If you find yourself asking “what is customer journey analytics?” you’re not alone. Customer journey analytics is the science of analyzing customer behavior data across touchpoints and over time to measure the impact of customer behavior on business outcomes.
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.
Journey analytics is an essential component of a successful customer journey management program, which enables enterprises to manage, measure and improve experiences across touchpoints and time. It is the component that analyzes data, generates actionable insights to optimize journeys and continuously measures the performance of CX initiatives.
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 journeys are increasingly complex, but your customers expect every interaction with your organization to reflect the context of their entire experience regardless of which touchpoint they use next.
But it’s only when you look at a customer journeys across channels and time that real pain points—and therefore opportunities for positive impact—arise.
McKinsey found that “performance on journeys are substantially more strongly correlated with customer satisfaction than performance on touchpoints.” They also found that journeys are strongly correlated with outcomes such as revenue and churn.
In short, monitoring and optimizing touchpoints in a vacuum is not enough to significantly improve customer experience.
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 a visual illustration of the customer’s experience with a business. Journey maps can cover the macro-level journey (e.g. from initial awareness and engagement along the way to a long-term relationship) or zoom in on one or more micro-level journeys, such as the steps to make an online purchase.
Customer journey maps are a static snapshot of customer experience. They lack enough detail to illustrate unique behaviors of thousands or millions of customers.
Journey maps typically convey a few representative journeys. In reality, every one of your millions of customers takes their own unique journey that may be similar or differ wildly from your prototypical version.
More importantly, the results yielded by these two approaches also differ in five very distinct ways:
Journey analytics are data-driven. Pictures without data are just stories; data is what makes the picture come alive. Journey maps are based on what you want to happen, not what is actually happening. To understand how customers behave, you need data. Without data, a journey map is at best a subjective observation of a small number of “representative” customers and at worst a hypothesis made up entirely by various parts of an organization based on their own interpretation of customer behavior.
Journey analytics provide real-time insights. Unlike the static snapshots that journey maps represent, journey analytics are inherently time-based, which allows you to see how journeys change over time. Continuously measuring complex omnichannel journeys, as well as the in-journey signals that predict journey success, aligns every member of your organization on journey performance.
Journey analytics streamline testing potential CX improvements. Most enterprises lack the comprehensive, up-to-date journey data needed to optimize each interaction, so they are forced to try out experience enhancements on their customers to measure their impact. With journey analytics, CX leaders can test and track the success of journey improvements in real time. This way, you can see how customers respond to each improvement as your customers experience them.
Journey analytics uncovers the root cause of CX issues. While you can pontificate about what prevents your customers from reaching their goals with journey maps, journey analytics can reveal the actual friction points in their experiences. Powered by artificial intelligence and machine learning, customer journey analytics enables you to identify the behaviors that are indicative of problems that negatively impact CX.
Journey analytics are more actionable than maps. Journey maps work well for internal communication and aligning employees across your organization around a customer-focused perspective, but then what? Analyzing end-to-end journeys yields a continual stream of data-driven insights that teams across the organization can act upon to improve CX.
Finally, journey maps are conceptual visualizations that cannot directly measure or quantify behavior and its impact on business outcomes.
Journey Analytics Can Quickly Answer Complex Questions
Data-driven enterprises leverage customer journey analytics to identify important customer journeys, define the appropriate KPIs to measure each journey and prioritize opportunities for optimization. Typically, journey analytics is used by CX, customer service, marketing and analytics teams.
A journey mapping project at a bank 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.
But by using journey analytics, you can go a step further and discover the answers to much more complex questions, such as:
What is causing customers to behave this way?
What is the customer’s goal?
How does their goal align with our business goals?
How does this journey impact business outcomes?
What actions or journeys have customers take in the past that led to this point?
What other paths do customers take?
What are the most likely outcomes for each journey or customer? How will they impact the business?
How do we add value for each customer in a given context?
Why Traditional Analytics Approaches Often Fail
Today, enterprises 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 previously.
Organizations are struggling to answer these types of questions using traditional analytics 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 in recent years. Websites, social media, point-of-sale systems, IVR systems, speech and text analytics and IoT data sources are all generating massive volumes of data, often continuously. Apart from requiring enormous quantities of in-house processing power to analyze data at scale, teams across the enterprise struggle to access insights they need to make data-driven decisions.
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. Transforming and aggregating data to the point where it is ready for real-time journey analysis, modeling or orchestration often requires a team of data scientists and is difficult to scale. Building these teams or the programs to optimize data integration takes time few enterprises have.
Scarcity of skills and resources. Even after integrating data, analyzing it in a practical and actionable way often requires skilled data scientists and analysts, who are difficult to hire and typically have more projects than they can possibly complete. For instance, to query and extract data out of these datasets, users need to be conversant with programming languages like SQL, R or Python, and know how to manipulate data. To extract actionable insights from data, teams of skilled data scientists are needed, who are often difficult to hire, retain and motivate.
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. Moreover, most analytics systems cannot connect to systems or applications that support journey orchestration, or orchestrating personalized actions for each individual customer.
Gap between analytics and action. Existing systems typically trigger actions within one or at most a few channels, which doesn’t help your customer. Unfortunately, actions taken in one channel could have disastrous consequences if you’re not aware of each customer’s entire experience with your company across channels and time. 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 determine personalized actions without journey analytics.
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 see 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 improve experiences based on a customer’s unique 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 enterprises are reorganizing around the customer and using customer journey analytics to understand behaviors and shape experiences.
Customer journey analytics empowers every team across your organization — from data scientists to analysts to business users — to align on journeys and optimize each customer’s experience.
By measuring and monitoring the real-world paths your customers actually take across channels and over time, customer journey analytics enables you to improve CX and achieve positive business outcomes.