Everything You Need to Know About Customer Journey Analytics

Customer journey analytics is the science of analyzing customer behavior across touchpoints and over time. It measures the impact of customer behavior on business outcomes. This approach continues to gain momentum as enterprises recognize the value of customer journeys as a way to monitor customer experience (CX) performance and identify opportunities for improvement. It’s the foundation of experience orchestration and a key differentiator in the experience economy.

Measuring customer experience is still a struggle for many enterprises. And several say that quantifying the ROI of CX initiatives and investments is a challenge. Data and organizational silos are the primary barriers, particularly for banking, telecom and health insurance companies. Traditionally, these industries deal with overwhelming amounts of data from millions of customers.

Today, more organizations rely on customer journey data analytics to overcome these hurdles. In “The State of Customer Experience” report from Genesys, top-performing organizations ranked customer journey analytics as their second-highest CX-related investment priority.

This article explores how to use a customer journey analytics tool to improve business outcomes. It also explains how a customer journey analytics platform compares to alternative methods and solutions.

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Visualize and measure every journey — from beginning to end.

How Journey Analytics Powers Your Journey Management Program

Customer journey analytics is an essential component of a successful customer journey management program, which enables organizations to manage, measure and improve CX and business outcomes. It’s the component that analyzes data, generates actionable insights to optimize journeys and address customer pain points. And it can continuously measure the performance of CX initiatives.

To manage journeys effectively, you need to start by aggregating customer data across channels and time. The result is customer journey data, which is time-series data that captures customer interactions indexed by time. It powers real-time modeling and analysis, as well as orchestrating actions to optimize journeys.

Unified customer journey data lays the foundation for your entire enterprise. It’s the first step toward aligning cross-functional teams around journeys and breaking down traditional data and organizational silos.

analysis of a customer journey within a customer journey management platform

Powered by an integrated source of customer journey data, journey analytics accelerates your ability to analyze customer journeys and score journey performance. Monitoring journeys, rather than isolated customer behavior in a limited number of channels, allows you to quickly understand which journeys best enable customers to achieve their goals.

This customer journey map allows you to pinpoint sources of friction that prevent customers from reaching their goals. Tracking omnichannel behavior helps you identify issues that negatively impact both CX and business outcomes. By leveraging customer journey analytics, you can:

  • Diagnose new problems in real time
  • Determine the best way to solve these issues
  • Prioritize improvements based on potential impact on CX and business objectives

Finally, customer journey analytics is a critical piece of effective journey orchestration. Without analyzing journeys to understand what works and what doesn’t, orchestrating actions within certain journeys is a spray and pray approach. Optimizing customer journeys is most successful when you consider each customer’s overall experience with your organization.

Customer journey analytics is ideal for quantifying CX performance and tracking the results of your improvements in real time. This allows you to plan and even adjust your tactics on the fly based on actual customer behavior without waiting for an entire test to complete.

Five Ways Business Teams Benefit from Customer Journey Analytics

Customer journey analytics provides your entire enterprise with real-time insights into customer behavior. Sharing outcomes and collaborating on a journey-based approach is crucial to maintaining organizational alignment, which is essential for improving customer experience.

  1. Customer Experience Teams

Quantifying the ROI of CX investments is a customer experience challenge. Without the ability to demonstrate how customer behavior impacts business results, it’s nearly impossible for CX teams to make a business case for future budget increases.

Customer journey analytics is gaining momentum as the best solution to this problem. CX teams also use journey analytics to facilitate customer journey measurement. This inherently journey-based approach helps CX leaders monitor:

  • Journey milestones: Key steps customers take on their path to achieve their goal
  • End-of-journey success metrics: Metrics that capture how well the experience enables your customers to reach their goals and your company achieve its desired outcome
  • In-journey signals: Indicators along the journey that predict whether or not your customers are likely to achieve their goal

Together, in-journey signals and end-of-journey success metrics enable you to score all your journeys and prioritize underperforming ones for improvements. This approach helps you uncover the specific journeys that are driving changes in revenue or cost, which enhances your ability to make a powerful business case for change and future investment.

Customer Journey Analytics in Action: Leverage Journey Analytics to Understand What Drives NPS

A health insurance provider has received a number of complaints from members trying to pay their claims and seeks to understand how to improve the experience. To further their understanding, the CX team uses a customer journey analytics platform to analyze its Voice of the Customer data for members who completed the Pay a Claim journey.

The provider quickly performs the analysis by channel to understand differences between members who pay their claim through the website, the IVR, or who speak with a contact center agent. The analysis shows that members who pay their claim through the IVR have the highest Net Promoter Score (NPS), while those who paid their claim through their website have the lowest NPS.

