How Sentiment Analysis Can Help Save Customer Satisfaction

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Using customer satisfaction surveys is often a good indicator of how satisfied your customers are, but they are just an indicator. The measurements say very little because very few respond to such surveys. According to CustomerSure, only 15 percent of surveys are answered by customers. With such a small data set, you probably do not have a good representation of all customers’ actual impressions of your company.

Sentiment analysis comes to the rescue

Sentiment analysis helps you discover the customer’s negative or positive feelings about a product or service when they are in dialogue with your company.

Sentiment analysis is a machine learning technique that divides a customer’s response into words. It assigns a score for words that reflect how positive, negative, or neutral the word is. The algorithm will then collect each word’s score to give a total sentiment score for the interaction.

To improve customer experiences, you can take positive, negative, and neutral sentiment points from an interaction with a customer in your contact center and find reasons that a customer satisfaction survey may not have addressed. With the right resources, the contact center can identify negative and positive sentiments and use this information to improve the customer experience.

The perfect customer satisfaction survey for the contact center

Using sentiment analysis as a customer satisfaction measure at the contact center can help product teams improve products according to customer needs. Analyzing such customer feedback can proactively help you improve your products and increase overall customer satisfaction. The insights you gain through sentiment analysis can help you enter emerging markets, develop new products or improve existing services. For example, suppose you are launching a new product or service. In that case, you can use sentiment analysis to determine what your customers liked and disliked. The analysis can thus be used to improve the products or services.

Whether you’ve just launched a marketing campaign, changed pricing structures, or launched a new product, sentiment monitoring can help you monitor customer responses. For example, monitoring how customers express their opinions on social media can help you identify an ongoing issue affecting your customers so you can act quickly.

Detect churn and sales signals in real time

You can also use sentiment analysis to detect customers who need extra attention. Be it customers who are struggling to pay, customers who are frustrated with the customer service representative at the contact center, or customers who, in the worst case, want to end their customer relationship. Sentiment analysis helps companies derive value from, among other things, product reviews, social media, and customer satisfaction index (often referred to as NPS – Net Promoter Score) and utilize this insight to make more intelligent decisions that improve customer satisfaction. The company can make data-driven decisions to improve customer satisfaction and loyalty by monitoring customers’ emotions. Through artificial intelligence, sentiment analysis can automatically categorize customers’ opinions as negative or positive – in real-time – saving you time instead of processing these responses manually.

Sentiment analysis allows you to uncover people’s feelings about your brand and utilize this information to improve the customer experience. You can easily find out who can be a brand ambassador for your products and services, which can be crucial to your customer club, product development, and social media strategy. Intention-based sentiment analysis reveals and understands customers’ intentions towards your brand, products, services, or even experiences (for example, if a customer is interested in buying your products or services). By targeting customers according to their feelings for your brand, you can create very personal experiences.

Proactive customer service

You can use the data collected through sentiment analysis to stay up to date, inform product teams internally about concerns or challenges, and influence new and existing customers. Combining traditional customer surveys via customer satisfaction survey (CSAT) or Net Promoter Score (NPS) survey with sentiment analysis, you will easily find hidden insight into the customer service experience. Combining traditional customer satisfaction surveys and sentiment analysis can give your company a deeper understanding of customers’ feelings about the brand.

With the help of sentiment analysis, you can gain an integrated, holistic, and actionable understanding of your customers. It will come in handy to help you understand the feelings your customers have about your business. Those emotions can be converted to build an empathic experience that will increase loyalty, sales, and repurchases. By monitoring the information in every contact center dialogue, the contact center can create recommendations and solutions based on customer trends and past interactions.

With sentiment analysis, your company can collect qualitative data, a powerful tool for improving the brand, reputation, and customer experience.

This post was first published on the Omnicus blog

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