As banks, credit unions, and financial services organizations look for ways to better serve their customers, many are seeking new and innovative ways to better understand their customers’ wants, needs, and behaviors. Increasingly, this means the use of digital tools and digital transformation initiatives, as well as the use of customer and predictive analytics, artificial intelligence (AI), and machine learning, to better understand their prospects’ and customers’ needs in order to provide an outstanding customer experience.
For some, the initial exposure to AI has been with such consumer products as Apple’s Siri and Amazon’s personal assistant, Alexa. For others, this journey began with exposure to progressive retailers who have been leaders in defining and executing on key elements of omni-channel retailing and omni-commerce. These firms, which include such names as Apple, Best Buy, and Nordstrom, and others, are relentless in their efforts to better understand their customers, and have been among the leaders in developing customer-centric systems.
Analytics and digital transformation efforts are often at the center of discussions about increasing customer interaction and engagement. Organizations are finding that such transformation is often less about changes in technology, and more about anticipating changes in customer expectations and responding with enhanced business models and differentiated customer service.
Understanding and anticipating customer expectations is more difficult than ever, with some organizations having to store massive amounts of big data, sometimes as much as dozens or hundreds of terabytes of both structured and unstructured data. This data often resides in databases and data warehouses throughout these organizations, sometimes in siloed, disparate systems. Analyzing this data often requires the use of AI, machine learning, and predictive analytics techniques to sift through it all to identify trends and predict customer wants, needs, and behaviors. These tools are becoming increasingly necessary, considering McKinsey estimates that the volume of all data continues to double every three years as information from digital platforms, wireless sensors, and mobile devices are shared across systems.1
Many organizations are finding that in order to deliver on customers’ heightened expectations, faster and more accurate ways to predict customer wants and behaviors are critical to successfully interact and engage with both prospects and customers. Analytics, AI, and machine learning can be instrumental in meeting these goals. These solutions can offer timely and relevant alerts and next-best-action suggestions, which can augment customer outreach efforts and improve the overall customer experience across industries.
The use of AI and machine learning is increasing, with AI being a key component of machine learning solutions, including the use of chatbots and similar tools in call and contact centers. The algorithms used in these solutions are programmed to learn in various ways based on the data they are exposed to, and interact with. And, as more data is processed and more insights learned from the data, the machine learning process becomes more intelligent and adept at discovering patterns, enabling improved prediction capabilities.
Some of the more visible examples of using AI and machine learning can be found in retail banking, wealth management, credit card, and insurance lines of business. Chatbot technology in retail banking for call and contact centers is on the rise, as is the use of robo advisers in financial institutions’ wealth management lines of business, typically in the mass affluent market segment.
An example of such uses of chatbots includes DBS Bank in Singapore, which began using chatbot technology within its Digibank digital bank in 2016, and it’s now estimated that over 80% of customer queries are handled in this manner.2 At DBS, and other institutions, the chatbot uses “conversational AI” to communicate with customers via voice and text, with customers enjoying a faster experience, and often not realizing that they are talking to a bot, and not a person.
The use of robo advisers is on the rise as well. Robo advisers can provide automated savings and investment advice based on individuals’ unique goals and financial situation, with rules-based algorithms and machine learning suggesting the most appropriate next-best-action. The resulting recommendations can be less costly than human-based advice, and results are based on industry best practices and tailored to meet the specific needs of customers.
In some cases, machine learning and personal interaction are being used together as part of a hybrid model to better serve customers. An example can be seen at Morgan Stanley, which offers an enhanced human advising process. This process includes machine learning to match investment options with client preferences and risk tolerances, which informs financial advisors about investment possibilities that can then be discussed with clients.
Benefits from AI and machine learning can be seen in the retail and manufacturing sectors as well. For example, McKinsey found that over the past five years, U.S. retailer supply chain operations who have adopted data and analytics solutions have seen up to a 19% increase in operating margins.3
While there has been much value realized from data management and analytics to-date, there are ample opportunities for improvement. The estimated potential value captured from the use of data and analytics has been uneven, with the retail industry capturing approximately 30-40% of potential value from such systems, and manufacturing only capturing about 20-30% of potential value, per McKinsey.
PwC estimates that almost half of all manufacturing activities might be automated through robotic process automation (RBA), which could translate into a $2 trillion reduction in global workforce costs.4 However, RBA is not used solely in manufacturing, as it is already used to resolve credit card disputes, process insurance claims, and reconcile financial statements, to name just a few tasks where AI and machine learning are being used.
Looking ahead, there are tremendous opportunities to increase the value derived from analytics, AI, and machine learning solutions. And the benefits of such systems can be realized in almost every line of business, with the potential for improvements in customer satisfaction and the delivery of an outstanding customer experience being paramount. Companies that ignore the potential of these capabilities do so at their own peril.
3 http://www.the-future-of-commerce.com/2017/06/20/machine-learning-in-retail/; http://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/the-age-of-analytics-competing-in-a-data-driven-world
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