Sentiment analysis in customer experience refers to the data analysis process of understanding and measuring how a customer feels about a particular product, service or brand. This data might be in written form and gathered from spoken language. A business can use the sentiment analysis metric to understand customer metrics like CSAT and NPS scores. This information can help redirect business operations, customer service and business processes to improve the customer experience.

Sentiment analysis is a form of data science that uses artificial intelligence (AI), machine learning, and natural language processing (NLP) to analyze customer feedback and customer reviews in real-time from across an organization. The sentiment analysis in customer experience specifically pulls from all types of interactions, such as support tickets, surveys, product reviews, phone calls, among other types.

By using these interactions as data, a business can figure out key issues customers are contacting them about and provide real-time insights into the sentiment behind each issue. In addition, by analyzing these data points an organization can figure out negative and positive sentiment drivers and get a visual of any fluctuation in customer sentiment.

The main goal of sentiment analysis in customer experience is understanding a customers’ feelings and emotions toward the brand. The sentiment analysis process can result in the implementation of tools, such as chatbots or a reconfigured marketing strategy that introduces a targeted marketing campaign. Sentiment analysis is just one part of a larger customer experience (CX) strategy, which typically involves multiple components. All with the goal of retaining current customers and bringing in new, lasting relationships.

The step-by-step process varies from one organization to another but the general outline of the process can be applied to most organizations seeking to better understand their customer and make data-backed improvements to their brand.

Step 1: Gather customer data

The first step to a sentiment analysis is collecting the data that will fuel the analysis. This data can be collected from a range of different sources depending on the organization.

• Customer interviews or focus groups: These insights can be through written customer interviews done online or over the phone. Or a select focus group that was created specifically to understand customer sentiment about a product.

• Emails or online reviews: Sentiment analysis tools can help collect email correspondence with customers and categorize them into their proper placement.

• Social media monitoring: Sentiment analysis can be used to understand and monitor customer comments made on social media platforms.

• Feedback on review sites: Some users might go to specific product review websites to offer their experience and feedback.

• Customer support tickets: Sentiment analysis can be used to separate and analyze customer support tickets opened by customer service staff and decipher if the issue was resolved or left open.

• Interactions with customer-facing teams, like sales or support: The sentiment analysis can also discern if a customer interacted directly with a customer support team person or if they went through an automated channel.

With data from multiple channels, the sentiment analysis can be more complete and give the organization a broader understanding of how a customer feels. The data collected at this stage is highly important as it is the foundation of the sentiment analysis process and will be what an organization will base decisions off of when it comes time to do so.

Step 2: Identify positive and negative sentiment patterns

Once the data has been collected the next step is identifying the positive and negative sentiments customers are having with the product or service. This can be done in several ways, the most common being to look at quantitative survey responses, such as net promoter score (NPS) and customer satisfaction score (CSAT) surveys.

Qualitative responses are a bit more difficult and require organizations to go through responses manually, but advances in technology have helped to automate this process. AI-powered feedback analysis that can pick up customer sentiment automatically is also gaining in popularity.

Step 3: Gain actionable insights

Once an organization has identified the positive and negative customer sentiments, its time to gather actionable insights to improve the overall customer experience. An example might be customer feedback analysis which shows that customers are unsatisfied during the onboarding stage, characterizing it as ‘overwhelming.’

Which would mean an overhaul to the onboarding process and a detailed break down of the current system in place. Other areas of analysis can also provide valuable insights into customer issues and negative experiences.

Step 4: Take insights and make improvements

Now it’s time to implement strategies to address these areas of improvement. Let’s use the example of an ‘overwhelming’ onboarding process. The organization would start by analyzing user behavior and support ticket analysis to get to the root cause of confusion.

A potential scenario is that many users aren’t completing the onboarding checklist. But with the help of sentiment analysis the organization finds customers are dropping off at one particular step. A way to improve the process is creating an interactive walkthrough that guides users through the task. Another option is a video tutorial with step-by-step guidance that gives better support interactions.

Step 5: Analyze and measure the impact

In this final step, organizations look at if the strategies implemented are making the wanted impact on their customers. The best way to do this is to measure performance and continuing to analyze sentiment insights. Organizations will likely need to couple sentiment analyses with further analytics data for accuracy.

Once those strategies are measured, organizations can evaluate the impact of them through monitoring key performance indicators (KPIs) and continuing to monitor customer sentiment analysis.

Sentiment analysis helps customer experience in a range of different ways. The following benefits of sentiment analysis are some of the biggest and most popular.

By doing a sentiment analysis a business can better understand customer expectations and produce a sentiment score that can be put into practice. The sentiment analysis allows businesses to understand customer emotions and customer and create a more personalized experience. Organizations can take the analysis and craft personalized experiences that resonate with individual users, ultimately improving brand reputation and customer loyalty.

Building off of a more personalized customer experience, another benefit to sentiment analysis is improved customer satisfaction. By analyzing customer sentiments organizations can create individualized experiences that align to their specific needs. Catering to the customer needs can result in better brand loyalty and more positive customer opinions.

With the help of KPIs like sentiment analysis an organization can monitor customer retention or customers at risk of churning. The analysis can also reveal why customers might be inclined to switch to a different product or service and organizations can then pivot to create new marketing strategies, such as social media posts or pricing changes.

A part of the customer experience that shouldn’t be forgotten by an organization is how well the product or service on offer is actually functioning. Sentiment analysis can make an organization’s products better by helping them recognize features customers desire and ones that are less successful or have defects.

A sentiment analysis can be extremely helpful to a business as it tries to understand customer insights, however text analysis can be challenging. Therefore, a data analysis process can’t pick up sarcasm, emotional tone or misspelled words, it isn’t a perfect science.

One word used by a customer might have a different connotation depending on the context, which can lead to confusion and ambiguity. A way to overcome this issue is to find sentiment analysis tools that are AI-based and can be trained to pick up on these nuances. It’s important for software to be updated regularly and trained by humans and not just through algorithms.

Sentiment analysis, also referred to as emotional artificial intelligence or opinion mining, is implemented to help a business figure out where improvements can be made in the customer journey to leave customers with a better experience.

A list of some sentiment analysis use cases are as follows:

• Improve product usability: Take data analysis from the sentiment analysis to simplify complex features in the product or service being provided.

• Personalize user engagement: Provide more relevant experiences through tailored content on various channels that are based on the sentiment of the individual user.

• Enhance the onboarding process: Find the pain points for new users who are struggling and implement onboard checklists or product tours, making it a simpler initial experience.

• Guide product development: Take positive sentiments and customer preferences into account when developing new products and features.

• Better customer support training: Help customer support teams by using sentiment data to train them on handling negative feedback and improving customer interactions.

• Detect friction in the customer journey: Use sentiment data to identify negative sentiments in feedback to hone in on recurring issues and friction points.