We are living in a 24/7 always on, internet-based, mobile-accessible consumer environment. With more and more commerce taking place online and on demand, banks acknowledge that shifting customer expectations are forcing them to rethink their engagement strategies – and they’re looking toward the benefits that AI-based technologies might provide.
A recent paper by Finextra and Pega, “How AI Refocuses Your Business on the Customer,” explains how financial firms recognize the need to build relationships through more meaningful customer conversations, plus discusses customer relationship challenges in the banking industry, and the role AI can play in improving engagement.
Banks recognize that today’s connected consumers expect on-demand, personalized engagement that speaks to their unique needs.
But this level of engagement is not easy to deliver. Banks need to be able to analyze a customer’s complete history and then make decisions on the best action to take based, not only on that history, but also the context of the current customer interaction, the bank’s goals, as well as any identified risks. All within a split second. And, if desired, on a 24/7 basis.
AI technologies can play a central role in advancing financial sales and service interactions to improve engagement goals. They can help organizations listen to and analyze information, make decisions about the best way to respond or interact, and connect with customers through multiple channels and devices.
For example, a 2017 Forrester “TechRadar™ for AI Technologies” identifies decision management as the most mature and established AI technology, and important for enterprise application development and delivery. Organizations that utilize an always-on digital customer brain for decision management can leverage predictive analytics to sense customer needs and guide customer offers and outreach in real time regardless of channel – and even in instances where customers may begin a conversation in one channel (e.g., email) and end in another (e.g., call center).The upside for financial services firms is significant. A Forbes study on customer engagement found that companies who execute customer engagement very well experienced 4x top and bottom line growth, and 3x increase in customer acquisition.
Predictive analytics, decisioning, and AI are key to putting customers squarely at the center of your banking initiatives.
Forrester predicts that by 2021, “insights-driven” businesses – businesses that systematically harness insights across their organization and use them to create a competitive advantage – are on track to earn a collective $1.8 trillion over traditional businesses. AI-based analytics are part of this advantage.
In the banking industry, predictive models and analytics from customer data can inform decisioning processes, weighing the recommended best actions with regard to sales, service, risk, operational decisions, and customer lifecycle – and they can help banks discover meaningful patterns and engagement opportunities by anticipating customers’ needs.
For example, technologies like Natural Language Processing (NLP) are useful in detecting patterns and anticipating customer needs. As part of an augmented CRM and decisioning system, NLP can capture content from customer interactions (e.g., emails, texts, social media posts), then the resulting data can be leveraged by decisioning and machine learning tools to recommend actions that will provide relevant and consistent customer-centered engagement.
Similarly, AI-based decisioning tools help banks move to a more personalized, one-to-one, outcome-focused approach. The advantage of using automated decisioning and machine learning is the ability to analyze hundreds of individual customer data points in a fraction of a second to determine the optimal approach or action for the customer at that particular moment. A single, omni-channel decisioning authority can underpin the delivery of personalized customer engagement.
Organizations are realizing the advantages of using AI-based tech to focus on customer outcomes.
In a recent review of large U.S. banks, conversational interfaces like intelligent virtual assistants and AI-powered chatbots garnered the most interest in adoption, along with machine learning, decisioning, and process improvements through robotic process automation (RPA). Similarly, a 2017 Economist Intelligence Unit study of more than 200 business executives found that 75% said AI would be actively implemented in their companies within the next three years.
Banks are seeking out AI-based technologies that can help support seamless, personalized customer interactions – and with good reason. The companies that have already implemented AI-based technologies are realizing amazing returns on investment.
For example, the Royal Bank of Scotland Group uses AI-based decisioning to provide one-to-one, personalized conversations for more than 17 million customers. By refocusing efforts on real-time, context-based conversations, RBS improved mortgage retention by 20%, and web-to-branch transition from 0% to 40%.
Commonwealth Bank of Australia (CBA) has seen a 10x increase in lead volume and a 3x increase in lead conversion by using AI-based decisioning to drive conversations in real time, when customers are engaged and ready to listen.
These are two examples of how banks integrated AI-based tools with their existing systems to improve customer engagement. By augmenting a unified CRM platform with AI and machine learning capabilities, banks can quickly automate and enhance personalized service and sales to make relevant recommendations, improve customer engagement, and improve outcomes – efficiently and profitably.
ABOUT THE AUTHOR: With more than three decades of Financial Industry experience, and in his role as Senior Director and Industry Principal for Pega’s Financial Services Sales and Marketing solutions, Scott Andrick helps clients around the world strengthen customer relationships and accelerate digital transformation.