Customer experience analytics is the process of collecting and analyzing data about how users experience your website and product to better understand their pain points and their perspectives. The understanding gained from CX analytics can then be used to make improvements to the product or other elements of the customer journey.
For example, a company might conduct a survey of past customers and then analyze the responses to better understand their customer experience.
This kind of CX analytics can highlight all sorts of opportunities. Sentiment analysis might indicate that a shift in the tone of marketing messaging could offer more appeal to customers. Customer stories may highlight pain points that the product hasn’t fully addressed.
But that’s just the tip of the iceberg when it comes to gleaning customer insight from customer service analytics!
What Are the Four Key Elements of the Customer Experience Model?
To better understand customer experience analytics, let’s take a look at some examples of CX metrics and advanced analytics across four key touchpoints in the customer journey.
Throughout the entire customer journey, the goal is to collect and analyze all available data (both structured and unstructured data) to mine it for insights into customer behavior.
This will require data analytics skills or tools and a contact center or customer service team that’s excited about digging into customer engagement and customer satisfaction.
Element 1: Engage Potential Customers
The first question for CX analytics to answer is how effectively you’re engaging the potential customers who land on your site.
Analysis of web traffic using a web analytics tool like Google Analytics is an obvious place to start. Which pages attract the most traffic? Which pages convert the best? Which pages frequently cause visitors to bounce?
Marketing data analytics for all of your paid campaigns will also be important here. Which ads are sending users who convert? Which content placements are generating qualified leads?
However, customer experience analytics at this stage will be more effective when it digs deeper. Mouse-tracking heatmap tools can help you identify the most and least effective parts of every page on your site.
Visitor surveys can help you collect additional data and offer actionable insights by highlighting customer pain points you may not be aware of.
Element 2: Convert Qualified Leads
The next step in the experience model is customer acquisition. Here, the aim of CX analytics is to measure how effectively your site is converting.
To understand conversion, you’ll want data analytics to look at a variety of touchpoints across the customer journey. Do specific types of customers convert at higher rates than others? What messages are most appealing?
Web analytics and user surveys can be helpful here, too. Collecting insights from customer service or the CX team about customer experiences and complaints is also important.
Additionally, because conversion is a binary metric (visitors either convert or they don’t) conversion offers numerous opportunities for impactful A/B testing.
Messaging, colors, page design, product photos, and much more are all elements of the customer experience. You can A/B test changes to these and many other elements to see how they impact conversion.
One important tip for this stage: don’t ignore the users who don’t convert. Although they can be more difficult to contact, data from users who bounce is an important customer touchpoint, and it can highlight problems with your customer journey that you might not be aware of.
Element 3: Meet or Exceed Customer Expectations
Customer experience analytics doesn’t end after a customer has been converted!
At this stage, you want to dig into the data to understand the customers’ experience with your product and with your customer service contact center so that you can better understand customer satisfaction.
A common baseline metric here is the Net Promoter Score (NPS Score).
Surveying customers to get a post-purchase NPS score provides a quick data point about their level of customer satisfaction, and it’s often an accurate predictor of customer retention and customer lifetime value.
If, for example, a SaaS business is converting visitors at a high rate, but continually seeing low NPS scores, they are likely to see a high degree of customer churn.
An NPS score is just a number, though. It’s best to have your customer service or CX team check-in across a number of different customer touchpoints, collecting both structured and unstructured data and using data analytics to dig into factors such as:
- What customers expected when purchasing your product
- How customers are using your product
- What customers contact customer service to ask
- What customers are saying about your product
These are questions that you may be able to analyze quantitatively by looking at product usage metrics, analyzing social media sentiment for mentions of your company, etc. But you can also collect data by conducting surveys, interviewing customers, and more.
Element 4: Nurture Repeat Customers
It’s far cheaper for any business to retain an existing customer than to bring in a new one, and customer experience analytics can play an important role in increasing your customer retention.
Customer service is a critical touchpoint here. Even if customers are reporting high NPS scores, for example, they may end up dissatisfied at some point later in their journey. If you’re not collecting and analyzing the right customer data, you may never know.
Implementing a holistic customer health scoring system can help you catch those kinds of issues, but interviews with the customer service team and analysis of customer service metrics can also offer insights into how likely customers are to make repeat purchases.
Customer experience management is also a critical part of building customer loyalty and turning one-time customers into repeat buyers and promoters. Digging into your customer service data will help highlight areas where you may not be effectively managing the customer experience.
Predictive analytics can also be valuable at this step.
