Customer Sentiment Analysis: All You Need to Know in 2024

Customer sentiment encapsulates customers' emotional tone, opinions and attitudes toward a brand, product or service. It's derived from diverse sources, including social media monitoring, surveys, text analysis and customer voice, offering invaluable insights into customer preferences and perceptions. 

Multiple stakeholders play crucial roles in determining customer sentiment, including marketing and brand managers, customer service teams, product development specialists, business analysts and executive leadership. Each contributes to the holistic understanding of customer sentiment, utilizing various tools and techniques to effectively capture and analyze customer feedback.  

Leveraging sentiment analysis enables your business to delve deeper into your customers' preferences, pain points and expectations, empowering you to tailor products and services to meet their evolving demands. Furthermore, by comprehending customer sentiment, you can proactively address concerns, optimize customer touchpoints and foster lasting loyalty, enhancing the overall customer experience.

Significance of customer sentiment

With little differentiation among products and services these days, customers often purchase or repurchase based on their experience with your business. So, understanding customer sentiment at every phase of the customer journey is vital. In addition, here are some reasons why customer sentiment tops the priority list of customer service leaders today.   

1. Early issue detection  

Sentiment analysis allows you to spot and address problems before they escalate. You can identify bottlenecks in the end-to-end customer journey and efficiently resolve issues by understanding how customers feel. This will demonstrate a commitment to customer satisfaction and bolster brand reputation management efforts.

2. Enhancing customer experience

Improving the customer experience requires understanding their emotions and opinions, which can provide valuable insights into their needs, preferences and pain points. With this knowledge, you can create personalized conversations and tailor your products and services to meet their expectations. This will ultimately lead to greater customer retention and satisfaction.

3. Driving business growth

Customer sentiment analysis isn't solely about retaining existing customers but also about attracting new ones. Positive sentiment draws prospective customers, whose experiences are influenced by satisfied customers, thus fueling business growth and expansion.

4. Informed decision-making

Understanding customer sentiment enables data-driven decision-making across various facets of your business. Whether it involves launching a new product, entering a new market or refining your marketing strategy, aligning decisions with customer needs and preferences minimizes risks and maximizes success.

5. Gaining competitive advantage

By delving into customer sentiment, you gain insights into what sets your brand apart in your customers' eyes. By identifying emerging trends, unmet needs and areas for improvement, you can stay ahead of the competition, strategically innovate and position your brand as a leader in your industry.  

Good Read: What is Competitive Benchmarking?

How to determine the customer sentiment score  

Despite being perceived as a qualitative measure, customer sentiment can be quantified through the score. This score is an aggregate value derived from individual sentiment scores assigned to each piece of customer feedback, encompassing sentiments' polarity and intensity.

Polarity: Polarity distinguishes whether sentiments are positive, negative or neutral. Positive sentiments indicate satisfaction, approval or enthusiasm, while negative sentiments reflect dissatisfaction, criticism or disappointment. Neutral sentiments signify an absence of strong feelings or opinions.

Intensity: Intensity measures the strength of emotional expression. For instance, a sentiment scored as highly positive may signal great satisfaction or delight, while a moderately positive sentiment indicates a reserved level of approval.

Consider a practical example involving a scale from 1 to 5, where 1 means "very dissatisfied" and 5 means "very satisfied." If a retail chain introduces a loyalty program and receives various ratings:

• A rating of 5 suggests high satisfaction.  

• A rating of 3 indicates moderate satisfaction.  

• A rating of 1 shows dissatisfaction.  

After analyzing all responses, imagine the retail chain calculates an average satisfaction score of 4.2 out of 5. This average score suggests most customers are highly satisfied with the loyalty program, with a score over 4. However, there's potential to enhance the program by addressing feedback from customers who gave lower ratings.   

Therefore, a customer sentiment score provides businesses with a clear indicator of effectiveness and areas for improvement. You can track changes in customer sentiment by analyzing feedback from reviews, customer surveys and social media, as well as using technology to assign values to words and phrases. This score is vital for guiding efforts to enrich customer experiences and stimulate growth.