The CX team leverages this information to prioritize an initiative to improve the user experience of the web payment experience. In the meantime, the team makes a few quick changes on the website to make members more aware that they can easily pay claims through the IVR.

  1. Marketing

Journey analytics allows marketers to better understand how to engage throughout each stage of the customer journey. In other words, it reveals the steps your customers take toward achieving their goals, rather than how they engage with specific channels.

This allows you to advance personalization efforts and show your customers you truly understand them, which will improve acquisition, up-sell and cross-sell campaigns, while increasing retention and loyalty.

Customer journey analytics also facilitates segmentation beyond demographic, geographic and firmographic information. It enables behavioral segmentation, a form of segmentation based on how your customers behave. This enables marketers to increase campaign performance through improved targeting.

Most importantly, this approach also enables marketers to connect strategy and tactics to customer experience and business results. This enables marketers to rise above opens and clicks and connect campaign performance to customer experience goals and business objectives such as revenue, cost and retention.

  1. Analytics

For customer insights, analytics and business intelligence teams, journey analytics is your key to answering internal questions faster. Sophisticated no-code journey analytics solutions drastically reduce required skills, as well as data prep time. As a result, business users can answer more of their own questions and analysts can focus their efforts on more complex queries.

Teams use journey analytics to detect issues as they arise and identify their root cause in minutes. Powered by artificial intelligence (AI), root cause analysis can rapidly reveal friction points within journeys and enable analysts to determine not just what’s happening, but why it’s happening. This way, analysts can determine exactly which interactions are driving or preventing your customers from achieving a successful outcome.

Customer journey analytics can also enhance and leverage outputs from custom data science models. Behavioral attributes can improve existing and future models. And by incorporating model output into journey analytics software, that information becomes available to every business unit across the organization to improve decision-making.

Finally, analysts can use journey analytics to increase collaboration within the organization. The resulting analytics give CX leaders, marketing teams and others the insights they need to take action and measure success.

Customer Journey Analytics in Action: Identify High-Impact CX Issues That Lead to Churn

The CX team at a leading telco uses customer journey analytics to understand the drivers of churn, so they can identify and prioritize CX initiatives that will have the largest impact on reducing customer churn.

First, the team uses customer journey analytics to identify CX issues across all support channels to find 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 those issues are responsible for the greatest revenue loss.

The company also determines that customers who experience internet issues are most likely to churn — and at the highest velocity.

Armed with this new information, the CX team isolated two specific internet-related issues that have the highest impact on churn and prioritized an initiative to address them. They anticipate the initiative will reduce their overall churn rate by roughly 2%, saving the firm an estimated $1.8 million in lost revenue over the first 12 months after implementing the solution.

  1. Digital

Every digital leader is under pressure to increase customer satisfaction while decreasing costs. And while these two goals might seem at odds with one another, journey analytics enables you to achieve both.

To meet these objectives, it’s critical to understand more than just drop off rates or leakage into agent supported channels. Journey analytics is a reliable approach to uncovering which goals customers are trying to achieve within digital channels.

Because journey analytics allows you to analyze cross-channel journeys, it’s easier to identify why customers use digital channels, why they transfer to other channels and which channels address their needs most effectively.



Based on this information, digital teams can take prescriptive steps to improve digital service and collaborate with CX, marketing and customer care teams to optimize end-to-end journeys. Together, you can decrease costs, while increasing customer satisfaction.

  1. Customer Care and Service Teams

Similarly, contact center leaders use journey analytics to identify the drivers of web and mobile chat, phone calls and even social media posts from customers. Once a customer reaches the contact center, it’s crucial to understand their journey context: the goal(s) they are trying to achieve and the path they’ve taken to do so.

Understanding your customers in this way will enable your agents to serve them more efficiently to minimize costs and improve customer effort and satisfaction. Further, service improvements will help reduce the volume of requests or cases that are escalated to supervisors. These typically cost you time, resources and your customers’ patience. Journey analytics is a crucial part of contact center optimization.

Quantifying success within the contact center is a challenge every enterprise faces. Customer journey analytics can leverage information from existing systems like natural language processors, text and speech analytics tools and customer feedback management systems to help agents address customer needs.

Lastly, having insight into analytics and the customer journey helps contact center professionals monitor contact center metrics and quantify how those metrics impact key business objectives like cost to serve, NPS and more.

