The Complete Guide to Customer Journey Analytics

In today’s advanced digital age, it can be increasingly difficult to understand customer behavior. There are so many different channels to navigate, with long and winding customer experiences that include both online and offline interactions.

However, understanding your customer experience with a customer journey analysis is critical to identifying potential bottlenecks and making improvements that will help you retain more customers and drive better business outcomes.

An omnichannel solution can help you determine which channels and touchpoints are thriving and what areas may need more attention from a customer experience (CX) standpoint.

What Is Customer Journey Analytics?

Customer journey analytics is the process of understanding, analyzing, and optimizing every customer interaction across all channels.

This analysis considers data like customer insights, online and offline attribution, and direct customer feedback from a variety of sources. It plays a key role in identifying significant drivers of customer behavior — both as a whole and across different customer segments — and overall satisfaction.

Customer journey analytics provides invaluable data about how customers are interacting with your business across all touchpoints, making this information vital to improving overall CX.

Tools and methods

There are a number of different customer journey mapping tools, which include:

• Heatmaps
• AI-driven sentiment analysis
• Predictive modeling
• Conversation analytics

Many customer journey tools integrate data from a variety of individual tools and may include data sources like CRM systems, social media insights, support tickets, and customer feedback. This information can give the analytics platforms vital data about customer segments, value, and behavior.

Key metrics analyzed

To better understand and optimize your customer journey, you need to assess a variety of different metrics. These include essential customer engagement metrics like the following:

• Customer satisfaction score: Leverages customer feedback to determine how happy customers were with a specific interaction. The higher the score, the better.
• Customer effort score (CES): Determines how much work it took for customers to resolve an issue. The lower the score, the better.
• Net Promoter Score: Assesses how likely a customer is to refer someone else to your business, based on customer surveys. The higher the score, the better.
• Sentiment analysis of customer conversations: Helps you understand how customers feel about everything from a support interaction to the actual products or services.

Finally, the customer journey analysis will assess the following:

• Conversation rates from different channels
• Churn patterns
• Offline attribution
• User interactions with specific touchpoints, including which pages they visit or paths they take through your site
• Direct customer feedback

Benefits of Customer Journey Analytics

Conducting a customer journey analysis offers extensive benefits. By understanding your customer journey, you can significantly improve the overall experience to ensure that you retain customers and leverage CX as a competitive differentiator.

Improved customer understanding

By reviewing the entire customer journey, you can gain deep insights into customer preferences, behaviors, and pain points. You’re eliminating the guesswork and getting down to what’s really happening, including why customers are or are not progressing through the digital sales funnel.

For example, you can use heatmaps to visualize website drop-off points. Your team can then investigate the issue closely:

• Is the copy not compelling enough to get users to take action?
• Is there too little information for them to move forward?
• Is the pricing too high because you’re attracting the wrong customer segment?
• Is there a technical issue preventing users from submitting a form?

This information can result in clear initiatives that you can take to improve or modify your CX.

Enhanced personalization

As you learn more about individual customer journeys, this opens the door for advanced personalization.

You can thus tailor interactions with individual customers based on their purchase and support history, accounting for their predicted needs. Advanced tools that leverage AI and machine learning are particularly helpful for this purpose.

E-commerce platforms, for example, may use a customer’s past history to recommend products based on items they’ve viewed or categories they’ve browsed in the past. These efforts can help customers find more products they’re interested in and are often capable of increasing average order value.

Reduced customer churn

Using analytics to improve customer retention is one of the most appealing use cases for many businesses and is a critical component of customer experience management.

A thorough analysis of your entire customer journey can be essential to preventing churn. By proactively identifying potential dissatisfaction trends, you can address issues early and before they cost you significant customers.

Airlines often use sentiment analysis to gauge customer feedback post-flight. They’ll review this feedback closely, looking for trends in the data. If passengers were to frequently complain about specific baggage fees that are too high, for example, the airline may make changes in their pricing.

Unified business operations

Understanding the state of your customer journey holistically can be key to unifying all business operations, breaking down the silos between marketing, sales, and customer service teams. The reality is that most of these teams end up working in a disjointed way even though they’re all working toward the same ultimate goal.

By getting all three teams on the same page, however, you can align strategies using a shared analytics dashboard. Your marketing team, for example, may need to adjust which customer segments they’re prioritizing, and your sales team may need to adapt their promises based on feedback from the customer service team.

Real-time feedback

Use real-time feedback to get immediate insights, which can be utilized to address customer complaints more effectively, optimize campaigns, and improve the customer experience. This results in better decision-making and happier customers.

