The customer experience, now more than ever, is the bar we use to predict the health and growth potential of a business. Most major financial institutions are taking this to heart by adapting their services to deliver the “delightful” customer experience we’ve come to expect as consumers (think Amazon, Netflix, and Uber.) I was reminded of the sea change that is moving our industry towards intelligent customer management while at the Chief Data Analytics Officers (CDAO) event in Boston last month. I contributed to a panel discussion about the emergence of machine learning in financial services, where I was joined by industry peers with first-hand experience transforming their business with data-driven insights. The efforts of fellow panelists and thought leaders, like José Murillo of Banorte, were on full display. Our lively exchange made clear that the disruptive forces of Artificial Intelligence (AI) and Machine Learning are here to stay.
AI and Machine Learning have long been terms owned by academics and sci-fi. But the very real benefits they produce are changing the conversation with the very real applications they enable for everyday business operations. Advances such as chatbots enable a human-like conversational experience to provide expert-level insights for your customers, bankers, and advisors. Robotic process automation increases productivity to free up your sales team to offer the personalized experience customers have come to expect. These operational advances will improve efficiency and effectiveness by providing a memorable service personalized for a “market of one”, all powered by the insights gleaned from your very own customers.
If your firm is looking to unlock the insights in your data, you need to invest in the potential of these technologies, whether that’s through the structure of your data, or putting the systems in place to ensure organization-wide success. We’ve been in customer management for over twenty-five years and know first-hand that these technologies can bolster your efforts by:
These advantages can be achieved today, with clear objectives and measurable business value. I’ve been fortunate enough to partner with industry leaders to help bring these modern, transformative capabilities to market, seeing firsthand the evolution of today’s personalized customer experience. How much further can we can raise the bar to provide the perfectly customized customer experience?
I was excited to see the energy and passion shared by my fellow panelists as we considered the disruptor that AI represents for our industry. But there is lots of work left to do! CDAO 2018 opened my eyes to the challenge of unlocking the potential inside our enterprise data assets. Change is coming, and it’s exciting to be on the leading edge of maximizing service with data-driven solutions.
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.
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.
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.
As consumers, we use AI platforms every day. Google uses AI to auto-complete your searches. Amazon builds personalized product recommendations based on the things you’ve bought in the past. Did you enjoy House of Cards on Netflix? Give it a 4 star rating and AI will suggest similar content that you’re bound to enjoy.
These platforms work because we supply them with the fuel they need to learn and adapt to our changing preferences and habits. A massive volume of data is created each year, and it’s growing in a hurry as we do more online. This is true for businesses too, as they turn to digital channels to transact and operate. It’s an area that’s experiencing explosive growth, and as technologies improve, we’re developing innovative ideas to apply to AI to grow business in compelling new ways.
So, what does this mean for banks? Well, let’s assume that you are looking for new ways to apply AI to your business; in fact, you know this is essential to your competitive advantage. But historically, this has been expensive, and it’s been difficult for you to deliver on that analytics initiative when you can’t make sense of the data assets that you have access to.
Fortunately, advancements in the key technologies underlying AI are rapidly changing the economics of your digital initiatives. Now, we have banking solutions being built on four AI technologies that drive insight discovery, outcome predictions, and task automation. They are: Big Data, Machine Learning, Natural Language Processing, and Predictive Analytics.
Big Data is the foundation of your AI solution. Big Data provides the raw building materials –structured or unstructured information – that are needed to identify patterns, surface insights, and make predictions. Your Big Data is found in the disconnected CRM solutions, core banking, transactional, third party, and social data streams that your firm maintains or has access to. Chances are that you’ve already implemented a Big Data Solution or Data Lake on a platform like Apache Hadoop or Amazon S3.
Next, we have Machine Learning, which involves training computers to achieve a result by describing the desired outcome, and then feeding it the data needed to produce that result. Machine Learning is being used to enable cognitive services that enhance customer management at the point of service. Platforms you might be familiar with include IBM Watson, Google Cloud, and Microsoft Azure.
Third, we have Natural Language Processing, which is used to find patterns within large volumes of unstructured data, such as the diary entries and call reports about client contacts in your customer set. An example of applied Natural Language Processing is to perform sentiment analysis against social media posts, or on the notes kept in your CRM systems. This will help you understand how a customer feels about a particular brand or product before you visit or call them.
Finally, Predictive Analytics will forecast unknown future events based on patterns that are identified in your data. Predictive Analytics will surface your next best offer or action based on the client insights that your AI platform has enabled. This is powerful stuff!
With this in mind, we’ll look at how banks are using AI to drive digital transformation and grow revenue by optimizing the customer journey across channels. Stay tuned for the next post: “AI & Your Competitive Edge in a Changing Marketplace.”
