A good product alone is not enough to succeed in any form of business. Products and services are becoming increasingly commoditized and customer service has emerged as the only true differentiator.
In the words of Maya Angelou; “People will forget what you said, people will forget what you did, but people will never forget how you made them feel.”
Businesses across industries have recognized how this statement applies to them and have started paying more attention to meeting customer expectations. It isn’t easy. Customers want personalized support; they want their questions and complaints resolved instantly and expect businesses to be available all the time.
Understanding Customer Sentiment
When these expectations aren’t met, clients do not shy away from expressing their opinions with a post on your social media pages or an online review. And, they’re not talking only to you; they’re telling everyone else what they think too. The good news is that happy customers also make public declarations to share stories of their good experiences.
Listening to what clients have to say about your customer service and understanding their sentiments towards the brand is the best way to go. This can be done by grabbing data online or by sending out surveys.
Are customers happy or sad?
Are they excited when new products are launched?
Are any of your customers angry?
Keeping track of your customer’s emotions is critical to making data-driven decisions to improve customer experiences and maintain brand loyalty.
This is where Customer Sentiment Analysis comes in. Plus, there are tools to help you perform these analyses in the most effective way.
What is Customer Sentiment Analysis?
Customer sentiment refers to the emotions expressed about your brand, product or customer experience. At its core, it could be positive, negative or neutral. Analyzing what your clients say to bracket it into 1 of the 3 categories is termed as Customer Sentiment Analysis. Analyzing everything your customers have to say and keeping track of it is next to impossible. Hence, it must be automated with a tool.
Customer Sentiment Analysis typically requires a tool of Natural Language Processing (NLP) models with specific algorithms to mine these opinions. The algorithms pick up keywords from a dataset to indicate sentiments.
Let’s look at a few examples of customer reviews and how they would be analyzed.
“My issue was resolved quickly” – positive customer sentiment from a happy customer
“Delivery was late” – negative customer sentiment from an unhappy customer
“It is a colorful dress” – neutral customer sentiment from a customer that doesn’t particularly like or dislike the product
In addition to this dictionary-based approach, advanced algorithms as used by Lettria, the best NLP tool, may also capture nuances to identify more specific emotions. Let’s say a customer said the service was a “perfect mess”. The 2 words have contradictory implications on their own but algorithms that use machine learning may categorize it as bad sentiment based on the 2 words being used together.
How to effectively perform Customer Sentiment Analysis
The best and most common mediums used to track customer sentiment are social media, customer feedback forms, surveys, and customer reviews. Natural Language Processing (NLP) tools scan each brand mentions to extract meaning and identify customer sentiment.
In its simplest form, this would involve scanning for keywords from a dataset. However, this may not always be enough.
Let’s take an example; “Great product but it was delivered late”. ‘Great’ is a positive word but ‘late’ is negative. Hence, the NLP should also be able to identify the context in which a word is used.
Ideally, an NLP tool needs to break the Customer Sentiment Analysis into 2 steps. Here is the best model:
Step 1: Grade keywords
The tool’s algorithm must first identify relevant adjectives and adverbs and give them a score according to a graded sentiment analysis. This usually ranges from -1 to 1 with 0 being an indicator of neutral sentiments.
For example, let’s take 2 simple texts, “the product is good” and “the product is very good”. Both texts indicate a positive sentiment but the addition of the word “very” in the second statement would increase the value of the emotion.
Step 2: Ascertain the value in the statement
Next, to ensure that the customer sentiment is understood accurately, the tool’s algorithm must identify the context of the sentiment. This is dependent on a framework of different emotional values such as annoyance, embarrassment, gratitude, pride, surprise, etc. This helps analyze more complicated statements like, “Great show! I had to wait 45 minutes for a reserved table!” The text ‘great’ may be identified as a positive sentiment, but a contextual analysis will show that the overall customer sentiment is negative.
