Key components of customer experience analytics

Customer experience is a relatively new discipline, but it’s gained a lot of traction over the last several years. But despite its popularity, it’s often more of an aspirational ideal and rallying cry in many companies, as opposed to something with a concrete tactic behind it. 

To back up the passion for creating a great customer experience, you have to take a strategic, data-driven approach. When you do this, you’re able to use  CX as a real tool for improving customer satisfaction and retention.

We’ve already talked before about the best metrics to include on an actionable CX dashboard, but let’s take a step back a little further and talk about the broader components that should be a part of your customer experience analytics strategy. 

Customer journey mapping

Since the customer experience takes into account every single touchpoint that your users have with your brand, customer journey mapping is one of the most valuable processes to include in customer experience analytics. 

Customer journey mapping helps you get both a broad and narrow view of your customers. While it gives you a birds eye view of all of the different interactions that a customer has with your brand - from initial contact to post-purchase - it also helps you focus on key points in the journey. 

During a customer journey mapping exercise, you can highlight any key touchpoints where customers interact with your product, helping you anticipate where extra support may be needed and identify areas for improvement. 

For example, an email marketing platform may identify the following moments as key touchpoints: Requesting a demo, account creation, setting up their first campaign, using more advanced features, seeking customer support, and renewing their subscription.

Typically, you’ll create different journey maps for different personas. Certain types of users may come to and use your platform very differently, and customer journey mapping helps you acknowledge and account for this. 

These journey maps you create will be foundational to basically everything else that we’re going to cover in this piece. Once you have solid (but still flexible) journey maps, you can leverage your other analytics to add context and help you create a plan to create better customer experience. 

Key actions:

• Identify all touchpoints in the customer journey
• Use tools like Lucidchart or Miro to visualize these journey maps
• Collect data at each touchpoint to understand customer interactions and pain points

Collecting and analyzing customer feedback

When your customers tell you what you’re doing right and wrong, you should listen. This is why the way you collect and act on customer feedback is one of the most necessary components of successful customer experience analytics. 

To continuously craft the best customer experience, you want to always be collecting direct feedback from customers to identify any new or expanded needs, preferences, and areas where your product may be falling short. 

This is where the traditional CX metrics come into play. Things like CSAT, NPS, and customer effort score are important as more of a temperature check to see how you’re doing, but they’re limited in their ability to diagnose what’s wrong (or right) with the experience. So you’ll want to pair these with additional questions that help you dig into more specific details about how they’re experiencing your product. 

You can use traditional survey tools like Typeform to do this, or you could use in-product surveys that allow you to collect more contextual information, i.e. asking them what they thought of a new product right after they used it for the first time. 

Use AI to dig deeper

Thanks to the explosion and evolution of conversational AI, you can get even more sophisticated with the way that you collect feedback. 

People are a lot more likely to provide meaningful feedback to a human than they are to a status survey. And while your customer success team may not always have the bandwidth to draw this feedback out of your customers, AI is the next best thing. Using AI-powered agents like our Copilot that create a very natural, human-feeling conversational experience, you can bring in more and more meaningful feedback more easily. 

Use sentiment analysis for implicit feedback

Not all feedback needs to be explicitly given. In fact, leveraging implicit feedback can help you identify trends that you may never otherwise catch. 

Sentiment analysis helps with this. It uses natural language processing to gauge customer emotions in the feedback you receive, but also in reviews, social media, and support messages. This helps you to better understand the overall sentiment towards your product. 

If you find areas that have an overwhelmingly (or growing) negative sentiment, it can help guide your strategy going forward. You could use this insight to make product improvements, provide better support, or make changes to your communication processes. 

Key actions:

• Use survey tools and conversational AI to collect a variety of feedback
• Analyze responses to identify recurring themes and areas for improvement
• Implement changes based on feedback and communicate these improvements to customers
• Feed all data you have with your customers’ real words into sentiment analysis tools to identify drivers of both positive and negative feelings

Leveraging customer support data

Your customer support data goes hand-in-hand with the feedback component of customer experience analytics. You want to train and encourage your customer support team to actively collect, organize, and interpret the large amounts of data they’re constantly getting from their customer interactions. 

Of course, conversational feedback is a large part of this, and similar strategies to those mentioned in the section above can be used to analyze that feedback, but customer support has access to much more concrete data, too. 

You can leverage metrics like first response time, resolution time, and ticket volume to track problem areas at certain points in the process. This data helps you to quantify these problems, which makes it much easier to prioritize improvements to make going forward. 

Key actions:

• Use support tools like Zendesk or Freshdesk to track and analyze support interactions
• Identify common issues and pain points from support data
• Implement training or product changes to address these issues and improve support quality

Monitoring customer behavior + usage patterns

How your users engage with your product is a massive indicator of the quality of the customer experience you’re providing. More engagement points to your strengths, while low engagement may point to areas that you need to improve.

Tracking product usage is an extremely valuable (and typically under-tapped) part of customer experience analytics. It helps you identify how customers are interacting with your platform as a whole, as well as specific features. This data helps you understand which features are most valuable and which need improvement. 

Metrics like time in the platform and daily/weekly/monthly active users, in addition to stats around which features are being used the most are highly valuable here. But you can also track their engagement with user assistance tools as an indicator of their investment in your product. 

For example, you can see how they’re engaging in the onboarding process, the rate at which people complete product tours, and the interest in/engagement with in-product announcements. 

This engagement piece is one of the most valuable aspects of customer experience analytics because it shows a tangible indicator of not only what people think of your product, but how those feelings translate into real action. 

Key actions:

• Use tools like Google Analytics and Commandbar to track in-product engagement
• Identify high-value features and underutilized ones
• Prioritize development efforts based on usage data to enhance user experience

Customer experience analytics should go broad and narrow

The idea of the customer experience encapsulates so many things. Anything from the emotions surrounding your product to actual engagement plays an important role in monitoring and perfecting the experience you’re providing, so your analytics strategy should pull in data on all of the data that you can from your customers.