CX Analytics & Insights For Beginners

Embarking on a new business tactic can be a risk. But with sentiment analysis of market-driven customer experience feedback data, this risk can be mitigated. Customer experience (CX) analytics, powered by artificial intelligence, is instrumental in giving you intelligent market insights that you can use to develop data-backed strategies for sustainable growth. They also empower you to keep evolving in an ever-changing business landscape.

CX analytics lets you tap into your customers’ minds through machine learning (ML) algorithms that extract rich insights from customer opinions in social media comments, videos, surveys, customer tickets, and other sources. These insights are much more detailed and advanced, going beyond CX metrics like net promoter score and CSAT scorecards.

In this article, I try to give businesses an overview of what CX analytics and insights entail, so they can confidently use machine learning for strategic business growth.

Table of Contents

What is customer experience analytics?
Why is customer experience important?
Commonly used CX metrics
Data sources
Machine Learning & CX analytics

What Is Customer Experience Analytics?

Customer experience is the overarching experience of a person in their interaction with your brand throughout their customer journey. CX analytics is the computational analysis of this experience - one that can be found in customer feedback data.

This feedback can be in any form, such as reviews, social media comments, videos, surveys, chatbot texts, and such. Machine learning platforms use several ML tasks such as natural language processing (NLP), text analytics, and sentiment analysis to discover and extract reliable insights from this data. Analyzing customer sentiment is instrumental in better understanding customer experience. It helps a business flourish and grow because it empowers you to engage with customers at a deeper level. It helps you understand their perspectives and fulfill any lacunas in their brand experience with you. This is especially important to build brand loyalty - the importance of which many businesses realized during the covid19 pandemic.

This principle remains the same whatever the requirement - whether you are a SAAS company wanting to boost business or a retailer who aims to enter a new market.

The Difference Between CX Analytics and CX Insights

Often, CX analytics and CX insights are used interchangeably. But there is a marked difference between the two.

While CX analytics is the mathematical processing of customer feedback data by an AI algorithm, customer experience (CX) insights are the findings that are received and inferred from the results provided by a CX analytics tool through its visualization dashboard.

Through computations that show details like aspect co-occurrence (trends where aspects of a business are mentioned together often by customers), sentiment trends, aspect-emotion co-occurrence (frequent instances where certain aspects like price or service are mentioned often with emotions like happy or satisfied), and such, CX analytics offers you CX insights that can help you improve your business. Thus, in this way, CX analytics and insights, together, provide you valuable information from customer feedback that go beyond numerical scores such as star ratings or NPS.

Why Is Customer Experience Important?

Analyzing customer experience helps you identify customer pain points and understand their needs. This is essential to grow any business because a great customer experience results in better customer satisfaction, better sales conversions, and building customer loyalty. It allows you to-

Better customer satisfaction. A happy customer is one who is happy with you right from the time they begin the process of researching you, to the time they actually buy and use your product or service, and reach out to you to resolve an issue after the purchase. That is why ensuring that there is communication at every stage of the sales journey is very important for understanding customer experience. As is collecting feedback even if the sale did not make it through.
Better sales conversions. When a person has a good customer experience with a brand at the consideration stage of their journey and feels that their questions and doubts are answered in a trustworthy and timely manner, it will lead to better sales conversions. Often, it is not always the price of the product or service, but rather the overall positive feeling that a customer gets from his experience with a brand that helps in making the sale.
Build customer loyalty. A good relationship with your customer base is of utmost importance in order to build trust because trust goes a long way in helping a business stay afloat in difficult times and flourish during the good ones. Trust comes with ensuring that you are always available when a customer needs you, that you have what they need, and are there to resolve any issues if they may crop up.
Increase in revenue. Investing in building a superior customer experience is a very good idea because it ultimately results in increased revenues through better sales, better leads, referrals, and return customers. In fact, studies show that companies with better CX grow around 8% faster than others.
Stay ahead of the competition. At a time when customers are spoiled for choice and bombarded with social media marketing-driven digital advertising, it is easy to lose them to a competitor. But if your target audience resonates with you and has a memorable experience with your brand, it helps you stay ahead of the competition and maintain your customer base.

Commonly Used Customer Experience Metrics

Companies commonly use four types of metrics or scorecards for understanding customer experience. These are CSAT (customer satisfaction score), also called Star ratings, CES (customer effort score), NPS (net promoter score), and TTR ( time to resolution). These CX metrics are used to judge the key performance indicators of a brand.

