In my best ominous television voice: every fifteen seconds, a retail bank or financial institution fails one of its customers. Okay, maybe it’s not that often. But somewhere, some poor customer is shouting into their smartphone this very moment, vowing to move their accounts to The Other Guy.

It really does happen a lot: according to the Coveo Service Relevance Report 2022, 73% of consumers will call it quits with a brand after just three bad experiences. When retail banking and credit unions are increasingly synonymous with digital banking, there’s ample opportunities for banks to drop the ball. 

Which might explain why the customer satisfaction benchmarks are so high for banks: the American Customer Satisfaction Index has the benchmark for national banks at 76; for regional and community banks, that benchmark rises to 80.

So how do the Capital Ones and the PNCs reach this level of customer experience? More often than not, they’re heavily invested in customer journey mapping — and they’re investing in artificial intelligence.

To better understand how the two go together, first let’s unpack the fundamentals of customer journey mapping.

What is Customer Journey Mapping in Retail Banking?

There’s what we think the customer journey map should be, and then there’s what your banking customer actually wants to experience. We deduce the latter from what customers are actually telling us — data points from websites, customer communities, mobile apps — all the myriad ways that customers interact with us digitally. 

This data tells us exactly where, how, and when customers engage with us along the customer lifecycle. That’s customer journey mapping in a nutshell: a representation of all the customer insights you’ve collected, from first hearing about you to the post-purchase experience. 

An image depicts a potential retail banking customer journey map.
A customer service journey involves a variety of touchpoints, and no two customers will follow the same path.

As one might imagine, customer journey mapping for retail banking enterprises can be quite comprehensive. There’s different business segments, customer categories, and products. To capture all of the interactions, including intent, motivation, and sentiment, requires organizational and technological investments.

At a high level, here are the traditional best practices that go into building a customer journey map: 

1. Define Your Customer Need

Naturally, the needs you define for your financial services institution will vary from others; all audiences are unique, especially in an age where one-to-one personalization is the bar. Which is why it’s so critical that retail banks dig into the quantitative and qualitative data at hand.

More often than not, customers are already telling you all about their needs and wants — through content discovery. How customers interact with your digital presence through search, clicks, time on page, and other metrics can tell you a lot about what they’re finding — and where they’re coming up empty. 

2. Map The Typical Customer Experience Journey

These days, the customer journey map is rarely linear. What we do know is that 50% of consumers interact with their bank weekly through mobile apps and websites. Are those customers, potential or existing, finding the answers they’re looking for? 

By tracking those interactions over time, you gain a better understanding of how a certain customer segment interacts with a customer touchpoint.

3. Identify The Typical Obstacles Encountered in Each Journey

For example, you can track the common page paths of a specific demographic to figure out why they leave your website without converting on a particular banking product page. 

Or perhaps a certain segment calls in with the same objection time and time again, highlighting a lack of information somewhere along the line.

How Banks Can Apply Journey Mapping to Customer Service Initiatives

There’s no question that banks are managing a rapid proliferation of digital channels in banking services. And no matter which channel they choose, customers now demand personalized experiences, almost across the board.

As to what that personalization ought to look like depends on the data. For example, if a bank knows the exact words that a potential customer or existing customer uses to search their site for low-interest home loans, they can personalize search results to bring more value to that experience. 

The same goes for queries, questions, clicks, and other interactions. Even from first-party data points alone, banks can extract considerable detail about the customer behavior performed and paths taken by different individuals (or groups), as well as what they might need to take the next step.

Retail Banking Use Case: Behavioral Data

Elsewhere, we’ve defined behavioral data as what customers do and interact with within your digital presence. Let’s consider behavioral data within the context of a specific banking segment: mortgage applicants.

Across the life of a mortgage application, applicants will interact with a variety of digital touchpoints. They fill out the application online, or using the mobile app. 

Perhaps they interact with the bank’s educational resources (blog posts, for example) related to mortgages, topics like interest rates, private mortgage insurance, etc. Some will dig in for more context on payment terms, credit score implications, and so on while they wait for their answer.

All of these data points paint a picture of the journey for mortgage applicants, one that gives the bank an idea on how to personalize and optimize each banking customer experience for better outcomes. 

