How To Use Data Analytics For Customer Experience? (+ Uses)

analytics for customer experience

Most businesses work on their CX strategies with the goal of making a lasting impression on customers that ultimately boosts brand loyalty.

Since this is essentially an enterprise-wide quest, the time, effort and resources put into it can be quite significant. As a business owner, how do you plan to analyze the impact of such an effort-intensive undertaking?

This is precisely why we recommend data analytics for customer experience — measuring the performance of CX strategies by tracking important CX metrics.

In this post, we will take a deep dive into the application of data analytics for improving customer experience. Let’s get started.

Table of contents
What is customer experience analytics?
Why customer experience analytics is important?
Who can leverage CX analytics?
How to optimize CX measurement?
Metrics used to analyze customer journey experience
Customer experience analytics use cases

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What is customer experience analytics?

Before we define and discuss customer experience analytics, let’s first understand the term “customer experience,” shall we?

Customer experience refers to the journey a customer embarks on when interacting with a business and how it impacts the customer’s relationship with it.

It all starts from the moment the customer comes across or becomes aware of the business. The journey leads customers through a series of discoveries that help them make an informed purchasing decision.

As a business owner, you’d want to make the most of this customer journey from an analytics standpoint.

Customer experience analytics allows businesses to collect and analyze customer data by marking key customer touchpoints throughout the experience.

It is essentially the process of collecting and analyzing customer data with the purpose of learning more about the target audience.

Through CX analytics, brands get the opportunity to understand changing customer needs and come up with data-driven strategies to increase customer engagement, satisfaction, and loyalty.

Data analytics tools that track and analyze customer experience generate adequate insights and recommendations that internal teams across an organization can implement.

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Why customer experience analytics is important?

Here are some of the key reasons why we recommend you use customer experience analytics.

1. Helps make informed decisions

Companies relying on data-driven and objective decision-making find it easier to back up the changes they make to business operations.

Data analytics help such companies couple CX strategies with concrete metrics and make the most of the generated customer insights.

CX data allows brands to make moves based on changing customer expectations and engagement. Such a data-driven approach to customer experience improvement eventually leads to higher retention.

2. Eliminate CX roadblocks

Regardless of how hard you work towards improving your products, services, and buyer journeys, customers will always find issues and obstacles.

As a business owner, you should approach such obstacles as an opportunity to strengthen the existing state of customer experience.

CX analytics, especially real-time analysis, allows brands to discover such roadblocks and eliminate them. The CX metrics we will discuss in this post can help you determine the difficulties faced by prospects in real-time.

3. Track and analyze CX metrics

Tracking CX metrics is highly recommended. However, data proliferation isn’t the solution as it often leads to “data noise.”

Business owners should have an in-depth brainstorming session with the marketing and analytics team to come up with a list of CX metrics to accurately monitor customer experience.

More importantly, investing in a quality CX analytics tool makes it easier to seamlessly compile data from various sources and touchpoints and present the results conveniently in a dashboard.

Who can leverage CX analytics?

Indeed, the customer experience insights generated by data analytics tools should be implemented across the organization. However, the analysis should be leveraged by certain key decision-makers that can have a significant impact on the CX strategies.

High-level executives should have access to CX metrics such as CSAT and NPS to figure out the positive and negative aspects of the existing customer experience strategy.

The findings can later be implemented in the product development roadmap. Executives can make key strategic decisions using CX analytics tools.

C-suite executives, especially CMOs, should make the most of CX data as they know how important it is to extend top-notch customer experience to encourage repeat purchases. CMOs have the most idea of how CX impacts marketing and business KPIs.

They should use data analytics for customer experience to achieve design and facilitate a holistic and company-wide customer experience. With concrete CX data in hand, CMOs have an excellent opportunity to achieve this.

The customer service agents and representatives make up for the last mile of the customer experience journey. These are essentially the group of employees that land the CX strategies as they are in direct contact with customers.

It’s imperative to keep contact center agents in the loop throughout the customer experience data analysis. This is because customer service reps have to make on-the-spot decisions and recommendations.

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How to optimize CX measurement?

Now that you know CX data analysis from the inside out, it’s time we share steps you can implement to optimize CX measurement.

1. Develop customer-centric business goals

It all starts with preparing the enterprise-wide operations for the CX strategy improvement endeavors. High-level executives across the organization should put the CX design and marketing teams on the same page and set customer-centric business goals.

Putting the customer first will trigger a mindset shift that helps prioritize the customers’ needs and preferences. Once the customer goals are mapped, these should be aligned with CX metrics like CES, NPS, and CSAT.

2. Integrate omnichannel customer data

To ensure frictionless and connected customer experiences, the data analytics will have to crunch cross-channel data. This is because the customer experience data across channels are often siloed and stored in databases categorized by business functions.

This is a massive challenge that can be overcome to an extent through integrated omnichannel analytics.

The other way of handling omnichannel customer data is by building models that help businesses visualize and measure cross-channel customer experience.

3. Solicit quality feedback for measurement

The quality of customer feedback used for data analysis calls for a major improvement to generate high-quality insights and recommendations.