Retailers may use customer journey data, for example, to adjust marketing campaigns midway through the process based on response data. If customers complain that a deal isn’t good enough, retailers may modify it. And if conversion rates are particularly low, they’ll use analytics tools to assess where and why people are dropping off to make changes on the fly.

How to Implement Customer Journey Analytics

While tracking a customer’s journey can be exceptionally difficult to do manually, specialized customer journey analytics tools are incredibly helpful when it comes to collecting, analyzing, and acting on the data in question.

Step 1: Map customer journeys

Start with a comprehensive journey map highlighting all touchpoints.

Pay particularly close attention to critical moments, including:

• Lead generation processes
• Customer onboarding
• Post-purchase engagement
• Post-support feedback

Consider what tools you may need to accurately track attribution across different customer segments, including both your offline and digital customer journey.

Step 2: Integrate data

Aggregate insights from your customer data platforms, including from your CRM, support logs, social media, email platforms, mobile apps, and customer surveys.

Consider all digital and offline touchpoints, sourcing data from all of your existing tools that integrate with your customer journey mapping. Offline attribution is often tracked manually, for example, but can still be uploaded as vital data.

Use a customer journey mapping tool to unify both structured and unstructured data into a single platform.

Step 3: Analyze and identify patterns

Leverage machine learning and AI tools for trend analysis, sentiment extraction, and predictive insights.

In many cases, customer journey data is extensive and almost exhaustive. It can be difficult for individuals to comb through for a thorough analysis, but AI tools are highly effective at spotting trends that a manual review may have missed.

For example, your AI-powered data analytics tool may detect recurring service complaints from call center logs, even if the callers use slightly different language to describe the same problem.

Step 4: Take informed actions

Implement changes based on the insights you’ve discovered. For example, you may optimize website navigation to simplify the customer journey or improve agent training to lower your CES.

Find ways to meet and even exceed customer expectations at all touchpoints based on the data analysis.

Hospitality brands, for example, offer discounts to dissatisfied customers flagged by analytics. Combined with outreach initiatives, this may help attract guests back for a return visit and salvage the overall relationship.

Step 5: Iterate and optimize

Continually monitor engagement, conversion, and customer sentiment metrics that your team has chosen as key performance indicators. Doing this will help you identify potential problems early so you can adjust strategies and meet evolving customer needs.

Seasonal businesses, for example, may adjust their workflows based on peak demand insights. This ensures not only that customers receive timely service but also that the business isn’t spending too much on support in slower seasons.

Customer Journey Analytics vs. Journey Mapping

When we talk about customer journey tracking, two types of tools are typically mentioned: customer journey analytics tools and customer journey mapping tools.

While sometimes people discuss the two interchangeably — and they do have complementary features — they ultimately have a few key differences to note.

Key differences

Customer journey mapping is static. It gives you a clear look at how customers interact with your business across different stages of their life cycle. With the right tools, you can track their interaction through your website, email campaigns, social media, mobile app, and even in store.

Customer journey analytics, however, is dynamic and data-driven. It assesses real-time behavior and explores outcomes and user behaviors, making it essential for customer journey orchestration.

Complementary nature

While they are different, customer journey mapping and analytics platforms can be highly effective when used together.

Mapping provides a clear foundation of knowledge that can be used to understand your baseline CX. Analytics, meanwhile, can be used as an optimizer. You can make initiatives to act on the data and monitor its impact in real time.

You can also use analytics to confirm or update assumptions drawn from a journey map. Doing this before you make changes — and then tracking the impact on CX — can be critical to customer journey optimization.

Nextiva: For All Your Mapping & Analytics Needs

Nextiva’s unified CX solution allows businesses to create a foundational journey map and enhance it with dynamic, actionable insights. These insights can help your team make more accurate data-driven decisions, resulting in better outcomes across the entire customer life cycle.

Our advanced analytics solution offers the following essential features:

• Comprehensive data integration: Consolidate customer interactions across voice, email, chat, and social media into a single, easy-to-navigate platform.
• AI-powered and real-time insights: Leverage AI to predict customer behavior, identify friction points, and respond instantly with informed decisions.
• Scalable and omnichannel excellence: Ensure seamless customer experiences across all communication channels, tailored to businesses of any size.

We’ll help your team use proactive workflows aided by AI to take advantage of customer journey insights. Unify all of your customer experience data in one place for exceptional journey orchestration.

Learn more about Nextiva’s customer journey platform today.