How to measure customer sentiment 

Here are five techniques for measuring customer sentiment:  

1. Using sentiment analysis software  

Sentiment analysis software automates the analysis of written content such as customer reviews, emails and support tickets. This software uses advanced natural language processing (NLP) and machine learning algorithms to quantify and categorize sentiments, providing valuable quantitative data for decision-making. Here is how it works: 

• Utilizing NLP and machine learning: The application uses NLP and machine learning to analyze text data and determine sentiment polarity, thus categorizing the sentiments as positive, negative or neutral.  
   

• Processing data from all feedback sources: The software seamlessly processes customer feedback from online reviews, support interactions and survey answers.    
   

• Identifying patterns and trends: This method allows you to identify patterns, trends and fluctuations in sentiment over time.  

Good to know: With modern conversational analytics software, you can understand how your customers feel about your products or services. This is achieved by categorizing various aspects or topics related to your offer. By doing so, you can better understand what your customers appreciate and dislike, which can guide you in making targeted improvements. Moreover, you can leverage this tool to identify the current trending topics among your customers and analyze their intents and sentiments across all conversation data.

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2. Using surveys and feedback forms 

Surveys and feedback forms are among the most popular methods to measure customer sentiment. This method collects direct feedback on a product or service from customers.

Implementation: This phase involves designing surveys with questions about customer experiences, satisfaction levels and expectations.

Scoring: Responses are often rated on a scale, such as 1 to 5 or 1 to 10, where higher scores indicate more satisfaction. This facilitates the quantification and analysis of feedback.

Benchmark: The overall sentiment can be gauged through average scores, with scores above a certain level, like 4 out of 5, usually indicating positive customer sentiment.

Benefit: This approach offers clear, direct consumer insights, pinpointing what works well and what needs improvement based on their feedback.  

How Sprinklr AI+ helps

While you can track customer feedback across conversations on 30+ digital and social channels — including voice with Sprinklr's omnichannel survey software, Sprinklr AI+ takes you one step further.

With Sprinklr AI+, you can assess customer sentiments in messages across channels, including chat, email and voice. It empowers you to analyze phrases and understand customer sentiment based on parameters such as conversation quality, behavior, resolution time, convenience and more. 

In addition, you can use AI to monitor conversations for a host of signals and predict CSAT scores on ongoing conversations in real time. Accordingly, you can set up alerts and escalation logic and analyze agent performance. 

3. Monitoring social media  

You can use AI-powered social listening tools to track brand mentions and hashtags for real-time sentiment analysis. 

Calculation method: Specialized tools can track and analyze mentions, providing scores or percentages of positive vs. negative sentiments.  

Benchmark: A positive mention percentage above 75% is typically considered strong, but this varies by industry and context. 

Benefit: Social listening is ideal for brand reputation management and evaluating public perception during specific campaigns or events.

4. Net Promoter Score (NPS) 

The net promoter score (NPS) is a straightforward metric to gauge customer loyalty and sentiment toward a brand.   

• Implementation: Customers rate their likelihood of recommending your business to friends or colleagues.   

• Scoring: Scores of 9 or 10 are considered promoters, 7 or 8 are passives and 0 through 6 are detractors. The NPS is the percentage of promoters minus the percentage of detractors.   

• Benchmarking: Any score higher than 0 is considered a positive net promoter score (NPS), indicating that most of your customers are promoters or are likely to recommend your products or services to others. However, it's essential to understand that the interpretation of NPS can vary depending on the industry and specific business context. 

According to industry standards, a score of 50 or more is often regarded as excellent, signaling high customer satisfaction and loyalty. A score over 70 is considered exceptional and indicates that your brand is exceptionally customer-centric, with a large proportion of customers acting as enthusiastic promoters.

Conversely, if the NPS falls below 0, it suggests that your customer base has more detractors than promoters. This signals potential issues with customer satisfaction and loyalty, requiring urgent attention to improve the overall customer experience and address underlying concerns. 

• Benefits: NPS quickly looks at customer loyalty and sentiment, forecasts business growth and guides efforts to enhance customer service. 

5.Text analytics and topic modeling 

Text analytics and topic modeling emerge as powerful ways of extracting meaningful text-based insights from unstructured textual data.  

Implementation: Topic modeling algorithms, such as latent Dirichlet allocation (LDA), help categorize customer feedback into relevant topics and assess sentiment within each topic category.   

