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- On LLM Series.
- On Cloud Series.
- On Platformization of Banking Series.
- AI and Data: The Powerhouse for Banks
- Reimagining Recommendations: LLMs as a New Frontier (LLM Part 12)
- Uplift Bank Profits. AI to Maximize Customer Lifetime Value (Product Holding Ratio). (LLM Part 13)
- AI-Driven Customer Lifetime Orchestration for Banks (LLM Part 14) - This is co-authored with Quynh Chi Pham and Amelia Longhurst .
- Augmented Intelligence (LLM Part 15) in banking
- Data Mesh in Banking. Orchestrating CLV. (LLM Part 16)
- Conquering Data Mesh Challenges in Banking & Driving ROI. (LLM Part 17)
- Intelligent Banking - Beyond Automation to Augmentation (LLM Part 18)
On this article:
- The Shift from Automation to Augmentation
- Understanding the Customer: The Foundation of Augmentation
- Personalized Orchestration: Delivering the Right Offer at the Right Time
- Boosting Product Holding Ratio through Intelligent Recommendations
- Maximizing CLV through Personalized Engagement
- The Human-AI Partnership: Empowering Bankers and Marketers
- The Future of Augmentation in Banking
The Shift from Automation to Augmentation
The banking industry is in the midst of a profound transformation, driven by the rapid advancement of AI. While automation has long been a tool for improving efficiency, a new paradigm is emerging: augmentation. This shift represents a move beyond simply automating existing processes to truly enhancing human capabilities. This exploration of "Intelligent Banking - Beyond Automation to Augmentation" focuses on how AI-powered augmentation can unlock unprecedented levels of customer engagement and profitability, specifically by maximizing Customer Lifetime Value (CLV) and product holding ratio.
This is not a technical deep dive. Rather, it is written with bank executives in mind, those who are strategizing for the future of their institutions. We will steer clear of complex algorithms and code, instead focusing on the strategic implications and practical applications of AI augmentation. Our goal is to provide a clear and concise understanding of how this powerful technology can be leveraged to drive sustainable growth and create a truly customer-centric banking experience.
In the context of CLV and product holding ratio, augmentation refers to using AI to enhance human capabilities in understanding customer needs, personalizing interactions, and orchestrating offers and services to maximize customer engagement and profitability. It's about empowering bankers and marketing teams with intelligent insights and tools, not replacing them. Augmentation focuses on:
- Deep Customer Understanding: AI analyzes vast amounts of data to provide a holistic view of each customer, including their financial behaviors, preferences, and predicted future needs.
- Personalized Orchestration: AI enables the creation of highly personalized journeys and offers, delivered at the right time through the right channels.
- Proactive Engagement: AI can anticipate customer needs and proactively offer relevant products and services, increasing the likelihood of adoption.
- Continuous Optimization: AI continuously learns and adapts to customer behavior, refining strategies to maximize CLV and product holding ratio.
The following pages will explore these key aspects of augmentation, providing concrete examples of how banks can implement these strategies to achieve tangible business results. We will examine how AI can be used to understand customers better, personalize their experiences, boost product adoption, and foster long-term loyalty. Ultimately, this exploration aims to provide a roadmap for building the intelligent bank of the future, a bank that is not only efficient but also deeply attuned to the needs of its customers.
Understanding the Customer: The Foundation of Augmentation
Truly understanding the customer has become the cornerstone of competitive advantage in modern banking. However, achieving this understanding is increasingly complex. Banks possess a wealth of data – transaction histories, online and mobile banking activity, CRM interactions, call center logs, even social media sentiment – but this information often resides in disparate systems, creating data silos that hinder a unified customer view. Traditional customer segmentation, relying on static demographics or basic product holdings, falls short of capturing the dynamic nature of individual financial needs and preferences. In this intricate data ecosystem, extracting actionable insights and delivering truly personalized experiences demands a sophisticated approach, one enabled by AI-powered augmentation.
AI provides the tools necessary to synthesize this fragmented data landscape. By leveraging machine learning algorithms, banks can integrate information from disparate sources – core banking systems, CRM platforms, marketing automation tools, and even third-party data providers – to construct a comprehensive 360-degree profile of each customer. AI can analyze vast datasets to uncover hidden correlations and behavioral patterns that would be imperceptible to human analysts. For instance, AI can identify micro-segments based on real-time transaction flows, revealing customers with similar spending habits or investment preferences. Furthermore, AI can leverage predictive analytics to anticipate future financial needs. By considering life events (e.g., marriage, birth of a child, home purchase), combined with past financial behavior and macroeconomic indicators, AI can forecast upcoming product needs (e.g., mortgages, education loans, retirement planning). This granular understanding, facilitated by AI and machine learning, becomes the bedrock upon which personalized product recommendations, proactive service delivery, and ultimately, optimized CLV and product holding ratios are built.
