Financial services organisations often have vast volumes of data, but turning this into actionable insights isn’t always easy. Thanks to his background in analytics, Damien Judge is helping Ulster Bank realise the full potential of its customer data with AI. As Head of Business Commercial Excellence at Ulster Bank, Damien isn’t just pioneering AI; he’s also blazing a customer service trail. We found out how working in his family business from a young age has shaped his attitude to customer service in the finance sector.
My father is a local businessman, and I’ve helped him run various companies since I was a teenager. I’ve worked on building sites, I’ve washed buses, I’ve answered customer calls, I’ve done book keeping and handled legal, banking and financial concerns – in fact, I still help out from time to time in between looking after my two young children and managing a local football team!
My SME background means I know what our commercial customers want from a bank, and I apply that knowledge to everything I do – even AI! When I was working in the family business, I found it really frustrating that I had to have the same conversations over and over again with different people at a competitor bank. Businesses want to work with financial services companies that understand – and remember – their challenges and goals.
I want to leverage AI to take customer service to the next level. By combining our analytics capabilities with Salesforce Einstein for machine learning, my team has built a Next Best Product (NBP) engine that helps relationship managers understand what products a commercial customer may need.
The potential for AI both in our professional and personal lives is amazing. People often don't realise they are using AI when they search on Google or complete a transaction on their smartphone. And it’s the same at the bank – our relationship managers use the engine but don’t often appreciate that it’s AI powering the recommendations.
We trained the engine’s underlying AI model using historical data on our existing commercial customers and their product holdings. This helps us better predict their needs and how we can fulfill them. For example, when we interact with commercial customers, we use the engine to understand the likelihood of the customer needing certain products and the potential of converting this opportunity. Our relationship managers can then apply their personal customer knowledge to decline or accept the recommendations from the engine.
We’ve made Trailhead mandatory for anyone using the Salesforce platform. Although CRM is a well-established discipline, a lot of people don’t understand its full potential. We want people to see CRM as integral to everything that we do, not as a separate function – Trailhead helps them see the bigger picture.
I've also built strong relationships with strategic players and other customers in the Salesforce community, which will help me to keep blazing a trail for the bank.
I’ve already been amazed by what we’ve achieved in such a short time. But that’s only spurred me on to do more! I really want to delve deeper into AI – I can see great potential for automating manual mundane tasks and increasing efficiency at the bank. My team is currently working on a Next Best Action engine, which will evaluate whether a customer has the optimum products for their business and suggest alternatives where appropriate. This will improve the customer experience and help relationship managers better meet their needs.
We only started our Salesforce and AI journey in January 2017; being asked to present at Dreamforce ’17 as a Trailblazer was just mind-blowing. I feel really proud that our efforts have been recognised externally. The team focused on achieving some quick wins and now that we’ve gained momentum, we’re tackling the more complex stuff.
As a Trailblazer, I want to share knowledge with other companies on their AI journeys and keep innovating and pushing boundaries.
Start slow and safe – this is particularly key in the financial service sector where there’s a lot of governance. It’s also important to make sure that you have the right resources on board. We already had some talented data scientists at the bank, which we supplemented with experience from Salesforce.
To bring an AI vision to life, you will need a robust data structure and support at every level. We engaged stakeholders from across the business to verify the engine’s results and refine our machine learning model to ensure the outputs were correct. User acceptance testing ended up taking a lot longer than expected but it was worth it, as when the engine went live there were very few issues.
By thinking like a customer, Damien is helping Ulster Bank take advantage of richer insights and smarter processes to deliver a better business banking experience. Find out more about how Ulster Bank plans to be first for customer service, trust and advocacy by 2020.