Benefit: It offers a detailed understanding of customers' sentiments, pinpoints core discussion areas and directs targeted improvements.   

Scenario: Particularly useful for large datasets, it helps decision-makers identify common threads and sentiments, bringing structure to prioritizing action based on customer feedback. 

Deep Dive: What is Text Analysis: A Complete Guide 

What is customer sentiment analysis? 

Customer sentiment analysis, also known as opinion mining, gauges the tone or emotion conveyed by a series of words—positive, negative or neutral—across various platforms such as social media, customer feedback forms, online surveys and more. It offers a comprehensive view of how individuals perceive your brand, products, advertisements and competitors.

The process of customer sentiment analysis 

Here's a step-by-step guide to effectively analyze and apply customer sentiment data: 

📨Step 1: Gather and aggregate data 

Start by collecting customer feedback from diverse sources such as surveys, social media, reviews and customer service interactions. This ensures a well-rounded view of customer sentiment across different touchpoints.  

🔍Step 2: Clean and preprocess data 

After the data has been collected, the next step is cleaning and preprocessing. This involves removing irrelevant information, such as spam or off-topic comments and standardizing formats to ensure consistency across data sources. 
  
💡Pro Tip 

Consider implementing automated data cleaning tools or scripts to streamline the process, saving time and effort while maintaining data integrity. Additionally, prioritize data quality over quantity, extracting and analyzing the most relevant and actionable insights from your customer feedback data. 

🤖Step 3: Apply sentiment analysis algorithms 

Utilize natural language processing (NLP), AI and machine learning technologies to gauge customer empathy and assess the emotional tone of feedback. These technologies categorize sentiments as positive, negative or neutral and can assign specific sentiment scores to individual pieces of feedback.

📊Step 4: Visualize data 

Translate your analysis results into visualizations like charts, graphs and heat maps. These visuals can highlight trends and patterns in customer sentiments over time or across different products. 

🚀Step 5: Act on insights 

Utilize findings from customer interaction analytics to address issues and enhance the customer experience. Whether improving products, refining marketing strategies or optimizing customer service processes, acting based on insights is crucial for driving positive outcomes. 

How to drive actionable insights from customer sentiment data 

Translating customer sentiment data and analysis into actionable business strategies is essential for realizing tangible improvements. Here’s how to turn insights into actions:

Identify and prioritize key issues

Utilize data on customer sentiment to identify the most urgent issues. Look for repeating themes and patterns in the feedback to determine the critical problems impacting customer satisfaction. Prioritizing these issues allows for faster improvement in customer experience.

Segment insights by customer demographics

Recognize that customer sentiment can vary significantly among different demographic groups. Customer segmentation by demographics such as age, gender, location or buying history enables tailored approaches to meet the unique needs of each group. This targeted method ensures positive outcomes for all customers.

Track sentiment trends over time

Customer opinions and feelings about your brand evolve, influenced by new products, services or external events. Monitoring changes in sentiment trends allows for timely strategy adaptation. Continuous audience analysis enables proactive issue avoidance and continual refinement to surpass customer expectations.

Integrate with other data sources  

Combining sentiment data with other information sources, such as sales data, customer service metrics and interaction analytics, is crucial to gaining a comprehensive understanding of customer sentiment. By correlating sentiment data with these data points, you can uncover valuable insights that inform decision-making across the organization.

Act and iterate

Recognize that improvement is an iterative process. Continuously collect feedback, measure outcomes and refine approaches to align with evolving customer sentiments. Regularly revisit strategies and adjust based on new and emerging data and insights. 

With the growing data points and diverse customer touchpoints, accurately determining customer sentiment at scale remains a significant challenge for many businesses. Traditional methods often fall short in capturing the nuances of customer feedback and leave you grappling with incomplete insights and missed opportunities for improvement.  

If the above resonates with you, you can try Sprinklr Service and see the difference yourself. By seamlessly aggregating and analyzing data from diverse sources, including social media, surveys, reviews and more, Sprinklr Service empowers you to gain deep insights into customer preferences, identify emerging trends and proactively address issues to enhance the overall customer experience. 

Schedule a customized demo today and our experts will be in touch.