Today’s leading businesses have embraced technology to innovate and find an edge in our connected marketplace. It’s the Age of the Customer, and your customers have more options than ever before to access the financial services they need. How can banks respond to fluid market conditions, a complex regulatory environment and growing customer expectations, all while meeting growth targets and increasing share of wallet? Can we do this while improving customer loyalty at the same time?
The answers lie in your existing data assets. Customer data, locked up in multiple CRM, banking and third party systems, and augmented by social streams, provide you with the opportunity to know your customer better. The advent of Artificial Intelligence is opening new frontiers of customer insight, enabling firms to differentiate on service across digital and traditional channels. Artificial Intelligence will optimize the customer journey at the point of service, helping you grow revenue across regions and product lines.
Join me on August 15th and 16th to explore how leading firms are applying Artificial Intelligence to increase share of wallet in today’s ultra-competitive marketplace. I look forward to connecting with you!
You’ve seen the demo and read the brochure. You’ve read a dozen RFP responses, and expertly managed conflicting requirements and competing stakeholder expectations. You’ve short-listed the vendors, and authored a business case that you can be proud of. Project success is all but assured, if only the Executive Steering Committee would approve your budget. If you can relate to this, then you’ve led an enterprise software selection process.
Enterprise software selection is complex and rewarding. In my career, I’ve been fortunate to experience this from both sides of the equation – as the bank’s IT delivery manager on one hand, and as the Solution Provider on the other. Along the way, I’ve learned how important a well-executed selection process is to selecting the right vendor. I’ve also learned that no one step is more critical to your evaluation than a well-run Proof of Concept.
The Proof of Concept provides you with the best opportunity to test vendor claims. It allows you to validate your assumptions and conclusions from the demos, the brochures and the meetings you’ve had with the most senior, most experienced team members that the vendor has to offer. At the conclusion of your Proof of Concept, you’ll have answered three critical questions that an RFP can’t address:
What can the product actually do? Test your short-listed vendors against two or three high-value use cases to find out how configurable, extensible and ideally suited for the enterprise the products are. As a bonus, well-crafted use cases will help you clarify your own business value proposition, allowing the opportunity to pivot or adjust prior to submitting your business case for approval.
What will the users think? The importance of early end-user engagement cannot be overstated. Solicit their feedback during the Proof of Concept to generate excitement and build advocacy for your software implementation project. This will pay dividends down the road when you need it most, by building support for the change management process and driving early user adoption.
Who is the vendor that I’m considering doing business with? You are entering into a multi-year relationship with your chosen vendor, so you’ll want to understand who they are as well as you understand the product they sell. The Proof of Concept will bring you into close contact with the vendor and allow you to assess their working style, problem solving skills, technical and financial services domain expertise. You’ll determine whether or not they are a cultural fit for your organization, and if they’ll engage with you as your strategic partner. These characteristics may be the most important predictors of future project success.
The Proof of Concept is an investment; it takes time and energy, but the payoff beyond the brochure is that you’ll be a well-informed buyer, optimally set up for project success.
I’ll explore how to prepare for and execute your Proof of Concept on my next post.
Next week, I’ll be flying to London, England, for the Customer Experience Exchange for Financial Services. The Exchange is a meeting of senior executives responsible for the design, development, and delivery of customer service strategies and solutions. Bringing together a range of exclusive experts from the Financial Services industry, this conference provides valuable information on current and future trends in customer experience.
This year, the conference is focused on the customer of the future and defining customer experience strategies. This includes changing regulations and compliance, and the uncertain post-Brexit situation, which presents both interesting opportunities and unique challenges. Additionally, this year’s conference covers mapping out the ideal end-to-end journey and knowing what your customer is experiencing in real-time. We’ll discuss how to think outside the box, the omnichannel experience, and strategies of the “new normal.”
I’m particularly excited for one of the keynote presentations, “A New Partnership in Customer Experience,” by Michael Donald of MBNA/Bank of America and Trevor Pereira of INTU Properties PLC. This presentation is part of the “Creating Millennial Moments” portion of the conference. I’m looking forward to seeing their take on how the expectations of millennials are shaping the future of customer experience.
Also, in the “Personalising an Anonymous World” portion of the conference, I’m excited for the “Making Customer Data Go Further For Your Business” presentation by Julia Sutton of HSBC. This presentation will discuss how data at HSBC is becoming digital, digital identities, and how HSBC is driving a change in the way customer data is being used to make interactions easier. At NexJ, the management and maintenance of data is a key priority. Ensuring that users have complete access to all available data is a key component of the NexJ strategy, and I look forward to learning from HSBC’s experiences.
To anyone else heading to the Customer Experience Exchange next week, I’ll see you there!