If you’ve collected enough data about customers across the previous three steps of the experience model, you should have the data required to build predictive models that can (for example) suggest other products your existing customers might be interested in.
Each element of the customer experience model reflects a step or several steps along the broader customer journey. When it comes to customer analytics, the customer journey is critical, so let’s take a closer look at it.
What is Customer Journey Analytics and How Does it Help with Customer Experience?
To truly understand all of this customer experience data, you need to understand the customer journey.
Customer journey analytics is closely connected with customer experience analytics. You can click here to read more about the terms customer journey vs. customer experience, but in a nutshell, a customer’s journey maps their customer experience.
Customer experience analytics ultimately falls under the umbrella of the more complete customer journey analytics.
In customer journey analytics, data points collected at touchpoints across the entire customer journey are analyzed to produce a holistic view of the customer journey.
Using Woopra, this journey can also be visualized using a customer journey map. If you take a look at some customer journey map examples, you’ll understand just how valuable customer journey analytics can be!
Let’s take a look at a few tips for how to do customer journey analytics and customer experience analytics more effectively to help you hit your KPIs.
How to Use Customer Journey Analytics to Improve the Customer Experience
Tip 1: Collect Data at Critical Touchpoints
User journeys can be long, and every one is a little different. To get an accurate big-picture view of your customer journey, you need to ensure that you’re collecting as much data as possible at all of your customer touchpoints.
For example, here are some touchpoints you’ll want to track as customers move from top-of-funnel potential users to long-term repeat customers. (This list is not all-encompassing, you probably have other touchpoints you can track, too!)
- Advertisement views and clicks
- Article views and clicks
- Organic searches
- Webpage views
- Chatbot and early sales or customer service interactions
- All contacts to your contact center
- Form submissions
- Survey responses
- Email opens and clicks
- Account creation
- Any and all in-app or in-product activity
- Training video views, documentation views
- Support tickets
- NPS survey responses
- Product use data (understanding product analytics is critical here)
- Upgrades and repeat purchases
- Reviews and other customer feedback
- Social media posts about the product or company
- Customer survey or interview responses
As you can see, there’s quite a lot of data to collect. Chances are that your customer journey has additional touchpoints not listed above.
Collecting all of this data isn’t easy, but it will be worth it. Understanding the customer journey will produce a ton of actionable insight.
Tip 2: Identify Users and Unify User Actions
All of the data points you collect will be important for journey mapping. But tracking every customer interaction can quickly turn into a nightmare for your data team unless you follow some best practices.
First, identify users as early as possible in the funnel, and tag all of their subsequent actions with the same unique identifier. This will make following individual customers and mapping out their journeys much easier, and ensure that there are no disconnects.
Second, unify user actions so that every individual action includes the same basic data types, including;
- User ID: Who is the person who performed this action?
- Action Type: What did this user do?
- Timestamp: When was this action performed?
Where possible, it will be useful to collect other types of metadata too, such as device type, user location, browser version, etc. But at a minimum, ensuring that all data points include a User ID, action type, and timestamp will make customer journey analytics and journey mapping easier.
Tip 3: Use Efficient Analytics Systems
With each user generating such a huge amount of data, even companies with modest userbases may quickly discover that their existing approach to analytics isn’t optimal for customer journey mapping.
Most companies use some form of SQL-based database to track everything from marketing data to customer churn. But SQL isn’t designed for the massive datasets generated when pursuing customer journey analytics.
It’s possible to create a customized, purpose-built analytics system, but that typically requires a lot of time, expertise, and investment.
A faster, easier, and more affordable option is to make use of an existing journey analytics platform like Woopra, which is already purpose-built for tracking and mapping customer journeys.
Tip 4: Build Easy-to-Understand Visual Reports
Whatever CX analytics solution you choose, don’t forget the importance of visualizing the data after it has been collected and analyzed.
This is a lesson that is often forgotten in the era of big data: your data is only as valuable as it is understandable. You can collect a thousand data points for each customer, but they’re not worth anything unless people can see the insights they generate.
That means that your analytics solution should produce reports that are attractive, understandable, and visual.
Ideally, even someone unfamiliar with customer analytics should be able to look at a report and understand what the data says about how customers move through the customer journey.
One key visualization here is the customer journey map, which demonstrates how different customers flow through your customer journey, highlighting areas where different user cohorts break off, users bounce or churn, etc.
Looking at a map like this will highlight problems, reveal customer needs, and suggest improvements in a way that anyone can understand.
Establishing a robust customer experience analytics setup is quickly becoming essential to any business that wants to remain competitive.