The stark acceleration of digital adoption and the shift in magnitude of customer expectations over the past few months has led leading financial institutions to look for new ways to drive efficiency growth and differentiate their customer experiences. 2020 has tested customer engagement strategies at financial institutions, large and small, and uncovered key areas of weakness.
Effectively managing and utilizing data is one of the primary obstacles that financial institutions face in their transformation initiatives. Lines of businesses are often siloed in the data they generate and receive, resulting in an incomplete view of the overall scope and performance of each relationship, particularly for complex and interrelated primary banking relationships that can span deposits, lending, wealth, and commercial. Banks simply have a messy array of technologies. Some business lines use one solution for relationship management, while another might use email/spreadsheets, for example. These disparate systems create data silos which, across the entire organization, have adverse effects on customer experience and significantly reduce the ability to harness valuable data insights.
Relationship management systems are central to data collection, customer engagement, and analytics. Yet many financial institutions struggle to adopt and implement these solutions in ways that drive their digital transformations forward. Though CRMs have been around for decades, the banking industry has been relatively slow to adopt these technologies. This resource will provide a brief history of CRM systems, their benefits in banking, and explore the root causes behind adoption and implementation challenges.
2020 tested customer engagement strategies at financial institutions, large and small, and uncovered key areas of weakness.
The essence of customer relationship management is rooted in its origins. In the 1970s, companies actively started pursuing customer feedback to gauge satisfaction through annual surveys. This was the start of what we refer to today as Customer Relationship Management (CRM). Over the next couple of decades, the concept transformed into more of a system of record – meaning a system that stores and provides access to information. The first CRM system was developed by Siebel Systems in the 1990s. Around this time, the concept of systematically tracking customer interactions, behaviors, and engagements was popularized. Other software companies like Peoplesoft, SAP, and Oracle started catching on and introducing their own CRM modules. Cut to the turn of the century and the linkage between these systems of record to business intelligence technology to create “systems of engagement” – meaning systems that facilitate the personalization of experiences throughout the customer experience. Salesforce championed this market and delivered a CRM system to drive engagement that worked for nearly every industry.
As digitalization continues, these progressively cloud-based systems must extend beyond simple personalization and anticipate customer needs in real-time using machine learning and AI models.
CRM systems are now widely adopted in nearly every industry to optimize sales productivity, customer engagement, and marketing but over 20% of banks still use email/spreadsheets to manage their relationships1 . Bank requirements for CRM are significantly different compared to other industries - they need systems with banking-specific data models that include financial accounts, household and professional relationships, credit data, and assets. Traditional “horizontal” CRM systems lack these capabilities out-of-the-box, requiring deep and often costly, customization.
Just like “CRM” brought the customer voice to light back in the 1970s, relationship management solutions can give today’s forward-thinking financial institutions an opportunity to deepen their understanding of customer needs while delivering prescriptive guidance and support to relationship managers on how best to engage.
A 2019 report cites, “boosting productivity is the number one strategic priority for mid-market banks. In fact, more than half of our survey respondents selected boosting productivity ahead of new customer growth, expanding digital presence, and improving security.²”
The efficiency ratio is one of the main ways banks measure their productivity, and historically a 50% efficiency ratio has been considered the industry standard. The same survey indicates that the adoption of digital/SaaS technologies is a major way that banks can increase their efficiency – “94% shared that cloud computing/SaaS technology was the most effective way their institutions are increasing their efficiency…. Even more telling, 91% of survey respondents indicated a wide acceptance of technology in boosting productivity”.
Purpose-built banking relationship management solutions help financial institutions achieve efficiency gains and overall business growth in four key ways – customer acquisition, engagement, retention, and growth.
Unified data sources help inform strategic growth decisions, marketing strategies, and onboarding workflows across the organization.
Simple user interfaces and a complete single view of every customer facilitate personalized, proactive, and timely engagement at every stage of the customer journey. By placing the customer at the center of every interaction, FIs can build long-term relationships and win primary ownership of every relationship, not just the ones they know best.
AI and machine learning models, along with simple rule-based analytics, can help banking teams identify and mitigate churn risks across portfolios.
AI and machine learning empowered CRM systems also drive efficiency gains throughout the sales process by uncovering unmet needs, cross-sell opportunities, and key transaction trends – leading to deeper, more personalized, and loyal relationships. Solutions that leverage modern software architecture also provide ways for financial institutions to grow as market demands and customer expectations shift even further into the decades ahead. API-enabled technologies are now table stakes and one of the only ways financial institutions can escape their data fragmentation.
