In our last post, we looked at the Artificial Intelligence (AI) platforms that banks are using to drive their digital initiatives. Now, we come to the central question that banks are faced with: “We have a great brand. How do we grow revenue? And can we increase customer loyalty while doing it?”. Hopefully, you’re beginning to form a picture of how you might use AI to solve these business problems.
As you know, today’s marketplace for Financial Services is crowded and ultra-competitive. In the Age of the Customer, your buyer has an unprecedented level of access to information. Social networks, communities, and websites have armed your customers with information about your products long before they engage you in the sales process. Also, innovative technologies have introduced new players to the market in the form of FinTechs, who are able to compete head-to-head with traditional suppliers of financial services for market share. Consider these trends against the backdrop of a highly regulated industry that features market-driven pricing, and we reach a startling conclusion; your products and services have become commoditized. So, how do you leverage your brand to differentiate and grow revenue? The answer’s clear: you need to differentiate on service by anticipating customer needs to deliver a tailored customer journey, enabled by AI.
Today, banks like yours are doing exactly this, by implementing a digital strategy built around the enterprise customer in response to the changing and connected marketplace. At the heart of this strategy is the wealth of customer data that you can use to deliver augmented intelligence and analytics at the point of service. This data is your greatest untapped business asset.
Blending Data Assets to Unleash the Potential of AI
But our customers tell us that this data is siloed across systems. And it’s growing explosively, as external, fast-moving data sources are added to the catalogue of systems that you depend on to sell and service your customer. Other systems in this mix include the multiple, disconnected CRMs and transactional systems that are in use at your firm. To make the most of this data, you’ll need to provide an Enterprise Customer View (ECV); a single, harmonized understanding of the customer across regions, product lines, and channels. With an ECV, you’ll be able to implement enterprise-wide initiatives without the potential of working from conflicting copies of data, that will enable the insight discovery, predictive analytics, and automation needed to differentiate on service.
So, the key to growing revenue and increasing customer loyalty is to apply AI to an Enterprise Customer View of the client. Let’s look at three cases where AI has been applied to improve customer service, identify new opportunities, and collaborate globally.
Use AI to Improve Customer Experience & Revenue Outcomes
In the global banking context, there are many inputs that we can leverage. We’ll call these signals. Signals might include the analytics applied to market data about trade corridors, or the share of trade between two entities. Signals are available from your existing customer data, such as the balance sheet, which you can use to proactively identify acquisition targets at the right time in your customer’s lifecycle. And you can surface signals from past interactions and purchases that are stored in your CRM.
Using AI, you can overlay each of these signals to produce triggers that drive suggested next actions and proactive engagement. In the simplest of terms, triggers are an AI response to a set of signals, such as current market conditions and past buying behaviours. Triggers can be sent to your bankers, sales, and trading team members, providing them with real-time insights that are surfaced through their existing CRM or other sales systems. This will augment banker intelligence at the point of service by suggesting next best actions, vectoring clients into a segmentation strategy that’s proven to retain business, and ultimately increase client “stickiness” and loyalty.
Signals can also be used to identify new sales opportunities and automate account planning activities. The ECV and AI can be used to feed client process management and enterprise workflow platforms. This will help you automate the activities currently performed by your bankers, and optimize data-intensive processes to automate account planning and identify new opportunities. These solutions will provide you with just-in-time service delivery that’s tailored to your customer’s specific needs and preferences. And you’ll be able to bring product to market faster and shorten the time to revenue!
AI and your ECV will dramatically improve collaboration across lines of business, channels, and regions. For the first time, you’ll be able to service the Global Customer in a coherent way, benefiting from predictive analytics and cognitive services at each level of the customer hierarchy. Would you like to trigger an FX deal in Singapore off the back of a trade finance deal for a mining customer in London? Or drive your prospecting and sales activities by tagging counterparties that are not banked customers? We call this augmented intelligence at the point of service, and it’s the key to tailoring a customer journey that drives revenue and loyalty. AI and the ECV will do this for you.
You’ve seen how AI and the ECV will deliver a cohesive, intelligent experience every time your customer interacts with the bank. We believe that a well-constructed Intelligence-Driven Customer Management strategy will produce measurable results. When you build a strategy around your customer, and apply the right expertise in the right context by augmenting intelligence at the point of service, you’ll become their Banker of Choice. We’re confident that this will lead to improved customer loyalty, and you’ll win more business and increase share of wallet.
How have you used AI to transform your customer management strategy? I look forward to your comments.