If you find yourself asking “what is customer journey analytics?” you’re not alone. Customer journey analytics is the weaving together of every touchpoint that a customer interacts with, across multiple channels and over time.
It connects millions of events into journeys from your customers’ point of view and is a data-driven approach to analyzing, measuring and influencing your customers’ journeys.
Gartner defines customer journey analytics as the process of tracking and analyzing the way customers use combinations of channels to interact with an organization and covers all channels present and future which interface directly with customers.
Customer journey analytics is an essential component of any enterprise customer journey management program. Journey analytics enables customer experience, analytics, customer care and marketing professionals to manage and measure cross-channel journeys over time and improve customer experiences. By analyzing millions of data points in real time, you can pinpoint underlying sources of friction, reveal the root cause of behavior that positively or negatively impacts your customers’ experiences and orchestrate actions to optimize CX and achieve business outcomes like increasing revenue and reducing churn.
In this post, I’ll take a look at what’s driving leading companies to adopt customer journey analytics and the various ways leading CX teams are using these solutions to improve CX and business results.
Maximizing satisfaction with customer journeys has the potential not only to increase customer satisfaction by 20 percent but also to lift revenue by up to 15 percent while lowering the cost of serving customers by as much as 20 percent.
Customer Experience is Driving the Need for Customer Journey Analytics
Customer experience has risen in importance over the last decade and is now lauded as the primary competitive battleground for enterprises across industries. From financial services and telecommunications to healthcare and retail, it’s now recognized that exceptional CX can significantly impact business outcomes like share of wallet, retention and customer lifetime value (CLV).
According to Forrester Research, better customer experience actually correlates with higher revenue growth. CX drives three types of customer loyalty: retention, enrichment and advocacy. Increased customer loyalty in turn tends to drive increased revenue growth.
The focus has shifted from measuring interactions within siloed touchpoints to developing a unified view of the customer and measuring the journeys that comprise their overall experience. To achieve this unified customer experience, customer analytics must evolve from retrospective reporting to real-time, behavior-driven engagement. Customer journey analytics is the means to enable this transformation.
Why Customer Journey Analytics is Different from Journey Mapping
Customer journey mapping is a standard practice, not only with customer experience professionals, but also within marketing, customer service, user experience (UX), product management and IT.
A customer journey map is basically a visual illustration of the steps of a customer journey. The process of creating a journey map is discussed in detail in many posts, including one by Neil Davey called How to create a customer journey map.
Although they share a common goal, customer journey analytics and journey mapping take very different approaches. More importantly, the results also differ in five very distinct ways:
Data Driven. Pictures without data are just stories. Data is what makes the picture come alive. The key difference between customer journey mapping and customer journey analytics is that the latter is based on hard data—millions or even billions of individual interactions—rather than the subjective interpretation of interviews or observations of a small number of ‘representative’ customers.
Comprehensive. Journey mapping exercises tend to simplify the endless variety of customer journeys and boil them down to a single representative journey. In contrast, customer journey analytics reveals the wide variety of paths that real customers actually take across channels, identifies the most significant ones and enables you to measure their impact on your business.
At Scale. Many organizations employ journey mapping to successfully create a few high-level macro-journeys. But, it’s extremely time-consuming to use journey mapping techniques to flesh out and visualize all the detailed steps that make up specific parts of the overall journey (a micro-journey). Customer journey analytics tools can let you view the macro journey, as well as provide endless opportunities to drill-down and uncover the behaviors that comprise them.
Real Time. Unlike the static snapshots that journey maps represent, journey analytics are inherently time-based, which allow you to see how journeys change over time. This enables marketers, for instance, to identify opportunities for real-time engagement based on a data-driven understanding of customer behavior.
Actionable. Pictures are great for internal communication and aligning employees across your organization around a common, customer-focused perspective. But after you determine which parts of the journey are providing the poorest experience, how do you act on them? Customer journey analytics enables marketing and CX teams to not only identify which journeys are most important, but automatically engage with each customer at the right time, through the best channel and in a personalized way.