Of course, capturing and analyzing this kind of data at scale, across customer segments, can be complicated. Indeed, 95% of executives believe that legacy systems and outdated tech capabilities are inhibiting their ability to optimize data.

That said, the right technology is available.

Start crafting your customer journey mapTemplate: Outline Your Customer Self-Service Journey Map

3 Ways AI Enhances Bank Journey Mapping

So, where should these executives direct their investments? How do retail banks and other financial institutions continuously map and optimize customer journey analytics across their often vast customer bases? 

Not surprisingly, AI holds at least some of the answers. AI, and more specifically, relevance platforms, can bring journey mapping to life for retail banks in a few ways: 

1. Personalize Search Results Across Digital Channels

Earlier, I touched on the ability to personalize content paths based on behavioral signals. AI can do the same for search, recommendations, and personalization. Relevance technologies use past search term data, clickstream interactions, site behavior, and other data points to boost the most relevant and useful content. What’s more, AI considers what the customer interacts with to learn and improve the next interaction. 

Here, the potential use cases are many. For example, let’s say over time AI learns that most customers searching for “existing auto loan” aren’t in fact looking to log into their account and check balances. In fact, nine times out of ten, they abandon the journey when they reach the login page from search results. 

But when AI boosts the landing page for refinancing a current auto loan, click through and average click rank goes way up. And because search results are now optimized for the true intent, the customer is more likely to actually convert (in this case, apply for a refinance), which can have a direct and measurable influence on revenue.

2. Unearth the Content Customers Want But Can’t Find

A relevance platform can also reveal content gaps. That is, the high-volume search keywords that lead to no results, or very poor click-through rates. The application here is quite simple: a high-volume keyword with “No results returned” indicates a hole in your customer journey. 

Imagine if everyone who calls in to the contact center frustrated that they can’t apply for a business credit card online would have avoided the call altogether if they could have found the content. Through content gap analysis, you can find those holes and create relevant content for those search experiences and prevent abandoned or unsuccessful digital journeys.

3. Free Up Talented Support Staff to Focus on High-Level Orchestration

There are some banks, who shall remain nameless, who hire teams of people just to create and enforce business rules. This is a time-consuming effort that can be handed over to AI. While AI automatically learns and optimizes as searches and clicks happen, banking teams can free up headcount to focus time and resources toward higher-level customer experience strategy.

It’s flipping the script really; rather than use headcount to guide customers toward a particular outcome, you put that energy toward creating new and improved outcomes. In the context of service, some call this a shift-left model: customer queries are handled by the lowest-effort, least expensive support tier.

Ideally, that lowest tier is actually self-service, which is continuously run and optimized with the help of AI.

The Last Word on Journey Maps for Banking

We have so much to learn from our customers, and customer journey mapping can help. It’s an exercise in understanding who our customers are, what motivates them, and what they need — in their own words and based on their behavior. 

Why do it? To keep customers happy and retain them long-term, yes. More specifically, we’re answering the call of the day, which is elevated customer expectations. Something more proactive, personalized, and even emotional. Being there even when customers don’t expect us to be. Today, customers don’t just demand it, but they’ll switch banks in a heartbeat if they can’t get it.

To deliver on this caliber of experience, 63% of retail and institutional banks plan to invest in customer data analytics; and 60% plan to invest in customer service. Unfortunately, 84% face challenges adopting AI in support of these efforts. As our own customers in banking and finance are discovering, a relevance engine bridges the gap between planned investments and persistent challenges to improving the customer experience. 

Of which AI-enhanced customer journey mapping plays a central role.

Dig Deeper

Start planning your integrated self-service journey with our Customer Self-Service Journey Map Template.

Start crafting your customer journey mapTemplate: Outline Your Customer Self-Service Journey Map

Or if you’d like to get a bigger picture of how Coveo can unify and personalize all of your banking journeys at scale, download a copy of our Blueprint for Personalized Digital Banking Experiences. 

Tailor interactions and boost loyaltyEbook: Blueprint for Personalized Digital Banking Experiences

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About Martin Ceisel

Martin Ceisel is a freelance writer specializing in customer service and B2B. He can be reached on LinkedIn and Twitter.

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