CSAT/Star Ratings CSAT scores or star ratings ask customers to rate their customer experience on a scale of excellent, very good, good, average, and poor. Reviews such as Google Reviews or Amazon reviews are a great example of how brands ask customers for CSAT ratings.
Customer Effort Score The customer effort score (CES) is a measure of how easy or difficult it was for a customer to navigate their way through the customer journey including after-sales and getting an issue resolved.
Time To Resolution (TTR) This scorecard is a measure of how long it took for a customer to get an issue resolved. Long response times to solve an issue in the purchase cycle or for information lead to higher frustration levels among customers. A low TTR is generally indicative of a good customer experience.
Net Promoter Score The Net Promoter Score (NPS) is a measure of whether a customer is likely to recommend you to someone else. The higher the positive customer experience, the higher the likelihood of a customers’ willingness to promote the brand.

Limitations of Using CX Metrics

Quantitative metrics of customer satisfaction only give a one-sided view of understanding customer experience. This is because these metrics, in no way, indicate or give the reasons as to why a customer gave a certain rating. In order to truly understand a customer’s sentiment about their experience with your brand, you need to go beyond numerical statistics.

Sentiment analysis allows you to analyze and extract important information from customer experience data such as open-ended questions in surveys, customer reviews on product and e-commerce websites, social media comments and videos, blogs, news, and others. These sources are treasure troves of customer experience details and opinions, which you can harness to develop your CX strategy.

Customer Experience Data Sources For Sentiment Analysis

A quick look at the main sources from where you can gather customer experience data to analyze sentiment for CX Insights.

Social Media. An ML platform can analyze social media comments and videos to give you all the insights you need. For example, you can conduct a TikTok video analysis and extract all the topics, themes, and even brands in the content for a more astute advertising campaign. Similarly, through social video analysis, you can can dig into comments and videos on Facebook, Instagram, YouTube, Douyin, or any other social media platform for brand amplification.

Reviews. Google reviews, Amazon reviews, and reviews found on websites like TrustPilot, Yelp, Clutch, etc, are all excellent data sources from which you can gather customer experience data for analysis.
Surveys. Surveys are very helpful in understanding customer experience because they can be done via emails, messages, or even on the spot during a checkout. An AI-based model can analyze survey data through semantic clustering and weed out redundancies and extract insights that are hidden in verbose answers that customers quite willingly give in response to open-ended questions.
News. Online news and magazines carry information that can give you vital data about your brand’s performance and its perception by customers. Comments on news websites too are collected when the CX platform analyses the data from the particular URL.
Blogs. Review blogs that are written by influencers or even by the general public can be analyzed for customer sentiment and used to generate CX insights.

There are several other sources for harnessing CX insights as well. These include customer support tickets, customer emails, data from your own CRM software, and other internal sources.

Machine Learning And CX Analytics

A CX analytics platform uses machine learning algorithms to analyze customer experience and feedback data from a variety of different sources and data types. It processes the data using ML tasks such as NLP, knowledge graphs, named entity recognition (NER), semantic clustering, text analytics, and others to extract sentiment analysis insights in a step-by-step manner. The platform can process thousands of data points in seconds through this methodical process.

The platform gives you a detailed view of what customers like about you, whether it is through social media sentiment analysis, news monitoring, or analyzing manual data that you can upload as an excel file. All data insights are processed in real-time and the results are presented on a visualization dashboard.

Benefits of using machine learning for CX insights

You can set alerts for brand mentions on social media, or keywords such as competitor brand names, products, locations, and even hashtags.
Get consistent customer and market insights at speed and scale.
Remove human errors and bias as manually decoding emotions in comments can be subjective based on personal experiences.
Assess video content for sentiment as easily as you would text data.
Measure and track sentiment trends across different timelines
Analyze multilingual customer experience data without resorting to machine translations. This is especially useful if you have a multilingual, multiethnic customer base.
Get an in-depth understanding of different aspects of your business operations, products, and services based on customer sentiment.
Discover new avenues for enhancing customer loyalty and satisfaction through relevant loyalty programs.
Find ways to improve business processes and operations based on customer feedback and pain points.
Enhance your product offerings and market share.

Conclusion

Hopefully, this article has given you a succinct idea of how AI-powered CX analytics and insights can boost your marketing efforts.

Investing in any kind of technology for the first time, or enhancing the capabilities of an existing CRM technology that you are currently using can be a time for deliberation because your investment should give you the returns you expect. The important thing to remember though, is that decisions based on machine learning-powered business intelligence are a sure-shot way to business longevity and competitive advantage because they are based on analytical data.