Customer Journey Analytics in Action: Decrease Support Call Volume and Spend

A top retail bank wants to decrease support call volume and enhance operational efficiency. Tasked with finding the solution, the bank’s customer service team uses customer journey analytics to discover which journeys drive new clients to make a support call.

The team relies on their customer journey dashboards and sees that call volumes in pay journeys are rising. Digging deeper, they notice that 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 IVR channels. By discovering the root cause of the support calls and comparing the data across multiple touchpoints and systems, the CX team can now focus on finding and addressing issues in the IVR system.


bank clients attempting to pay bill


The bank estimates that each call costs approximately $6 per agent-assisted contact. By decreasing call volume for clients making their first payment by 50%, the bank will save an estimated $3 million. Those are just a few examples of how customer journey analytics offers value for the business.

How Customer Journey Analytics Compares to Alternative Approaches

Today, the number of available customer analytics tools is overwhelming. The landscape includes options that promise a great deal but often underdeliver.

Many platforms on the market address one or two of your most important customer experience needs but fail to address them in a comprehensive way.

Let’s examine how some of the most popular approaches and solutions enterprises use to analyze and engage customers compare to customer journey analytics.

Marketing Campaign Management

Campaign management platforms are foundational tools that enable marketers to manage hundreds or thousands of cross-channel campaigns in one place. Marketing teams use these tools to automate certain actions based on customer behavior within web, email and paid channels, as well as track their performance.

The purpose of campaign management is to improve marketing effectiveness. Therefore, these systems are more focused on the marketer’s needs than measuring and optimizing your customer’s experience.

Campaign management platforms can’t make a direct connection between campaign performance and business outcomes such as revenue, cost and churn. To quantify campaign influence, they require the use of black box models that use statistical correlation to determine causation.

  • Manages campaign planning, tracking and execution in one platform
  • Automates actions based on predefined segments and behaviors
  • Only captures behavior in a limited number of marketing-owned channels
  • Measures progress toward marketer goals, rather than customer goals
  • Challenging to analyze behavior of anonymous and known users
Customer Data Platforms

Customer data platforms (CDPs) have grown in popularity in recent years, primarily due to the need to aggregate and combine customer data from a variety of separate data systems. CDPs promise to solve this challenge by centralizing data collection, unifying customer profiles from disparate sources and creating and managing segments.

Some CDPs have analytical capabilities and others can be used to drive engagement across a variety of channels. Most CDPs serve as a place to retrieve unified customer records. They can neither store time-based events, nor analyze and act on complex customer behaviors.

  • Unifies first-party, individual-level customer data from multiple sources to create “Golden Records”
  • Consolidates profiles at the person level and connects attributes to identities
  • Users can create and manage segments
  • Does not require the level of technical skill of a typical data warehouse project
  • Data can be used by other systems for analysis and to manage customer interactions
  • Difficult to incorporate unstructured data
  • Many have immature analytics capabilities
  • Most don’t support time series analyses (e.g., how many people did Y before Z but after X?)
  • Can’t store or analyze behavior of anonymous customers
  • Rapidly changing and consolidating market of more than 80 vendors
Process Mining

Chief information, digital and financial officers, as well as ​​operations analysts, use process mining to optimize business workflows. These tools help organizations measure processes from start to finish and identify inefficiencies that must be improved. As the name suggests, process mining is focused on internal processes rather than customer experiences.

  • Documents and improves the efficiency of business workflows
  • Analyzes event logs within enterprise transaction systems
  • Does not capture customer behavior across all channels
  • Actual customer journeys are not pre-planned business sequences
  • Many platforms are not built to be usable or accessible across all business units or functions
Journey Orchestration Platforms

A journey orchestration platform is ideally powered by connected customer journey data. This enables you to improve journey outcomes by orchestrating actions that are relevant to the context of each customer’s goals and their overall experience.

Only when powered by journey data and effective journey measurement, is it possible to deliver consistent experiences that reflect each customer’s unique context. As a result, journey orchestration solutions are far more effective when combined with powerful customer journey analytics software. Unfortunately, most orchestration-focused solutions simply count predefined sequences of steps and try to pass off that simple capability as “analytics.”

  • Automates actions across multiple channels
  • Identifies key trends and behaviors to determine next best action
  • Creates and captures the results from A/B tests
  • Many solutions can’t ingest the 12-plus months of historical data needed to truly understand each customer’s experience
  • Analytics may need to be performed outside the platform by services teams using Python or other programming languages
  • Many use a centralized, rules-based approach that is better suited for centralized, small and medium size businesses than larger enterprises with distributed teams

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. Learn more about what experience orchestration can do for your business.