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What can Customer Sentiment Analysis tell you about your existing customer service?
According to a study, 25% of customers will switch to a competitor brand after a single bad experience. To make matters worse, 60% of consumer survey respondents said that they would not use a business with negative reviews. Thus, poor customer experience is a cost that can snowball.
Tracking customer sentiment across the internet can help your customer support team take quick action and be more personable with customers. It can also help you define your Net Promoter Score (NPS). Let’s dive into a few insights Customer Sentiment Analysis has to offer.
Customer Sentiment Analysis helps you get a better picture of what your clients are asking for. This may be conveyed directly in feedback forms or indirectly by mentioning specific aspects of your customer support in their reviews.
For example, customers of a modular furniture company may say that they love your product but it takes a while to assemble. This is adequate to infer that customers want clearer instructions or simplified construction with fewer components.
Such audience insights can help the product and support teams make necessary changes to give customers a better experience.
The most common issues with customer service
Given that client expectations are ever-changing, no company can claim to have consistent, issue-free customer service. That said, as a customer-centric company, you must constantly strive to identify and rectify issues faced by your customers. Customer Sentiment Analysis helps with the first part of this task.
As it analyzes user-generated content, the application can also highlight keywords that occur frequently. Let’s say, there are 100 reviews of a hotel that mention “limited breakfast buffet options” and 20 that mention “expensive”. This tells you that pricing is not as big an issue as the breakfast menu. Adding a few dishes will probably make your customers happier than reducing the price.
What your customers feel about customer service agents
User-generated content that mentions specific customer support agents by name helps identify the most helpful agents as well as those who require additional training. Similarly, if reviews from one state are full of praise while those from another state have a more negative note, you could infer that the difference is because they have been serviced by different teams. This knowledge lets you take appropriate action for customer support training.
How your customer service agents feel about customers
Analyzing user-generated content can help you measure the performance of your customer service agents. If a customer talks about sending repeated emails without receiving a response, it could indicate an apathetic attitude. Similarly, when customers talk about your brand online, your customer support agents may respond. The tonality of their response will indicate their sentiment towards your customers.
How a change in service makes customers feel
How does a company know they’re on the right path to meeting customer expectations – by asking the customer. Let’s say the hotel mentioned in the example added 5 dishes to its breakfast menu.
A customer sentiment analysis over the next few months showed 50 people mention “excellent breakfast” but also showed 30 mentions of “insufficient English breakfast options”. This shows that the number of options available is now quite sufficient but changing a few dishes to an English cuisine may improve the experience further.
How your service compares to your competitor’s service
It’s not enough to know what customers are saying about your brand, you should also know how your support compares to your competitors. This often becomes apparent in review websites with comparative feedback charts. For instance, a phone service company could see how many people considered their plans expensive as compared to competitors or how many people were pleased with the call quality offered.
How to use Customer Sentiment Analysis to improve customer experience
As listed above, Customer Sentiment Analysis tells you a lot about the existing state of customer experience. It pays well to use these insights to discover new ways of keeping customers satisfied. Statistically speaking, you could increase revenue by 10-15% by improving customer experiences. The question is how can this data be used?
To improve products and processes
Customer Sentiment Analysis helps you understand how customers feel about your product and helps pin-point problem areas. Once you’ve identified the issue, you can work on rectifying it. A simple example - If customers complain about receiving damaged products, you know you need to work on improving your packaging.
Even when you offer great support, Customer Sentiment Analysis may help you identify opportunities for further improvement. Let’s say a customer said “I love shopping here! When are you opening a branch in Delhi?” The customer is happy for sure and maybe looking at opening a store in Delhi is the ideal next step for your brand…
To reward exceptional service to raise customer service standards
To keep your customer support team motivated, exceptional work should be publicly recognized. You may not be able to listen in to every call or hear what your staff has to say to every customer. But, when customers commend your team members by name, you know they’ve done something right. Appreciating their efforts and rewarding them for the same goes a long way toward keeping your team motivated and setting a standard for them to live up to.
