CX Analytics & Insights For Beginners
Overview 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.
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
The Difference Between CX Analytics and CX Insights 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
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. Better sales conversions. Build customer loyalty. Increase in revenue. Stay ahead of the competition.
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 ecommerce 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
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.
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
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
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