To grow and stay ahead of the competition, SMBs must pay attention to how the market perceives their products and services. Has the product met their needs? Is there a specific problem faced by multiple customers? Is there a particular feature they value the most? The answers to these questions illustrate customer sentiments towards your business.
There are different channels through which you can access this data—online reviews, mentions on social media platforms and news articles, survey responses, customer service calls, etc.—which means you can collect the information you need fairly easily.
Going through all the data and analyzing customer sentiment is where the challenge lies. For example, recording calls and listening to all of them to understand what customers think is time-consuming. That’s where AI sentiment analysis tools can help.
Let’s dive into the relevance of sentiment analysis in today’s business environment, how sentiment analysis works, and the role that Artificial Intelligence has to play.
What is sentiment analysis?
Sentiment analysis refers to understanding the emotions expressed in words to assess the opinion of an individual or a group. It can be used to evaluate spoken words or written text. At its core, sentiment analysis evaluates a body of text as positive, negative or neutral. Here are a few examples:
I like my new software — positive sentiment
I got a new software — neutral sentiment
My software does not process information fast enough — negative sentiment
Sentiments may also be graded as very positive, positive, neutral, negative, and very negative.
To do this, subjective information is extracted from customer-generated text. It must then be analyzed while keeping the context of the conversation in mind. Take these two statements for example:
The dinner service was great!
Great! I’ve waited an hour for a table and it will still take a few minutes more.
‘Great’ may be identified as the emotional keyword for both statements but the inference is different. The first statement is positive while the sarcastic context in the second highlights a negative sentiment.
AI and sentiment analysis
Your customer service team probably handles 100+ calls every day. Even if you were to rely on call recordings to assess customer sentiment, keeping up with the daily call volume would be next to impossible.
Businesses intent on getting an unbiased view of customer sentiments and prefer to leverage Natural Language Processing (NLP) technology. It automates and speeds up the process while delivering more reliable results.
These AI tools use specific algorithms to identify and extract keywords and scan them against a dataset to evaluate the sentiment behind them. Customer sentiments are graded as +1 for positive sentiments, 0 for neutral statements and -1 for negative sentiments. AI sentiment analysis tools can also go beyond this to detect specific emotions such as frustration or anger, urgency and the customer’s level of interest.
What is an example of sentiment analysis at work?
Let’s say a women’s fashion brand sells 100 pieces of a dress. Evaluating the sentiment expressed in reviews and calls with the customer service team shows that customers love the dress for its length. When it's time to design the next collection, this data will prove invaluable.
How does sentiment analysis work?
Sentiment analysis tools usually take one of three paths to analyze text:
1. Rule-based analysis
Models following this approach rely on libraries of scored keywords and manually set parameters. For example, keywords may be scored as:
Great = 1
Very good = 0.9
Good = 0.7
Okay = 0
Bad = -0.5
Very bad = -0.8
The number of keywords used in a body of text is first identified. A sentiment score for the content is then calculated according to these rules.
Rule-based analysis is easy to set up and gives SMBs a good overall picture of whether customer sentiments are positive or negative. However, the inability to understand sarcasm and metaphors may skew analytics. For example, let’s say a customer says “you’re killing it”. Rule-based analysis may mark this as a negative sentiment given the use of the word “killing”. But, it’s a compliment!
2. Machine-learning analysis
For this approach, sentiment analysis models have a keyword database and work on learning which words have positive and negative sentiments. Once the model has been sufficiently trained, it may be used to analyze new datasets. These deep-learning models are more complex but more accurate in terms of sentiment classification.
This approach is widely preferred due to its ability to detect irony, sarcasm, etc., and deliver more accurate results. That said, it is not very well suited to small datasets.
3. Hybrid analysis
This approach combines rules and machine-learning techniques. The sentiment analysis tools have a database of scored keywords and continue to learn and update this database as they process more data. Hybrid models enjoy the flexibility of customization but require less training.
What is sentiment analysis used for?
Sentiment analysis can be useful in many ways:
Customer support management
Customer support teams typically receive very high call volumes. Many of these calls may be forwarded to voicemail. Rather than having to listen to each voicemail to assess the complaint, sentiment analysis tools can be used to identify issues and prioritize responses. This can also help route tickets to relevant teams.
Navigate a potential PR crisis
A one-off complaint isn’t usually an issue. But, 1,000 agents dealing with the same complaint from different customers can become a PR crisis. Sentiment analysis algorithms that work on real-time calls help identify such instances, allowing brands to proactively respond to an issue before it becomes a bigger problem.
Measure customer perception of a product
Sentiment analysis is very helpful when you want to understand how the market perceives a new product. Reviews and brand mentions can be searched for specific keywords and their use to understand general sentiment. This helps identify features that customers appreciate and those that need improvement.