Journey Analytics Can Quickly Answer Complex Questions
A journey mapping project for a financial services firm might uncover, based on a series of interviews, that a subset of clients usually place a phone call to their financial advisor before purchasing a new investment product. By using customer journey analytics, you can go a step further and learn the answers to much more complex questions, such as:
What percentage of clients take this path?
What steps did clients that ultimately purchased the product take prior to the call?
What steps did clients that did not purchase take prior to the call?
When is the best time to interact with a given client?
What is the best channel to interact with the client?
What other paths do clients take?
Which kinds of clients take each path?
How do we add value for each client in a given context?
Why Traditional Analytics Approaches Often Fail
Today, companies are looking to switch from product-centric to customer-centric organizations. The goal is to bring together all the different pieces of data in a contextual manner to create an experience that is personalized to the needs of an individual customer. To do that, they have to know the answer to many complex questions, such as the ones listed above. Companies are struggling to answer these types of questions using traditional analytical tools and approaches due to five main limitations:
Volume and velocity of data. The number of customer touchpoints and the volume of data produced by each has exploded. Websites, social media, point-of-sale systems, contact center systems, chat applications and new IOT data sources are generating massive volumes of data, often continuously.
Data integration is difficult and time consuming. Data exists in silos across different systems. Extracting data out of the original sources and integrating it has led to innumerable challenges of data mismatches, missing and bad data that makes traditional data integration projects stretch out over weeks or even months.
Scarcity of skills and resources. Even after integrating data, analyzing it in a practical and actionable way often requires skilled data scientists, who are difficult to hire and typically have more projects than they can possibly complete.
Inability to attain rapid customer insights. Traditional analytical approaches typically require analysts to spend 80% of their time assembling and cleaning data. This commonly results in multi-week wait times to obtain answers for questions about omnichannel customer behavior, which in today’s world of rapidly shifting customer behavior, the answer is often rendered useless before it can be acted upon.
Gap between analytics and action. Existing marketing systems typically automate engagement for only one or at most a few channels, which cannot capture your customers’ real omnichannel journeys. Even if you’ve recently deployed a CDP or journey orchestration platform, it likely won’t help you to analyze historical customer behavior and use it to drive action based on each customer’s entire experience with your company.
Customer journey analytics platforms, sometimes offered within customer journey management software, are designed to overcome these challenges. A superior journey analytics solution is built to aggregate and present data in an easy, practical and efficient way to facilitate engagement that reflects your customer’s entire experience with your organization, not just their most recent interaction in a single channel.
Customer Journey Analytics Delivers Immediate Business Impact
Leading companies are now using customer journey analytics to attain real business impact. Some of the most frequent journey analytics use cases or applications enable you to:
Use NPS to Prioritize Initiatives to Improve Customer Experience
A regional cable and internet provider uses customer journey analytics to identify and prioritize areas for CX improvement.
The customer care team at this regional provider of cable and internet services knows that customers who miss a payment often ask for a late payment fee waiver.
So, they want to evaluate and compare the customer experience for three different channels their customers can use to resolve a late payment fee: the mobile app, web chat and the call center.
By conducting a simple analysis in Pointillist, they find that not only did the highest volume of customers make an inbound call to request a fee waiver, but those that did had a lower NPS score than those that resolved the payment fee through the mobile app or web chat. In contrast, the mobile app journey had the highest average NPS score.
As a result, the customer care team chooses to launch an initiative to guide more customers who missed a payment to the app, so they can increase overall customer satisfaction, while simultaneously reducing cost to serve.
2. Increase Operational Efficiency and Cost Savings
Customer journey analytics enables teams across your organization to identify journeys that hinder customer experience and drive operational costs. Effective teams use journey analytics to find opportunities to streamline inefficient journeys in order to lower customer effort, while decreasing cost to serve.
