Good brands understand their customers, but ideal brands understand the sentiments of their customers. They take care of customers’ needs by identifying how they feel about their products and services. To meet the rising customer expectations, real-time insights about customer sentiments can come handy for service agents to engage with them in a highly personalized manner and deliver empathetic service.
Can you foretell whether the next customer who calls in for service is in a bad or happy mood?
Picture this – one of your customers receives a damaged product and emailed your customer support expressing his anger about how you spoiled a surprise he planned for his son’s birthday and then he dials to follow up. Now, imagine if your customer support system can identify that he is already unhappy with your services and direct the further steps accordingly. For starters, skip the IVR (Interactive Voice Response) for his call and route him directly to a dedicated agent who can look into his issue on priority. This will certainly help in pacifying the already aggravated customer in the first place.
This is possible with the help of Sentiment Analysis, which will give you insight into the customer’s emotional state in real time. So let’s try to understand what does sentiment analysis mean, why is it needed for customer service and how you can implement it for your business.
What Is Sentiment Analysis?
Sentiment analysis is a branch of speech analytics which pivots around evaluating the emotional states exhibited in a conversation. In simple words, it can be defined as the process of using algorithms to analyze a certain piece of text in order to determine whether the writer’s perception towards a specific topic is positive, negative or neutral.
Application of sentiment analysis in business scenarios can help to identify the emotional states of customers to enable brands to predict and plan for future actions. Usually, sentiment analysis runs on NLP (Natural Language Processing) which assists machine to understand the text and looks for specific keywords. Machine learning algorithms then enable the program to learn from historical text documents to predict the different emotional states – positive, negative or neutral.
How Does Sentiment Analysis work In Customer Service?
Machine learning combined with artificial intelligence can help in determining the tone of customer expression by looking for specific keywords in the text message through various passive channels like email, chat, social media messages. This helps to analyze and manage customer experience across different touch points throughout their journey with the brand.
Sentiment analysis works best for a contact center when combined with predictive analytics. A smart call center software can use sentiment analysis algorithm to handle the calls in an intelligent manner by routing the calls to the right agents at the right time and automating ticket prioritization process to improve agent productivity and ensure better customer experience.
Social media is an increasingly used communication media to seek customer service, and sentiment analysis can be used for social listening to watch for complaints about specific issues. This would help your customer support team to identify issues and take steps to alleviate customer problems in a proactive manner.
A simple chronology of how sentiment analysis works in a contact center includes of following steps:
- Customer Raises a Ticket – Customer sends in a support query via passive channel like email, chat or social media.
- Artificial Intelligence Analyzes the Text Expression – Algorithm set for sentiment analysis looks for specific keywords and phrases to determine customer’s emotions.
- Business Rules are Triggered Based on Customer State – Business rules like ticket routing or prioritization are initiated based on the analysis of the customer’s sentiments.
- Context-Based Personalized Support – The agent understands the current emotional state of the customer and would cater to his need in an empathetic manner to avoid further aggravation and provide the customer with the best possible service.
Why Sentiment Analysis Needed For Customer Service?
Since sentiment analysis can determine whether a text expression is positive, negative, or neutral, it can help by furnishing rich insight into complex customer service issues. When implemented in the customer service domain, Sentiment analysis calculates the mood of the customer based upon the last interaction the customer had with a customer service agent. It takes into account specific terms and phrases across all passive mediums of interaction such as Facebook post, Twitter post, Email and calculates the mood of the customer. This prior understanding of customer mood empowers customer support team to instantly identify and take proactive action on the critical issues, to avoid any customer aggravation.
Let’s have a quick look at the various benefits of sentiment analysis in the customer service domain:
Understand Your Customers Better
Sentiment analysis in contact centres can provide real-time insight into customer’s perception about your brand, product and services which will guide your customer service team to provide better support. Understanding your customer’s emotions will lead you to analyze their pain points early and work in the right direction to resolve their issues quickly.
Personalize Customer Engagement
When your agents know how your customers are feeling before entering into a conversation they can smartly analyze their next move and deliver personalized experiences for customers. When customers are treated in an empathetic manner, you are sure to win their loyalty. Empathetic customer service gives your customers an assurance that your brand cares for their needs.
Prioritize Customer Service Tickets
Customer support system can use sentiment analysis to automatically categorize customer support tickets as “Highly Critical” to “Not Critical” depending upon the sentiment of the text mentioned in the ticket based on the various terms and phrases. Therefore support agents can then prioritize the tickets and focus their effort and time on highly critical issues first.
Improve Agent Engagement Skills
Measuring the sentiment trend over a period of time can help in identifying the gaps in agents skills and provide insight for agent training. When the agents have such feedback it becomes easy for them to understand where they lack in driving empathetic customer service . Therefore, sentiment analysis empowers them with real-time sneak-peak into customer’s mood to prepare for the engagement beforehand.
Boost Brand Reputation
When service team already knows about your customers’ current mood and their entire buyer journey,, they can plan their next action and engage with customers accordingly. This avoids any chance of escalations. Customers are delighted when their issues are resolved quickly without and happy customers tend to stay loyal with a brand, resulting in better brand preference.
With increasing complexity of customer service domain and rise of technological advancement, sentiment analysis is becoming more of a necessity to ensure impeccable customer experience. Explore the features of Customer Service Software.