Data Analytics ‘Performance Gap’ Destroys Customer Experiences in Banking

The mission of trying to build one-to-one communications and engagement is not a new concept. In 1993, Don Peppers and Martha Rogers, Ph.D., proposed that organizations could use technology to gather information about, and to communicate directly with, individuals to form a personal bond. The book, “The One to One Future: Building Relationships One Customer at a Time” stated that technology had made it possible and affordable to track individual consumers, to understand each person’s individual journey and to provide contextual offers at the optimal time of need.

Six years later, internationally recognized best selling author Seth Godin published the book, Permission Marketing, built a logical case for creating incentives for consumers to accept advertising voluntarily. He showed how reaching out only to those individuals who have signaled an interest in learning more about a product would enable companies to develop long-term relationships, create trust, build brand awareness — and greatly improve the chances of making a sale.

In other words, both books, from over 20 years ago illustrated the logic of moving beyond mass marketing communication, using data, analytics and advanced marketing technology tp build trusting relationships with a foundation of timely engagement and offers.

Just like two decades ago, the objective of effective marketing in banking (and any industry) is to improve the customer experience by using real-time insight to understand and improve every interaction a consumer has with an organization. Going beyond individual transactions – viewing the entire customer journey in context – allows organizations to understand the cumulative impact of customer engagement over time.

Research by the Digital Banking Report Intelligence Unit found that there is a significant ‘performance gap’ when we view the stated importance of using advanced analytics in financial marketing and the actual use of AI in financial marketing. In fact, the level of stated importance of using AI for targeting, proactive advice, the delivery of communication across channels, for delivering contextual offers and for marketing planning were all either extremely or very important for more than 80% of the organizations we surveyed.

Unfortunately, the effectiveness of using AI in financial marketing did not reflectthe level of importance stated. Overall, virtually no organizations believed their application of advance analytics was extremely effective. The vast majority of organizations believed their application across all of the desired uses was either ‘somewhat effective’ or ‘not very effective’. This is definitely a red flag at a time when consumers are expecting more.

Despite the potential for AI and machine learning, there are a number of obstacles to successful utilization of these advanced technologies in marketing. Beyond, budgets, data integration, analytics and other internal issues, probably the most significant obstacle over the next 5 years will be in skills required.

The skills needed for successful use of AI and machine learning are in high demand and short supply … especially in the banking industry. From skills related to AI, to advanced marketing and data privacy, financial services organizations will be challenged to find skilled people internally and will be forced to enlist the support of partner organizations that are built around advanced technologies

The beauty of this concept, compared to 20 years ago, is that the capabilities to deliver this level of engagement is not just for the largest organizations any more. In fact, access to highly detailed consumer data, and advanced decisioning engines that can deliver actionable insights ‘at scale’, is both available and affordable to financial institutions of any size.