No brand can operate in isolation. Along with using sentiment analysis tools on your own brand mentions, they can be used to track social media mentions or survey responses that include competitor names. You can get a better understanding of how customers perceive these brands and use the insights gained to improve your own communication.
AI sentiment analysis tools can help evaluate customer service agent performance. Understanding the customer sentiment of calls handled shows how well agents have resolved queries. Similarly, assessing the sentiment of messages and email text helps evaluate the agent’s tone and could provide insights into issues hindering their productivity. In turn, these data help organizations develop better training programs and drive employee engagement.
What are the benefits of sentiment analysis?
The benefits of sentiment analysis are spread across departments. Some of the most significant among them are:
Easier identification of high performers
Relying solely on basic reports on KPIs such as call duration does not give an accurate picture of agent performance. A long call may either indicate that an agent is ineffective at handling issues, or reflect their ability to show empathy in customer service.
Sentiment analysis helps understand whether the calls handled by your agents are associated with a positive or negative sentiment. Agents who continuously deliver positive customer experiences can be recognized as high performers and rewarded accordingly. This is a great way to drive continued motivation and encourage others to aim high.
Efficient quality control
Team leaders do not have time to listen in to all calls handled by their agents. Leveraging customer sentiment analysis tools on recorded calls and call transcripts helps identify negative customer experiences. This gives supervisors a better idea of where to focus their attention and use their time more efficiently.
Actionable insights into customer experiences
Customer service calls are often followed by a survey, yet only a certain percentage of customers will complete one. As a result, businesses are not always able to carry out a comprehensive analysis on the quality of customer experiences. Using AI for customer service helps SMBs go a step beyond and analyze each call based on the type of customer experience provided.
It also makes it easier to identify areas that companies need to focus their efforts on to improve the customer experience. For example, if ‘long wait times’ are a common complaint, you might need to onboard new agents to handle the call volumes.
Insights into product expectations
Though the calls may be handled by customer service teams, the insights provided by sentiment analysis tools can benefit the product development team as well. Let’s take the example of a furniture brand’s customer service team who frequently hears the phrase ‘chipped polish’ in customer conversations.
This brings the product development team’s focus to polish quality. Improving this aspect can make the product more appealing to customers and thereby increase the conversion rate.
Easy tracking of market trends
To stay ahead of the competition, brands must keep an eye on market trends. Surveys and research studies can be supplemented with sentiment analysis results. This helps identify trends and plan ahead to handle market situations.
If needed, they can make changes to their supply chain, prepone or postpone launches, make schedules, and so on. Brands can also anticipate how the market will respond to new products or changes in an existing product line.
Optimization of resources
No matter how big your customer service team is, there is always a limit to the resources available to you. Time is limited too. When it comes to reacting to market trends or addressing customer concerns, the more responsive you are, the more beneficial it will be for your brand.
Sentiment analysis automates the feedback process to free your agent’s time for other tasks, which in turn saves them time.
Unbiased brand analysis
When we talk, it’s not just the words being used but also the tone and context that influence meaning. A statement may be seen in many different ways depending on the experiences and biases of the person analyzing it.
Human bias may tag a statement like “Good service but expensive” as negative because of the price factor and ignore the overall positive sentiment. Using an AI-based sentiment analysis tool helps overcome such biases. These tools can gauge the overall sentiment while keeping track of the details.
Choosing a sentiment analysis tool
Given the benefits of automated text sentiment analysis, it is important to find the tool best suited to your business. There are many different sentiment analysis tools available on the market. In addition to the cost of running a sentiment analysis tool, you need to consider the following:
1. Ease of use
Look for NLP tools that are easy to set up and require minimal training before they can be put to work. Also, pay attention to their processing power, especially if they will be used to handle large datasets.
2. Scope of analysis
With most businesses using a VoIP platform for their communication, basic analytics are easily available. Your AI tool should be able to supplement this and give you a more well-rounded picture of service quality.
For example, Aircall AI gives supervisors access to a transcript of each call as well as an AI-generated summary, a list of the key conversation topics and the talk ratio. This can be integrated even further with sentiment analysis tools.
Human language is constantly changing. There’s new slang almost every day. Hence, the NLP tool you pick must be able to keep up with expanding vocabularies and train itself to accurately analyze customer opinions.
To maximize efficiency, the sentiment analysis tool must be easy to integrate with your preferred communication platform. This automates the system and allows it to seamlessly analyze every customer interaction.
To sum it all up
Automating sentiment analysis with AI tools helps organizations get a clearer, unbiased view of how the brand and its products are perceived by customers. Machine-learning can benefit all departments, from product development to customer service. That’s why it is important to choose the right tool and integrate it with your communication platform at the earliest.
Try Aircall for free for 7 days and see how our telephony software can optimize your customer support operations.
Published on February 26, 2024.