Personalized Orchestration: Delivering the Right Offer at the Right Time
Traditional, one-size-fits-all marketing approaches in banking are increasingly ineffective in today's dynamic and personalized world. Generic product promotions, mass email blasts, and static website content fail to resonate with individual customers who expect tailored experiences. These outdated methods often result in low conversion rates, wasted marketing spend, and missed opportunities to deepen customer relationships. Customers today expect their bank to understand their unique financial circumstances and offer solutions that are relevant to their specific needs and goals. This necessitates a shift from broad-based marketing to personalized orchestration, powered by AI.
The Backbase Customer Lifetime Orchestrator (CLO) begins by deeply understanding your product portfolio and their respective Customer Value Propositions (CVPs) as well as marketing branding guidelines for your bank and if any product specific across all categories. This foundational step is crucial for proactively orchestrating ongoing customer engagement.
AI empowers banks to move beyond generic campaigns and create truly individualized customer journeys. By leveraging the 360-degree customer view developed through data integration and analysis (as discussed in the previous section), AI can enable personalized offers and interactions delivered at the optimal moment through the customer's preferred channel. Instead of pushing generic products, AI can identify specific customer needs and proactively offer relevant solutions. For example, AI-driven recommendations can suggest appropriate financial products – mortgages, personal loans, investment opportunities – based on a customer's individual profile, financial goals, and risk tolerance. New customers can benefit from personalized onboarding experiences, where AI guides them through relevant products and services based on their specific needs. Furthermore, AI can trigger proactive offers based on specific customer actions or life events. For instance, a customer who has just been promoted might receive a personalized offer for wealth management services, while a customer who has recently started a family might be presented with tailored insurance options. This level of personalization, enabled by AI, not only improves customer engagement and satisfaction but also significantly increases the likelihood of product adoption and contributes to higher CLV and product holding ratios.
Boosting Product Holding Ratio through Intelligent Recommendations
Increasing the product holding ratio – the number of products and services used by each customer – is a crucial driver of revenue growth and customer loyalty in banking. Customers who have multiple relationships with a bank are not only more profitable but also less likely to switch to competitors. However, simply offering more products isn't enough. Customers need solutions that genuinely address their individual financial needs and goals. This is where AI-powered intelligent recommendations come into play. AI can analyze the wealth of data available to banks to identify opportunities for cross-selling and up-selling, ensuring that recommendations are relevant, timely, and personalized.
AI can identify opportunities to cross-sell and up-sell by analyzing existing product holdings and customer behavior. For example, a customer who has a checking account but no credit card might be a prime candidate for a tailored credit card offer. AI can analyze their spending patterns to recommend a card with appropriate rewards and credit limits. Similarly, a customer with a mortgage might be interested in related products like home insurance or investment options. AI can proactively identify these opportunities and deliver personalized recommendations through the customer's preferred channel. Beyond simple cross-selling, AI can also identify customers who would benefit from consolidating their finances with the bank. For instance, a customer with multiple loans from different institutions could be offered a debt consolidation loan, simplifying their finances and potentially saving them money. AI can also facilitate the creation of bundled product packages tailored to specific customer segments. For example, young professionals might be offered a package that includes a checking account, a credit card, and a basic investment product, all designed to meet their specific financial needs.
Consider this example of AI-driven orchestration to boost product holding ratio: "Based on your specific objectives—such as NTB onboarding within 90 days, driving wealth product adoption among affluent customers, or maximizing credit card EMOB—our AI Agent Customer Lifetime Orchestrator generates optimized 90-day plans. These plans leverage customer engagement data from our embedded CDP, including login history, financial behavior, and product holdings. Importantly, our AI agents continuously learn and refine plan generation based on its past performance, ensuring ongoing optimization and self-learning." This demonstrates how AI can be used to create highly targeted and personalized campaigns to increase product adoption. For NTB customers, the AI might recommend specific products based on their initial interactions and demographics, guiding them through a personalized onboarding journey. For affluent customers, the AI might identify opportunities to cross-sell wealth management products based on their investment history and risk tolerance. For existing credit card holders, the AI might suggest increasing their credit limit or adding supplementary cards based on their spending patterns. The key is that the AI continuously learns and adapts, ensuring that the recommendations are always relevant and optimized for maximum impact on product holding ratio.
Maximizing CLV through Personalized Engagement
Customer Lifetime Value (CLV) is inextricably linked to customer engagement. Highly engaged customers are more likely to remain loyal, purchase additional products and services, and ultimately contribute more to the bank's bottom line. Conversely, disengaged customers are at a higher risk of churning, taking their business elsewhere. Therefore, maximizing CLV requires a focus on fostering meaningful and personalized engagement. AI offers powerful tools to enhance customer engagement by tailoring communications, interactions, and even loyalty programs to individual preferences and needs.
AI can personalize customer engagement in several key ways. First, it can deliver personalized financial advice and insights through the customer's preferred channels. Instead of generic market updates, AI can provide tailored recommendations based on a customer's specific portfolio, risk tolerance, and financial goals. These insights can be delivered through email, mobile app notifications, or even personalized video messages from a financial advisor. Second, AI can enable proactive customer support and issue resolution. By analyzing customer interactions and identifying potential problems, AI can trigger alerts for customer service teams to reach out proactively and offer assistance. This not only resolves issues quickly but also demonstrates the bank's commitment to customer satisfaction. Third, AI can power gamified loyalty programs that reward customers for engaging with the bank. Instead of generic rewards, AI can personalize incentives based on individual customer preferences and behavior. For example, a customer who frequently uses mobile banking might be rewarded with bonus points for digital transactions, while a customer who prefers in-person interactions might receive discounts on branch services.
