What is Customer Sensitivity Analysis?

Customer sentiment analysis is a way of processing data from customers, typically in text form, to identify feelings and opinions. Customers always provide their feedback or opinions about the product or service in some way (social media, forums, etc.). Feedback can be positive, neutral or negative sentiment. Their experience can reveal a few key indicators that point to the problem facing the service or product.

While companies can gather hundreds or thousands of responses and ideas weekly through help desks or different communication tools, it is difficult to try to understand what customers are saying without reading each response. This is where emotion analysis, a computer-based method, can be best used. Being able to understand hundreds of customer emotions through sensitivity analysis can help identify patterns and trends that will help improve the customer user experience.

How is Customer Sensitivity Analysis Performed?

Customer sentiment analysis is a system based on assigning certain words that are expressions of emotion to evaluate a customer response. These words, which are expressions of emotion, are assigned a number to reflect how positive, negative or natural they seem, and then the scores for each word to be taken are added up. It is a machine learning method on the sum of points and a general sensitivity score is deducted for the given answer. With the score achieved, organizations can evaluate sensitivity and note appropriate feedback.

Modern sensitivity analysis models are directed not only to satisfaction (positive, negative, neutral) but also to perspectives and feelings (angry, happy, frustrated, etc.). And even focuses on motives (eg, interested in purchasing your product, not interested in purchasing your product).

Benefits of Analyzing Customer Emotions

Learning how consumers feel about the product or service is key to improving the customer experience delivered to them. The benefits of customer sensitivity analysis can be listed as follows;

Provides Feedback for the Constructive Product or Service

One of the most important benefits of analyzing customer sentiment in responses is to easily identify areas of improvement in a product or service. Negative feedback can reveal that the support team for the application is slow to process or a critical bug that has remained uncorrected for a long time. Early analysis of such customer feedback can help to proactively improve the product and increase the overall satisfaction of customers.

Improves Customer Service

By receiving periodic customer feedback through CSAT (Customer Satisfaction) or NPS (Net Promoter Score) surveys, confidential information can easily be obtained about the quality of customer service customers receive. It can be understood which types of customer problems take the longest to resolve. By using these insights and customer sensitivity, meaningful changes can be made in customer service processes.

Brand Popularity is Traceable and Measurable

Using sentiment analysis, you can quickly spot angry tweets or posts directed at your company on social media and take action instantly. If these posts are left unnoticed, it could seriously hurt brand popularity. A quick response goes a long way in making sure customers have a cutting-edge customer experience. These will also help alleviate the situation that would otherwise go to a point of no return.

Marketing or Product Strategy Can Be Improved

The weaknesses and strength of the brand’s competition can easily be revealed with appropriate customer sentiment analysis. By focusing on the weakness of competitors, marketing and product strategy can be adjusted. By learning the strengths of competitors, they can be imitated or duplicated in the product or service, and used for the benefit of this company. The return on investment of marketing campaigns can be evaluated on both numerical (conversion rate) and non-numeric (customer sensitivity) returns.

There are some drawbacks to not paying enough attention to negative customer feedback. Customers express their disappointment through online media, hoping that other customers who want to purchase the product or service will not experience the same. Negative customer experiences can cause:

• Decrease in customer acquisition rates

• Decrease in profit margins

• General loss of trust with the brand or product

Where Are Customer Emotions Measured?

Modern organizations interact with customers through a wide variety of channels. Some of the popular channels used to measure customer sentiment are:

Customer Support

Most organizations use software like HappyFox, Zendesk or just Email to centralize and manage all support requests. By transferring their customer responses to a sentiment analysis tool, they can quickly determine how happy, neutral, upset a customer is based on the service they provide them. HappyFox has a built-in customer sentiment analysis feature, which means it can instantly receive customer sentiment ratings directly from the help desk interface.

Community Forums

Companies create their own public forums for customer interaction. Having a good forum presence will increase brand reputation and increase customer loyalty. That’s why it’s so important to keep an eye on angry customer posts and resolve their issues quickly. These outliers are quickly revealed by running customer posts through a sentiment analysis engine.

Social media

Social media platforms such as Facebook and Twitter remain the most popular choice for customers to reach the brand. By collecting all tweets and direct messages that mention the brand, a comprehensive analysis can be done to assess customer sentiment. Most social media platforms allow companies to export customer response data via their APIs.

Software Review Sites and Trade Sites

Customers often leave comments about the product or service on popular review sites like Capterra, G2 Crowd, or GetApp. The customer reviews displayed by these providers are quite extensive and a lot of information can be gathered from them. The best way to get data for sentiment analysis on these websites is to extract data via web scraping.


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