Decrease Support Call Volume and Spend with Customer Journey Analytics
A top retail bank wants to decrease support call volume and enhance operational efficiency. Tasked with finding the solution, the bank’s CX team uses a journey analytics platform to understand what drives new clients to make a support call.
The team begins by identifying new clients as those who were sent their first invoice in the last month. Of the 12.5 million people who received their first bill, 1.1 million made a support call to complete their payment.
The data reveals that 81% of those calls were initiated because of a problem with one of the bank’s self-service channels, interactive voice response (IVR).
By uncovering the root cause of the support calls and comparing the data across multiple touchpoints and journeys, the CX team can now focus on finding and addressing issues in the IVR system.
Their goal is to decrease the number of support calls and reduce overall call center costs, while simultaneously improving customer experience.
3. Manage Retention and Churn
CX teams leverage journey analytics to uncover not only the journeys that result in the highest volume or frequency of churn, but the behavioral indicators that signal churn. These solutions provide insights that help you determine which areas of CX need immediate optimization to reduce churn and increase retention.
Identify High-Impact CX Issues that Lead to Churn with Journey Analytics
The CX team at a leading telco uses Pointillist to understand the drivers of churn, so they can identify and prioritize CX initiatives that will have the largest impact on reducing churn.
First, the team uses customer journey analytics to analyze CX issues across all support channels to identify the most common issues customers encounter that could subsequently lead to churn. Through the analysis, they find that billing and tv issues are by far the most prevalent.
However, in the next step of the analysis they discover that while the highest volume of customers are experiencing billing and tv issues, internet-related issues are actually driving the largest number of account closures and are responsible for the greatest revenue loss. They also determine that customers who experience internet issues are most likely to churn, and at the highest velocity.
Armed with this new information, the CX team uses Pointillist to isolate two specific internet-related issues that are having the highest impact on churn, and prioritize an initiative to address them.
They anticipate that the initiative will reduce their overall churn rate by roughly 2.0%, saving the firm an estimated $1.8 million in lost revenue over the first 12 months after implementing the solution.
4. Grow Revenue
Customer journey analytics supports revenue growth by enabling organizations to orchestrate experiences based on a customer’s context. These platforms fuel CX and marketing teams’ ability to deliver upsell and cross-sell offers to the customers who are most likely to convert, maximizing lifetime value and revenue.
Customer Journey Analytics Helps a Major Retail Bank Increase Credit Card Conversions
A retail bank leverages customer journey analytics to identify the most effective message and channel to drive conversions.
The credit card team at a major retail bank is tasked with improving credit card opening rates among millennials. To understand the role that different channels play in credit card offers and their respective efficiencies, the bank uses the Pointillist Customer Journey Analytics platform.
They uncover a variety of customer journeys across online and offline channels—such as branch visits, website browsing, mobile data, email data and in-app interactions—that lead customers to view a credit card offer. Within minutes, they see how many customers go on to apply for a card online versus how many reject or ignore the offer.
With one click, they are able to see how many customers move forward at each step, how many drop out and how many are still present at that step.
Using Pointillist, the bank determines that the offer converts better for people who see it as an email than as a text message or within the bank’s mobile app. Based on this information, they decide to send a personalized email offer to those who view the credit card offer and then abandon their journey.
A few days later, the credit card team reviews the results and are delighted to see a large number of the email offers have been converted into new credit card applications. Since Pointillist is already integrated with their email platform, they decide to activate an audience and add anyone abandoning this journey in the future to the new email campaign.
This multichannel analysis would have taken days and consumed high-level data science resources to accomplish using traditional analytics approaches. Using Pointillist, the credit card team is able to quickly find, analyze and orchestrate a solution themselves with minimal outside support.
Now It’s Your Turn
Today, leading companies are reorganizing around the customer and using customer journey analytics to understand behaviors and shape experiences. By measuring and monitoring the real-world paths your customers actually take across channels and over time, customer journey analytics enables you to improve customer experience and achieve positive business outcomes.