Consider these concrete examples: Imagine a customer nearing retirement. AI can analyze their savings, investment portfolio, and projected retirement income to generate personalized financial planning advice. This advice can be delivered through a secure portal or via a consultation with a financial advisor, tailored to their specific needs. Or, consider a customer who is struggling to keep up with their credit card payments. AI can proactively identify this and offer personalized budgeting tips and debt management resources, delivered through their preferred channel. Finally, a customer who frequently uses the bank's mobile app might be offered personalized challenges and rewards within the app, encouraging continued engagement and use of digital services. These examples illustrate how AI can be used to create personalized experiences that not only enhance customer engagement but also drive long-term loyalty and maximize CLV.
The Human-AI Partnership: Empowering Bankers and Marketers
While AI offers immense potential for transforming banking, it's crucial to recognize that it is a tool, not a replacement for human expertise. The most effective approach is a human-AI partnership, where AI augments human capabilities, empowering bankers and marketers to make better decisions and deliver more personalized experiences. Human oversight and intervention remain essential, particularly in areas requiring judgment, empathy, and ethical considerations. AI algorithms, while powerful, are only as good as the data they are trained on, and human review is necessary to ensure fairness, accuracy, and compliance. Furthermore, human interaction remains vital for building trust and rapport with customers, especially for complex financial decisions.
AI empowers bankers and marketers by providing intelligent insights and tools that streamline workflows and enhance decision-making. AI-powered dashboards can provide real-time insights into customer behavior, campaign performance, and market trends. These dashboards can aggregate data from multiple sources, providing a holistic view of the business and enabling bankers and marketers to identify opportunities, track progress, and make data-driven decisions. For example, a marketing manager can use an AI-powered dashboard to monitor the performance of different campaigns, identify which channels are most effective, and optimize marketing spend in real-time. Similarly, a branch manager can use a dashboard to track customer traffic, identify peak hours, and allocate resources effectively. Beyond dashboards, AI can also provide specific recommendations that bankers can use to personalize customer interactions. For instance, AI can suggest relevant products and services to offer a customer based on their individual profile and financial goals. Bankers can then use these recommendations as a starting point for conversations, tailoring their approach based on the customer's specific needs and preferences.
Consider these concrete examples: A relationship manager working with high-net-worth clients can leverage AI-driven insights to understand their clients' investment portfolios in greater detail, identify potential opportunities, and tailor their advice accordingly. The AI may highlight changes in market conditions that are relevant to a client's specific holdings, enabling the relationship manager to proactively reach out with personalized recommendations. Or, imagine a marketing team planning a new product launch. AI can analyze customer data to identify the target audience, predict their likely response to different messaging, and recommend the optimal channels for reaching them. This allows the marketing team to focus their efforts on the most promising segments, maximizing the impact of their campaign. The human element remains critical: the relationship manager still needs to build rapport with the client, explain complex financial concepts, and address any concerns. The marketing team still needs to craft compelling messaging and ensure that the campaign aligns with the bank's overall brand strategy. The AI acts as a powerful assistant, providing valuable insights and recommendations, but the human touch remains essential for building trust and delivering exceptional customer experiences.
The Future of Augmentation in Banking
The future of augmentation in banking is brimming with potential. Emerging trends in AI, such as advancements in NLP and more sophisticated machine learning models, promise to further personalize customer interactions and optimize product recommendations. NLP will enable more natural and intuitive communication between customers and AI-powered systems, opening up new avenues for personalized financial advice and support. As AI models become more sophisticated, they will be able to analyze even more complex datasets and identify even more nuanced customer needs, leading to hyper-personalized offers and experiences. Real-time data integration and analysis will become increasingly important, allowing banks to respond to customer needs and market changes with greater agility. The key to success in this dynamic landscape will be continuous learning and adaptation. Banks must embrace a culture of experimentation, constantly testing and refining their AI strategies to ensure they are maximizing CLV and product holding ratio. This means investing in data infrastructure, developing robust AI capabilities, and fostering a team of data scientists and AI specialists or can use a free trial of Backbase Customer Lifetime Orchestrator (CLO).
In conclusion, the intelligent bank of the future is not a vision of robots replacing human bankers. Rather, it is a vision of humans and AI working in synergy, each leveraging their unique strengths to create a truly customer-centric banking experience. AI-powered augmentation represents a paradigm shift, moving beyond simple automation to empower bankers and marketers with the insights and tools they need to deeply understand their customers, personalize interactions, and deliver relevant solutions at the right time. By embracing the power of augmentation, banks can unlock new levels of customer engagement, drive sustainable growth, and build lasting customer loyalty. The time to act is now. Those banks that embrace this transformation will be best positioned to thrive in the increasingly competitive and customer-driven banking landscape of tomorrow.
