What is Customer Sentiment Analytics?

Author: Wil Sokanovic | Date: 23/05/2022

Customer service is a vital component of any business, often functioning as the primary way consumers connect with a brand. It also directly impacts success — often determining how willing consumers are to buy from a given business.

This is backed up by a recent survey which suggests 89% of consumers are more likely to make a repeat purchase after a positive customer service experience.¹ As a result, businesses are increasingly focused on improving their customer service outcomes.

Delivering exceptional customer service isn’t easy. Customers aren’t always explicit about their feelings, often hiding their true thoughts, which makes it difficult for agents to provide a comprehensive and personalised experience.

As with many historical call centre problems, technological advances provide a solution. In this instance, customer sentiment analytics is helping contact centres better understand their customers and tailor their approach accordingly.

In this article, we’ll explore the concept of customer sentiment analytics and look at how to improve customer service outcomes.

Suggested reading: If you want to read more about the impact technology is having on contact centres, check out our blog — Contact Centre Future Trends: 2022 and Beyond

Defining Customer Sentiment Analytics

Customer sentiment analytics is the utilisation of software to better understand human emotion. It’s the process of interpreting customers’ unspoken feelings through conversational analytics, applying artificial intelligence (AI) to decipher what they want and need.

The goal of sentiment analytics is simple — provide organisations with a complete picture of the sentiment behind their customers’ words. This makes it possible to ascertain:

What they like
What they don’t like
What they feel positive about
What they don’t feel positive about

Armed with this information, companies can make better-informed decisions about their products and services, marketing campaigns, and, most importantly, customer support services.

How it works

Customer sentiment analytics uses a range of tools to discern the emotion behind the messages and calls that occur across contact centres. The process is based on speech and voice analytics:

Speech analytics: Determines what’s being said by transcribing spoken conversations into readable text to identify precise words and phrases.
Voice analytics: Goes one step further, using AI and machine learning (ML) to pinpoint specific words and determine the customer emotion and sentiment embedded within.

Through this combination of words and sentiment, contact centres can obtain a much better understanding of their customers’ voice. The process of determining whether a customer-agent interaction was positive, negative or neutral, is simplified, and further insights into the discourse and its overall quality can be obtained.

The benefits of customer sentiment analytics in contact centres

In today’s highly competitive business environment, organisations are constantly looking for ways they can gain an edge over their competition. Delivering outstanding customer service outcomes on a consistent basis is one way of doing just that.

For that to become a reality, contact centres need to be equipped with the latest software advancements. Customer sentiment analytics is just one of these, but it has clear benefits that can significantly impact outcomes.

Let’s take a look at some of these benefits in more detail.

#1 Identifying opportunities for operational improvement

Better insight into communications between customers and agents has long been a desirable yet unattainable outcome in customer services. Previously, this required manual processes, whereby each call is listened to and analysed, taking up considerable resources.

However, customer sentiment analytics has made these processes easier and far less labour intensive. Contact centre managers and team leaders can now obtain a far better understanding of the emotions behind customer utterances using a fraction of the resources.

As a result, it is now much easier to identify when the customer is unhappy, frustrated or otherwise unsatisfied with the level of service they are receiving, even if they don’t vocalise their feelings. For example, during any specific call, it might be that the customer:

Feels that the agent isn’t grasping their query quickly enough
Is unhappy at the steps the agent is taking them through
Wants a more personalised experience they can’t get with an agent reading from a script

Customer sentiment analytics and call centre speech analytics has made it much easier to identify when instances like the above occur. Subsequently, managers and team leaders can better understand where they need to make operational improvements in their contact centre.

#2 Improving customer service

On top of making it much easier to identify where there are opportunities for improvement, customer sentiment analysis can also facilitate the delivery of better customer service outcomes.

By honing in on the instances where customers become dissatisfied or unengaged that result in a bad experience, contact centre managers can start to implement changes to ensure their agents are equipped to meet demand.

For example, customer sentiment analytics might identify a specific part of an agent’s script that customers aren’t reacting well to. Or, it might highlight an area of customer service delivery a range of agents in the contact centre are struggling with.

In these instances, managers and team leaders can make script changes based on customer data provided by sentiment analytics, and arrange relevant follow-up training sessions to help improve customer experiences.

As a result, the insights obtained can be invaluable in improving crucial contact centre KPIs, including:

First Call Resolution (FCR)
Customer satisfaction
Average Handling Time (AHT)
Agent turnover rates

#3 Protecting and enhancing brand reputation

One of the main reasons customer service has become so pivotal is because of the way it impacts how customers feel about your brand. In this way, it’s a tool that can be leveraged to improve a company’s reputation and, subsequently, its overall performance.

By monitoring customer-agent conversations and improving understanding of the sentiment behind language choices, businesses can get a better sense of how they are perceived.

They can also take rapid and proactive action when an unenthusiastic sentiment begins to spread due to negative experiences, preventing any long-term damage. This can be done by improving customer service delivery, or making adjustments in other business areas, such as marketing strategy.

This can help to enhance brand perception across the board. It can also increase customer loyalty, retention and referrals, with a rise of just a 5% rise having the potential to increase profitability by as much as 95%.²

Once again, technology holds the key. Software that makes it possible to record, store and access all customer-agent interactions allows contact centres to identify and monitor how consumers view their brand, and then track the impact of changes they implement.

Understand your customers better and drive contact centre success

Understanding your agents and the individuals they engage with on a daily basis is key to contact centre success. As a result, solutions that provide a deeper understanding of the sentiment behind customer service calls is crucial. That’s where Awaken comes in.

We believe technology holds the key to providing a more comprehensive customer experience. Interaction analytics lets you listen into customers’ interactions and obtain an understanding that helps you to guide agents to the next best action, ensuring they deliver the right information at the right time, every time.

Using our platform gives you access to a range of functionality that enhances understanding of your customers and agents, including:

Voice to text: Transcription of conversations that recognises names, acronyms and seeded words.
Natural Language Processing (NLP): Account for the meaning of words within the content of the conversation.
Topic modelling: Identify conversational topics through hidden semantic structures.
Textual sentiment detection: Detect the sentiment of words — positive, negative or neutral.
Vocal emotion detection: Detect anger, sadness, happiness, neutrality, anxiety, vulnerability, misunderstanding, and complaint indicators.

Are you ready to revolutionise your understanding of your customers and start driving improved customer service outcomes? Get in touch with our team today, and find out how our Conversational Analytics could help transform your customer service delivery.