How Can Sentiment Analysis be Used to Improve Customer Experience?

According to the 2022 Customer Experience Index, customer experience quality fell for 19% of brands, which is the biggest drop since the survey began. And since 62% of customers expect a seamless, personalised experience with every interaction, which opens up a big opportunity for organisations who are serious about customer experience. Enter sentiment analysis.

Sentiment analysis is a powerful tool that helps analyse and interpret what your customers are saying. This may be in the form of social media posts, reviews, customer support interactions, or just any way customers are speaking to or about your business. Analysing this data means that you can better understand how your customers feel. Through the use of natural language processing (NLP) as well as machine learning algorithms. These tools can help detect the emotional tone of a customer’s message to show whether it’s positive, negative, or neutral.

The use of sentiment analysis can benefit customer phone support teams to help support agents identify and address customer issues more efficiently. This helps bring faster resolutions and better customer experience.

In this article, we’ll go over the importance of implementing sentiment analysis in customer support. We'll explore some examples of companies that have done so successfully. We’ll also talk about common challenges and ways to navigate those.

Even with the advent of texting, social media, and other communication channels, phone support remains one of the most important links between you and your customers.

Research shows that 61% of people prefer to speak to someone over the phone when they need assistance. So ensuring that your phone support function is running as efficiently as possible is key.

Sentiment analysis helps gauge the emotion of customer interactions so that you can provide better phone support. This analysis is done in real-time or asynchronously. sentiment analysis enables customer support agents to understand customer needs and emotions better. This results in more personalised interactions and increased customer satisfaction.

One of the main benefits of using sentiment analysis is that support agents can better understand where the customer is coming from. Agents can identify whether the customer is happy, frustrated, or angry. This helps them, allowing agents to tailor their responses to match the customer’s emotional state. With this personalised approach, customer phone support teams can reduce customer frustration.

Keeping customers happy is vital, especially in an era where 23% of all customers are either neutral or dissatisfied with the current state of customer service. These unhappy customers are at a high risk of leaving your company for another that provides better service.

Put simply, understanding the sentiment of your customers helps agents understand customer needs more efficiently. By examining how customers feel during their interactions, an agent can spot recurring problems and use tried and tested methods to resolve those. As a result, the support process becomes smoother. This reduces wait times and boosts customer satisfaction.

Furthermore, sentiment analysis helps support teams create more personalised interactions with customers. According to Zendesk, elevated personalisation can help customers feel “more valued.” When they feel more valued, it can lead to increased brand loyalty and potentially more sales.

Sentiment analysis is a great tool for customer phone support teams and contact centres by providing agents with a better understanding of customer needs and emotions. Agents can personalise their interactions and improve customer experiences across the board. Companies that use sentiment analysis are more likely to attract and keep loyal customers. This can ultimately lead to increased revenue and growth.

So, exactly how can sentiment analysis be used to improve customer experience? The process doesn't need to be intimidating. Let's go over it together:

1. Data collection – The first step of the process involves using sentiment analysis to collect quality data. This can be done via call recordings and transcripts. It can also be done as chat transcripts and social media interactions. The more customer data you can collect, the more helpful the output will be. This can also help with quality assurance efforts.

2. Analysis of Customer Sentiment – Once the data is collected, it needs to be fed into the sentiment analysis tool for analysis to detect the tone of a customer’s communication and whether that message is positive, negative, or neutral. More advanced systems will automate this, and process the data automatically after a call, or even during, as a real-time process.

3. Understanding Customer Sentiment – But now what? Well, now it's time to take the data and do something with it. First start on an aggregate basis and see how your customers are feeling in general, then drill down into specific teams and see how sentiment varies. You would expect sentiment to be more negative in the complaints department but look at other departments and see how this changes. Track sentiment over time and analyse when it spikes and when it dips. Does sentiment increase during holiday periods? Does it decrease in the winter versus the summer? You'll need to closely analyse the data and find out what the drivers are, and how you as an organisation can mitigate or capitalise on this. The takeaway: identify trends and patterns.

4. Making Changes - Once you've worked out what's impacting your sentiment you'll need to put together an action plan. To do:

• Prioritise negative sentiment interactions and address them promptly to prevent customer dissatisfaction.
• Identify recurring issues and take steps to resolve them at their root cause.
• Identify positive sentiment interactions and acknowledge and reward customers or support agents for excellent service.
• Monitor sentiment trends over time to evaluate the effectiveness of process improvements.
• Continuously make changes to internal processes based on data and feedback from sentiment analysis

While sentiment analysis can be a powerful tool for customer phone support teams, it does not come without its challenges. To successfully use sentiment analysis, an organisation must first consider three key issues:

1. Data privacy and security
2. Potential biases in machine learning algorithms
3. Balancing automation with human intervention across customer interactions.

With data privacy and security, companies must be sure that they are collecting customer data ethically. This means complying with data privacy regulations such as GDPR. It also means protecting company and customer data from cyber threats. To do this, businesses must have strong security measures in place. Such as encryption, firewalls, and access controls in place.

It's also important to address potential biases in machine learning algorithms. Sentiment analysis relies on machine learning algorithms to analyse customer data. Because of this, organisations must ensure their algorithms are as impartial as possible. This means algorithms must be trained on diverse data sets and have regular reviews and updates. This will help ensure that your sentiment analysis results are accurate and unbiased.

Companies must also balance automation with human intervention across customer interactions. Sentiment analysis is capable of automating some aspects of customer interactions. But, companies must also ensure a balance between automation and human judgement. Human agents can provide a more empathetic and nuanced response to complex customer issues. So think of using sentiment analysis to guide your humans in the right direction, rather than being a completely standalone tool.

The feedback that sentiment analysis can deliver is rapidly becoming a vital tool for many customer-centric companies. Research from Bain & Company showed that over half of the companies interviewed had already adopted sentiment analysis technology, with this adoption is expected to increase to 80% in 2023.

For example, Second Nature, an organisation in the healthcare industry. They weren’t able to get the full picture when it came to the performance of their phone support teams. They didn’t have insights into the basic metrics needed to ensure that their customer support teams were meeting their customers’ needs. Through the use of sentiment analysis their managers were able to select only the calls with a certain sentiment - negative, neutral or positive. Then drill down into the drivers behind these and strategy can then be developed to resolve the problem.

Customer support teams can improve efficiency, and increase customer satisfaction through the use of sentiment analysis to aid in understanding customer calls.

To summarise:

• Sentiment analysis can make your customer support team more responsive and efficient. This leads to reduced wait times, increased customer satisfaction, and greater brand loyalty.

• Sentiment analysis process: collect data, analyse sentiment, understand sentiment, make changes to internal processes. Easy!

• Ensure that you are taking the correct measures to protect company and customer data. Use sentiment analysis to help your human agents, not replace them.

• Managing customer expectations can be a difficult task. But, effective sentiment analysis can provide a window into customers’ emotions and preferences allowing you to better serve your customers and reduce churn.

• Case studies show how effective sentiment analysis can be in providing great customer experience. This is especially true when properly integrated into business operations.