little technology apart from SAS and lots of Excel spreadsheets. We were able to deploy that data and insights to our frontline bankers, which spring-boarded the conversation around analytics [investment].
“We came out of merger without losing hundreds of thousands of customers, so after that we had a fresh set of dollars to invest in the bank and in new capability to use our data better.”
The focus on analytics activity at ANZ New Zealand has since shifted onto driving customer satisfaction and how that turns into a revenue opportunity, MacLean said.
While the Commonwealth Bank has always been data analytics driven, lead data scientist, Michael Bewley, said bringing new technologies into its analytics capability has been a priority for his team in recent years. One challenge he identified with rising interest in data was the thousands of dashboards that have ended up proliferating across the organisation, making it difficult for teams to know which actions to take off the back of those insights.
“It’s not so much trying to convince people data is important, but the way in which you use it to drive action that has been the change in conversations internally,” he said. “My role has been to figure out how to make best use of all new stuff happening out there, integrate with mature existing capability and leverage strengths.”
Another challenge for Bewley was overcoming existing expectations around well-established processes. “When you have to change those and focus on different areas, it can take a while,” he commented.
For MacLean, getting buy-in from frontline teams into using data analytics for action won’t happen unless they see value in why information is important to them.
“Those teams don’t care if it’s important to you or not, it’s about their perception,” she said. “We have provided lots of proactive leads to frontline, and what we talk about is why that lead doesn’t replace them, it’s about how they use the information put in front of them to drive a conversation.
“If the information isn’t correct, that probably means the information in the system isn’t right and it’s those staff who are putting that in the system. We try and encourage a feedback loop so we’re constantly learning and improving those leads and data from frontline teams.”
As an example, with voice of the customer and feedback on services, the analytics team is working to help bankers understand that it’s not about their score per se, but the experience and changing the opportunity conversation with the customer, MacLean said.
“It’s about taking that feedback on-board rather than the fact that you’ve just dropped by a percentage point,” she advised. “None of these things are easy and it’s always a journey, it’s trying to find the right conversations that take that forward.”
Bewley also works hard to provide stories on how leads help improve bankers to have better conversations with customers.
“We phrase it as an opportunity –something you might not know about that customer so you can have a better conversation,” he said.“I’m lucky that I have end-to-end analytics capability in mind team, and we have data engineers/advanced analytics and business reporting team as one analytical unit.
“We try and make sure the connection within the teams – what’s the customer opportunity at be predictive end through to reporting on that, building process and engagement across the team that enables an end to end view.”
Read more of our coverage on building a data analytics capability and culture:
- How AMEX is using data and creative to tap into customer contexts
- How Tesco's loyalty card transformed customer data tracking
- How IAG uses first-party data to drive customer engagement
- Three killer strategies for data-driven audience targeting
- How a CMO, CIO and CFO data alliance is helping Tatts Group transform its digital marketing game
- Why data-driven marketers are increasingly turning to first-party customer intelligence