Did you know that 89% of successful businesses say that anticipating what customers need at every stage of their journey is critical to growth?

That’s just one of the many statistics from Google’s “An Audience of Individuals” report that underline the importance of customer journey analytics.

In this blog, you’ll learn exactly what CJA is, with real-world customer journey analytics examples. It includes a roadmap for improving customer satisfaction across touchpoints, and also lists three of the best customer journey mapping platforms on the market today.
 

Customer journey analytics (CJA) is the practice of examining how customers engage with a business across all touchpoints.

Otherwise known as customer journey data analytics, it includes both online and offline milestones.

This distinguishes it from digital experience analytics, which is limited to interactions in the online world. 

Additionally, customer journey analytics takes a holistic view of the entire customer journey across various channels - a key distinction from user journey analytics, which focuses specifically on user behavior within digital platforms.

If you want, try thinking of CJA as a "customer detective kit" - a Sherlock URL-mes who investigates the full scope of a customer’s journey and finds subtle clues that lead to better decisions and targeted improvements.

Some notable customer journey analytics use cases include:

→ identifying key drop-off points in the purchasing process,
→ optimizing the onboarding experience for new users,
→ improving customer support interactions,
→ and tailoring marketing campaigns based on user behavior and preferences.

For an ecommerce business, the online CJA touchpoints would cover:

• Discovery: How internet users discover the webstore, such as through Google Search, social media, or paid ads.
• Browsing: The ways customers navigate the website, including product searches, filtering options, and content engagement.
• Purchase Process: The entire checkout experience, encompassing cart additions, payment methods, and confirmation notifications.
• Post-Purchase Engagement: Follow-up emails, customer feedback requests, and personalized recommendations for future purchases.

However, eCommerce has one foot in the physical world.

As such, customer journey analytics would also include the following offline touchpoints:

• Shipping Process: The experience of receiving the product, including shipping notifications, delivery tracking, and the condition of the package upon arrival.
• Customer Service Interactions: Any offline interactions customers have with support teams, whether through phone calls or in-store visits for returns and inquiries.
• In-Store Experience: For businesses with physical locations, this includes how customers interact with products, staff, and the overall atmosphere in-store.
• Returns and Exchanges: The process customers go through to return or exchange products, which can greatly impact their overall satisfaction and likelihood of future purchases.
• Import Taxes and Duties: The clarity and transparency regarding any import taxes or duties that customers may incur upon receiving their products, which can affect their overall purchasing experience and satisfaction.

Clearly, there are many milestones in the customer journey that all need to be refined for a smoother experience.

By tracking each interaction, marketers can uncover patterns in customer behavior and gain a deeper understanding of their experiences.

This insight helps to optimize business outcomes by refining marketing, customer service, and product strategies.
 

Let’s look at a real customer journey analytics example from the world of retail.

Imagine an eCommerce business that is suffering from a low NET Promoter Score and an unacceptable number of returns despite selling a popular range of Nike trainers.

This is overwhelming their customer support team and driving up costs.

Here’s how customer journey analytics would play out:

The Customer Journey

First off, the business worked to map the the return journey, which typically looks like this:

1. The customer initiates a refund from their Returns webpage.
2. They print a return label and drop the package at a Evri pickup point.
3. The company receives the package, processes it, and issues a refund.

The Pain Points

By tracking eCommerce KPIs and using customer journey analytics, the business identified issues affecting satisfaction:

• Some customers struggle to find the return instructions, leading to confusion.
• This often results in calls to customer service, making the process frustrating.
• Regular shipping delays mean customers wait longer than expected for refunds.

The Conclusion

The business realized that unclear return instructions and delayed refunds were causing frustration, leading to a low NPS score and a high volume of messages to their customer support team.

The Action

To resolve this issue, the business redesigned their Returns webpage to make it easier to understand the returns process and print the shipping label.

→ Customers can now easily generate return labels, select preferred shipping options, and receive instant notifications when the return is processed.

They also tell customers that the refund process takes ten days.

→ But since the business in reality completes the refund within five days, customers receive their money faster than expected - a useful trick for increasing satisfaction. 

The business also decided to move away from Evri and handle shipping in house to remove an unreliable third-party from the returns process.

The Result

After these changes, the company noticed an immediate decrease in the number of customers contacting their support team.

They even started receiving messages from customers delighted with the speed with which they received their refunds - despite the fact that nothing had actually changed.

The business also continued monitoring their NPS score, and found that it started increasing almost immediately after they implemented changes.
 

Customer journey analytics can be facilitated with tools that include customer journey mapping, behavior analytics, and feedback acquisition. 

Here are seven customer journey analytics examples that cover all the different stages involved:

• Conversion Funnels
  Analyzing conversion funnels helps businesses pinpoint where customers drop off in their online journey, allowing for targeted optimizations to improve conversion rates and overall sales performance.
• Session Recordings
  Session recording tools capture real-time user interactions on a website, enabling businesses to observe common user pathways and identify potential issues or friction points in the customer journey.
• Heatmaps
  Heatmap features visualize where users click, scroll, and spend time on a website. Understanding user behavior in this way helps businesses adjust their layouts and content to better meet customer needs.
• Customer Feedback Platforms
  Survey tools and others feedback platforms gather direct input from customers about their experiences, providing valuable insights into satisfaction levels and areas for improvement across the business.
• Social Listening Tools
  These tools monitor social media platforms for customer sentiment and feedback. By analyzing conversations about the brand, businesses can better understand public perception and adjust their strategies accordingly.
• Outgoing Traffic Analytics
  This feature provides insights into where customers go after leaving a website, allowing businesses to understand customer behavior beyond their own digital properties.
• Customer Relationship Management (CRM) Systems
  CRM systems are crucial for tracking customer interactions across various channels, managing relationships, and analyzing data to enhance customer experiences throughout the journey.

By utilizing these customer journey analytics tools, companies can gain a comprehensive understanding of their customer journeys, leading to more informed decisions and enhanced user experiences.