This can be done by prioritizing metrics that offer an overall outlook of the customer experience, such as metrics that reflect customers’ entire experience.

The best way to achieve that is by capturing customer feedback not after each interaction, but after specific customer goal completion. Not only does this reduce survey fatigue, but it also allows for improved feedback assessment.

4. Invest in real-time CX measurement

But what if prospects leave the customer journey without completing the set goals?

Abrupt abandonment will end up with nothing measured. This is why we suggest investing in the measurement of real-time metrics that allow businesses to predict goal completion.

Measuring customer experience in real-time opens the window for customer journey orchestration, for example business owners will know when customers encounter problems in their journeys.

With real-time CX measurement, you will be aware of the customer journey obstacles and ensure initiatives to improve customer satisfaction, trust, and loyalty.

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Metrics used to analyze customer journey experience

Which metrics should you track to improve customer experience data analysis? Here are the most important customer experience metrics you can monitor.

1. Customer Satisfaction Score (CSAT)

Customer Satisfaction Score (CSAT) is a straightforward customer experience metric that gauges how satisfied customers are with the product, service, or interaction with the brand.

CSAT surveys are triggered after key business-customer interaction, such as interaction with a customer support rep. Once the interaction is over, customers can rank the experience with the agent.

2. Customer Effort Score (CES)

Customer Effort Score (CES), as the name suggests, revolves around identifying how easily customers are able to find solutions to their problems.

After all, as a business, you’d want to create simpler interactions for customers so that they can easily move through the sales funnel.

For example, it should be very easy for customers to reach your service team. A CES survey helps solicit feedback regarding the convenience of service.

3. Net Promoter Score (NPS)

Net Promoter Score (NPS) is one of the most popular and widely used customer experience metrics. This is because it provides feedback about the overall customer experience.

With NPS, companies collect information about detractors, passives, and promoters.

Net Promoter Score is calculated by sending the NPS survey with the questions — “How likely are you to recommend our brand to others?” The feedback is collected on a 1-10 scale.

4. Customer Lifetime Value (CLV)

Customer Lifetime Value (CLV) is essentially a customer experience metric that allows a company to know how much business value customers have delivered during the relationship.

In other words, CLV refers to the customers’ worth to a company.

Moreover, CLV calculation factors in the resources the company is spending to keep the customers. Customer lifetime value is calculated by multiplying customer value with the average customer lifespan.

5. Customer Retention Rate

Retention is key to long-term business success.

Customer retention rate is a useful customer experience metric that helps companies figure out how many customers they have retained over a set period. Customer retention rate is directly connected to customer loyalty.

However, the customer retention rate is quite challenging to calculate. This is because the calculation involves the number of customers retained, lost, and onboarded.

6. Customer Churn Rate

Customer churn refers to customers leaving your company or not renewing their subscriptions. Businesses should keep track of customer churn rates by monitoring cancellations during a time period.

While customer churn rate is an important customer experience metric, you should dive deeper and figure out why churn is happening in the first place.

To calculate the customer churn rate, you’ll need to divide the total number of churned customers by the total number of acquired customers during a time period, and then multiply the result by a hundred.

Customer experience analytics use cases

Although customer experience analytics allows for several use cases, these are its most important applications.

1. To develop personalized customer experiences

Data analytics for customer experience finds a major application in developing personalized experiences across business operations.

The customer experience analytics tools make this possible by effectively segmenting the target audience and figuring out the respective expectations and needs.

In this way, analytics allows businesses to create more personalized customer experiences catering to different target audience segments. Moreover, data analytics reveals entirely new customer segments that can be targeted to boost ROI.

2. To increase the rate of repeated purchases

The long-term success of a business largely depends on how well it retains its customers.

In other words, the company should come up with customer experience strategies that encourage repeat purchases.

With customer experience-specific data analytics, companies can not only nail customer onboarding but also deploy strategies to increase customer lifetime value.

Customer experience analytics tools make a major use case for companies that find it challenging to close the loop with customers and elevate customer services and customer satisfaction scores.

3. To decrease customer churn rate

Customer churn is bad for business. What’s worse is businesses coming across the dissatisfaction only when customers leave.

Customer experience analytics finds an excellent use case for companies that fail to see trends that indicate the growth of unhappy customers.

With data analytics, businesses can quickly move from measurement to management, allowing them to use data-driven insights to discover trends in customer satisfaction, understand the pain points, and revamp CX strategies.

Final thoughts

Indeed, most businesses boast of their state-of-the-art products and services.

But, how many of them are able to communicate their exclusiveness through impeccable customer service? The overall customer experience often becomes the differentiating factor between ordinary and thriving brands.

Ultimately, your business works because it helps the customer solve their problems.

Creating customer-centric experience strategies fueled by real-time customer data can make it easier to dominate the market. We highly recommend you rely on CX analytics tools to improve customer retention and turn repeat customers into brand advocates.

Did you find this post helpful? Stay tuned as we share more updates related to data-driven customer experience in the upcoming posts.

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