To personalize responses
A McKinsey study showed that 78% of customers felt personalized communication made them more likely to make repeat purchases. Customer Sentiment Analysis tells you what your customers are expecting and how they have reacted to your service in the past. Equipped with this information, customer support agents can personalize responses and improve customer satisfaction rates.
While customers expect brands to be available 24x7, not all brands can afford to have a full team round-the-clock. Many incoming customer service conversations are handled by chatbots. These bots typically have scripted questions and answers. It’s helpful when it works but when it doesn’t address the customer’s specific question, it can be frustrating. Customer Sentiment Analysis can help identify such instances so that you can edit the scripts and make them more inclusive.
To act on negative sentiments for competitors
Along with tracking your customer sentiments, you can measure the customer sentiment towards your competitors. When you identify an issue that a competitor is getting flack for, you can do two things – first, check yourself to ensure your customers aren’t facing the same issue. Second, use it to market your services.
For example, if customers feel a competitor’s delivery timelines are too long, you can use shorter timelines as a marketing strategy.
To identify influencers
Today, people often buy products just because it was recommended by a trusted influencer. To really take advantage of this new channel for your business, it’s important to find the right influencers for your brand. This is where tracking sentiment can help. See which influencers are talking about your category of products and how their audience feels about what they say. You can then identify and engage with them, as well as their fans. Influencers may also be found in your customer base, who can be identified with their NPS.
To manage a PR crisis
When it comes to a PR crisis, the earlier the issue is addressed, the faster it will die out. Customer Sentiment Analytics allows you to track brand mentions across platforms and identify anything from a sudden increase in brand mentions to a peak in negative emotions on a review site. This allows you to tackle it before it worsens and, in turn, shows customers that you care about their opinions.
Examples of how to use Customer Sentiment Analysis to improve customer experience
Identifying negative sentiment to respond carefully
Customer Sentiment Analysis would identify this message to customer service as having a “negative customer sentiment”. It will help customer service agents know to respond to the customer in a careful way. You can also filter through all negative customer service messages and NLP will show you what aspects of your company customers are upset with.
Identifying positive sentiment to improve customer delight
Here’s an example of a message to customer service that displays a positive customer sentiment. The customer seems to be happy with the product and in a good mood. A customer sentiment analysis tool could help customer service agents know to match the positive energy. It could also help management know if customers are generally upset with a given product or service or if they just need more support. With this information, they can take a strategic step to change something in the product or service or decide to simply provide more support, supporting documents, training, etc.
Identifying sentiment of your agents to measure their performance
Here is an example of a customer service agent showing positive sentiment towards the client. Measuring this helps management measure performance and helps them know if their team is responding to customers properly.
Use Sentiments To Deliver Positive Customer Experience
Your brand is the sum total of your products, customer service, people, visual identity, advertising and many other such elements. A study found that 73% of customers love a brand simply because of friendly customer service. If you haven’t started yet, this should be a big enough reason to pay attention to how your customers feel about the brand.
The insights offered by Customer Sentiment Analytics can give you an overview of the general sentiment associated with your brand as well as zero‑in on the underlying issues and opportunities for improvement. That said, performing Customer Sentiment Analysis isn’t always as simple as it seems. Dealing with the sheer number of opinions floating on the web and the different types of slang used can be daunting. But, not when you’re using Lettria.
Give Your Brand The Lettria Edge
Lettria’s software goes beyond simple textual analysis and allows your NLP to identify multiple sentiments with relevant contexts from the same statement. This feature software helps you get the best, more accurate reading of what your customers feel.
With 15 pre-trained and customizable multilingual models, you can focus on training AI models to identify keywords and emotions specific to your business and industry. Take a closer look at our software to see the role Lettria’s features can play in improving your customer experience strategies. Read more here or contact us for more information.