There are various ways to capture what people discuss about your business. This usually happens both online and offline. People converse over social media platforms, on your website or leave reviews on Google or Yelp pages. While it can get difficult to create data from un-tracked metrics, a customer sentiment dashboard can collect emotions and curate meaningful reports for CX teams.

What is Customer Sentiment Analysis?

Customer sentiment analysis is the process where machine learning algorithms analyze customer feedback and segregate them under positive, neutral or negative reviews. This is done to understand customer sentiment towards a brand, product, or service. The process uses natural language processing (NLP) techniques, machine learning algorithms, and sentiment analysis models to extract sentiment insights from unstructured data.

It is an essential tool for companies. Stakeholders, customer service teams and CX departments can gather real-time information about how people feel online. This allows businesses to quantify customer sentiment, track changes over time, and identify areas for improvement.

How is the customer sentiment dashboard useful?

People’s emotional metrics is a set of complex data to comprehend. To compile and present this data, you need to have a dashboard that can track crucial metrics. This way, the results will be easier to understand, digest and utilize.

• A customer sentiment dashboard can make sense of complex human emotions and convert it into actionable insights.
• The sentiment analysis dashboard can track trends, conversations, spot patterns, and identify pain points. This makes it easier for customer support teams to close feedback loop at the earliest.
• A sentiment analysis dashboard is a great way to share compiled information with your teams and stakeholders.
• Detect early churn, capture happy consumers/ brand advocates, pinpoint detractors and close the feedback loop sooner. This can help marketing departments act fast and in real time.

By analyzing customer sentiment, businesses can gain actionable insights into customer experiences, pain points, and satisfaction levels. It enables companies to comprehend how people perceive their products/services, and helps them make informed decisions to enhance customer satisfaction and loyalty.

3 Types of Sentiment Scores on the Customer Sentiment Dashboard

Positive: “I am extremely satisfied with the service” is an example of a positive sentiment. This will have a sentiment score closer to +1.

Neutral: “I availed of the service last week” is an example of a neutral sentiment. This will have a sentiment score close to 0.

Negative: “I am extremely dissatisfied with the service” is an example of a negative sentiment. This will have a sentiment score closer to -1. 

4 Aspects of Customer Sentiment Analysis

An advanced tool will pick up sentiment aspects from a single sentence and segregate it either as positive or a negative experience. Segregate based on emotions and their intent in the ‘aspect-based’ sentiment analysis. Since human conversations can be of complex nature, an aspect-based sentiment analysis aims to assess the ‘why’ behind various emotions.

Polarity: Polarity will identify if reviews are on a spectrum of positive, neutral or negative segments.

Emotions: Emotions will segregate responses based on happy, sad, frustrated, disappointed, angry.

Urgency: This metric will identify responses based on urgent or non-urgent basis.

Intentions: intentions are to map on the basis of potential customer, risk of churn, trying to make a purchase.

While there can be massive amounts of customer conversations, a well-designed machine learning tool can segregate emotions on a spectrum and identify intent. With this, you can capture real-time information and analyze complex customer experiences. Every metric adds up to your CX protocol. 

5 Reasons A Business To Conduct Customer Sentiment Analysis

There are several important reasons why businesses should conduct customer sentiment analysis as part of their CX management strategy:

• Identify potential issues and pain points: Sentiment analysis allows businesses to uncover potential problems, complaints, and pain points more effectively. Businesses can identify recurring problems and take proactive measures to address them, improving overall customer experience management.
• Improve after-sales service and support interactions: By analyzing customer sentiment, businesses can gain valuable insights into expectations, sentiment classification, and sentiment scores. This information enables support agents to provide better service, address consumer issues promptly, and personalize interactions, enhancing customer satisfaction.
• Gain actionable insights: Customer sentiment dashboard brings valuable insights into customer preferences, opinions, and sentiment data. By understanding customer sentiment, businesses can tailor their products, services, and marketing strategies to better meet market needs and preferences.
• Enhance customer loyalty: Positive customer sentiment is essential for building brand advocates. Leverage sentiment analysis to identify satisfied customers, advocate positive sentiment, assess sentiment scores, and analyze negative feedback. Address negative feedback promptly and turn detractors into brand advocates avoiding online conflicts.
• Improve internal CX protocols: Sentiment analysis dashboard provides real-time customer sentiment insights that can be used to enhance customer experience. It helps businesses understand user sentiment scores, qualitative feedback, relevant information, phone calls and support tickets over the dashboard. 

Analyze Emotions on the Customer Sentiment Dashboard

Gathering customer sentiment data from diverse sources is the initial step. Classifying customer feedback and sentiment insights using a sentiment analysis model comes next. Visualizing this data with a powerful sentiment analysis dashboard helps in analyzing sentiment insights for improved customer interactions and satisfaction.

Identify omnichannel feedback platforms for your target respondents:

Your consumers are active on most online channels. Keep communication channels open to collect feedback via responsive surveys. This includes website chatbots, AMP emails, instant WhatsApp surveys and Facebook messenger surveys and SMS. Responses captured over customer support calls and live chats are always unbiased, candid and contain a number of emotional metrics. This also includes customer reviews over websites, Google pages and other third-party review sites. Every testimonial gives a gold-mine of data for emotion-based analysis.

When it comes to customer satisfaction surveys, there are popular metrics that are industry standard. These surveys can accurately capture the emotional response of people via scales and scores. Commonly used customer feedback metrics are net promoter score or NPS, CSAT score or the customer satisfaction score, conversations from customer support team (calls, live chat, automated chatbot), client testimonials over the internet to name a few.

Analyze and categorize feedback based on emotions and sentiments:

Once you have gathered customer feedback from various sources, the next step is to analyze and categorize it based on emotions and sentiments. This process involves using a sentiment analysis tool that can accurately classify respondent feedback and provide insights into their emotions. An important pointer to note is that when companies gather reviews, the support team must identify the source from where the review was generated.

By visualizing this customer data on a powerful sentiment analysis dashboard, businesses can gain valuable insights into customer sentiment. You can track people at the ‘rick of churn’, unhappy customers or trace where you have to close the feedback loops at the earliest. Categorize each feedback on aspect based sentiment segregation, discover the intent, polarity and urgency. Place them against the sentiment score dashboard and create a plan of action depending on scores. This can streamline where the support teams can gather their resources to attend to respondents and avert potential crises.

Automated report generation and data analysis on actionable insight:

There are different ways to display a dashboard report. Since the dashboard collects data- qualitative and quantitative– both need their own formats. Automation of reporting format can compile complex human emotions, survey metrics and bring them together in a digestible format. Real-time reporting format also tracks conversations real time on social media mentions, captures survey responses and measures brand reputation round the clock.

Reports can be segregated into bar charts, graphs and individual reports for stakeholders. However, analysis and report generation should be accurate and reliable. Manual handling of data is prone to tampering, bias and can have loss of data. 

Conclusion

Customer sentiment analysis is more than just tracking promoters, detractors and passives. People come with expectations and emotions. A well-crafted customer sentiment analysis can transform satisfaction metrics, increase loyalty and brand advocacy and supercharge your business among market competitors. Automated machine learning processes can make it easier for industries to track and map complex human emotions. While qualitative feedback will also need some manual intervention, a customer sentiment dashboard is a must-have across organisations. Quickly extract the pulse of your customers , initiate action to close any feedback loop and create brand advocates.