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.
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.
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.
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.
Read more at: CX Analytics & Insights
Would you be concerned about what people are saying about you?
Sentiment analysis is essentially the process of aggregating, understanding, and analyzing the views of people about a particular brand.
The term is also known as mining opinions in data analytics lingo.Sentiment analysis as a process helps the businesses in understanding whether the underlying emotion about their brand is neutral, positive, or negative.We are producing 2.5 quintillion bytes of data daily.
The insights gained through sentiment analysis can enable your brand to create positive sentiment about your brand by designing public relations tools.Benefits of Sentiment AnalysisOptimize your marketing strategyConducting functional sentimental analysis helps brands in optimizing their marketing strategy as they can know if their marketing strategy is proceeding in the correct direction or not.Evaluating the ROI of your marketing campaignSentiment analysis helps in evaluating the ROI of your marketing campaign.
Thus giving you ample time to respond to these negative discussions as the earlier you react, the better it is.By adjusting your marketing campaign according to the preferences of your customers, by improving the product quality substantially and by extending excellent customer support, you will increase the chances of gaining better leads.As they say, in business, perception is more important than the actual thing.
When Nike announced that it was taking the controversial NFL quarterback in its latest announcement, the customer sentiment was overwhelmingly negative.
Investors weighed signs of the economy, recovering against a rise in coronavirus infections in the global Sentiment Analytics Market.
Market Research Future digs into account and found that the market would rise at a pace of 14% CAGR and would reach USD 6 Billion valuations by 2023.
The study reveals that the overall market would get bigger post the pandemic at an increased pace with the invention of the new-fangled dynamism, which is making rapid progress in the market’s growth, mainly from the years 2017-2023 (forecasted period).
Request a Free Sample @ https://www.marketresearchfuture.com/sample_request/4304 Competitive Outlook:The well-known players in the sentiment analytics market are included as Clarabridge (U.S.), Angoss Sentiment Corporation (Canada), SAS Institute (U.S.), IBM (U.S.), Bottlenose.Com (U.S.), Aylien (Ireland), Brandwatch (U.K.), Crowdflower (U.S.), Adoreboard (U.K.) and Twizoo (U.K.).Segmentation:The sentiment analytics market is further segmented among the segments, such as components, deployment, organization size, and vertical.Depending on the organization size segment, small and medium enterprises hold the largest market share.
Escalating competition creates an opportunity for businesses to collect efficient data related to sentiment development is one of the prime factors boosting the sentiment analytics market.Sentiment analytics market by components segment: Comprises sentiment, professional services, service and support, and maintenance services.Sentiment analytics market by deployment segment: Comprises cloud and on-premise.Sentiment analytics market by vertical segment: Comprises BFSI, transportation & logistics, retail, education, healthcare & life sciences, media & entertainment, and others.
Regional Analysis:The regional analysis of the sentiment analytics market is being studied for regions of the Americas, Europe, Asia Pacific, and the Rest of the World.
OptiRisk specializes in optimization and risk analytics and is renowned for its research and development of models and software systems in these domains.
In the domain of Sentiment Analysis, OptiRisk is a partner of Thomson Reuters and RavenPack.
The company has researched and published the Handbook of News Analytics in Finance and the Handbook of Sentiment Analysis in Finance.