Banking relationship management solutions also provide unique capabilities to a variety of end-users at financial institutions. Ensuring these users work with the same data, regardless of business line, is critical to delivering on the promise of omnichannel customer experiences and achieving the same type of frictionless journeys customers experience in their consumer lives.
The day in and day out users of CRM – they derive immense productivity gains from banking-specific solutions that unify data and deliver a complete view of relationship data and linkages. Growing customer portfolios and an increasingly broad range of product offers make it nearly impossible for relationship managers to provide personalized and authentic experiences at scale with current solutions. Over 85% of revenues in an average customer portfolio come from the 25% of relationships they know best3. What if they could engage ALL of their customers in the same proactive manner?
Data silos force RMs to hunt for data across multiple systems, wasting time better spent with the customer. Breaking down these silos helps RMs deepen their understanding of every customer, at a glance, and deliver timely guidance and personalized engagements. FIs who go a step further a leverage AI and machine learning models to get smarter about their data can see significant returns on investment. “Across more than 25 use cases, AI technologies can help boost revenues through increased personalization of services to customers (and employees); lower costs through efficiencies generated by higher automation, reduced error rates, and better resource utilization; and uncover new and previously unrealized opportunities based on an improved ability to process and generate insights from a vast trove of data4.” Some examples of insights and analytics include:
Highlight Surplus Liquidity insights for commercial customers using a commercial checking account but have high surplus liquidity, signaling they may be strong candidates for a sweep account or further investment opportunity to improve their financial position.
Recommend a home equity line-of-credit (HELOC) to a customer who has two kids approaching college age, a mortgage, and great credit – a HELOC could help them pay potential upcoming tuition costs.
Identify potential wealth customers from across the bank by surfacing large cash payouts associated with bonuses and/or vesting and delivering insights directly to the relationship owners or wealth managers to reach out.
Without access to a unified view of data from across the bank, leadership teams struggle to effectively react to changing customer needs and macro-level shifts in transactions, product performance, and online engagement.
Before choosing and implementing a relationship management solution, financial institutions must consider what improvements they want to achieve and how it will affect the overall customer experience. Don’t let the technology define the strategy. Whether pursuing a “homegrown” solution or working with a fintech partner, relationship management should be customizable and scalable to meet evolving business requirements and customer needs.
Technology itself will not create the incremental changes to client experience and overall digital transformation that financial institutions are looking to achieve. It starts with the alignment of goals, leadership teams, and both human and financial resources. Traditional operating structures at banks can stifle agile transformation because various business lines define goals unilaterally and struggle to align data intelligence and analytics strategies with each other. While not all relationship management solutions focus on AI and analytics, it is a clear enabler for those capabilities. They require a “single source of truth” to produce timely and relevant intelligence. Getting “buy in” from key stakeholders should be one of the first steps taken before vetting fintech partners or developing a homegrown solution.
Cloud Based
Purpose-built for Banking and any Line of Business
Flexible Software Architecture
Out-of-the-box AI and Machine Learning Models
The current banking relationship management market includes a number of horizontal CRM solutions that require extensive customization to work for financial institutions – “a mile wide and an inch deep.” These solutions are expensive to implement, often lack key banking-specific capabilities, and require continued maintenance. Historically, these CRMs built for other industries struggle to win banker adoption across the organization because of their complexity, leaving banking teams to fend for themselves in outlook or various spreadsheets.
Along with cultivating a fundamental shift in customer-centric processes, selecting a banking-specific solution is critical for a successful CRM implementation, particularly for small-mid-sized financial institutions who generally don’t have the same budget to spend customizing traditional CRM solutions to meet banking needs.
Winning primary ownership of the customer relationship and meeting and exceeding customer expectations in the new digital-first era, requires banking providers to assess their engagement structures. Without real-time access to valuable data across lines of business, financial institutions will miss valuable opportunities to build long-term loyalty with current and new customers. Enabling your banking teams with intelligent systems of engagement that combine data from across the organization and deliver actionable insights and analytics is increasing becoming a requirement for digital-first banks.
Growing product portfolios and a new wave of competitors seeking to unbundle traditional banking relationships have put the pressure on FIs slower to adopt digital technology. Adapting quickly and delivering guidance and support when clients need it most is how the leading FIs of the next decade will champion their growth.
1. Bottomline Internal Data
2. West Monroe, “Driving Down the Bank Efficiency Ratio: Despite Digital Adoption, Vast Improvements Remain,” August 2019
3. Mckinsey & Company, “Special Edition on Advanced Analytics in Banking,” August 2018
4. Michael Chui, Sankalp Malhotra, “AI adoption advances, but foundational barriers remain,” November 2018, McKinsey.com.
