Did you know that growth-focused organizations collect more customer experience data than non-growth companies? Yes, you heard that right! These companies rely on customer experience insights to make informed decisions that can take their business to the next level. While surveys have always been a favorite tool of companies to gather valuable inputs, sentiment analysis is a much better and smarter approach to dig deeper into your customers’ minds.
It is a technique that uses artificial intelligence to detect customer sentiments and decode them, enabling you to craft delightful customer experiences. This blog uncovers everything you need to know about sentiment analysis.
What is Sentiment Analysis and What Are Its Benefits?
In simple terms, sentiment analysis uses the technique of natural language processing to determine whether the data received expresses a positive, negative or neutral tone. It is also called opinion mining or emotional AI.
Since the customer data is extremely noisy, unstructured, and pours-in from multiple channels, it is impossible to manually scan and process every message and analyze it to deliver better services. That’s where sentiment analysis comes into play.
Sentiment analysis processes all kinds of customer communication – direct feedback, email responses, comments, reviews, posts, etc. and distills the data to a mathematical score that indicates subjectivity and tone.
This AI-powered technique is more profound than the conventional NPS and CSAT metrics. It taps into customer emotions, giving you more specific and qualitative insights.
Sentiment analysis helps you understand two of the most crucial things:
- How do your customers feel about your brand and brand offerings in general?
- How do your customers feel about the experiences and journeys you offer?
With such powerful data-driven insights into customer attitudes, you can:
- Make customer-centric CX decisions.
- Identify pain points
The following screenshots from Customer Experience Tools and Trends Survey 2020 show the importance of sentiment analysis.
This survey conducted by a reputed global firm shows how impactful customer sentiment analysis is for a business.
Here are some of the major advantages of customer sentiment analysis.
Benefits of Sentiment Analysis
1. Upselling Opportunities
Sentiment analysis allows you to segregate your entire customer base into different segments, such as the happiest customers, customers with the highest buying potential, frustrated customers, and customers that are ready to leave your brand.
You can create upselling opportunities by leveraging your happy customers.
2. Deliver Exceptional Support
You can use sentiment analysis to train your chatbots to detect the right time to escalate the chat to an agent or route it to your experienced professionals. This is a great way to improve customer support and deliver delightful experiences.
3. Identify Emotional Triggers
A majority of customer actions stem from their emotions and the experiences they receive. Positive customer service can arouse positive emotions, leading to positive actions. On the contrary, poor service can spark negative emotions and actions. This can have various permutations and combinations.
With sentiment analysis, you can identify what messages and chats act as emotional triggers for your customers.
For example, it is possible that the phrase – “We will get back to you in some time” annoys customers. Or, adding emojis to a chat, message, or email triggers a friendly response from customers.
Sentiment analysis decodes customers’ emotions and actions. Using this analysis, you can make educated decisions to improve the quality of your customer service.
4. Handle Multiple Customers at One Time
It is normal for your agents to handle multiple customers at a time. However, it is impossible to manually process all the questions and answer them all at once. Sentiment analysis proves to be extremely helpful in identifying which customer conversations are going smoothly and which require swift actions.
5. Engage Positively With Customers
Sentiment analysis decodes customers’ tone, attitude, and mood, giving you the opportunity to engage with the right people at the right time. You will know who you should reach out to, what you should talk about, and which channel you should use for the same.
Connecting and engaging with customers in the right way brings you scores of benefits such as improved sales and delighted customers.
Some other benefits of sentiment analysis are:
- Tackle customer churn
- Monitor customer satisfaction
- Seamless escalation of customer issues
- Insights-driven marketing strategies and campaigns
- Improved customer experience
Let us find out how to do sentiment analysis and the different techniques for the same.
How to Conduct Sentiment Analysis to Improve Customer Experience?
There are many ways of collecting customer sentiment and processing the same for business purposes. This section covers some of the best techniques for online sentiment analysis.
Sentiment Analysis at the Top Document Level
The top document-level sentiment analysis determines the tone of a document. It has data processing workflows that include traversing a text block to assess its tone – negative, positive, or neutral. Some tools also offer a third output called NULL, which means that the text is neither negative nor positive.
This sentiment analysis method works the best when the text being analyzed is lengthy.
While the top document-level sentiment analysis can offer the overall brand perception among the customer clusters, it cannot provide further actionable deeper insights.
For instance, take a look at the following reviews. These are commendable sentiment analysis examples:
Jack: I hate this brand…poorest customer service!
Brody: I don’t like their products.. such poor quality!!
While a top document-level sentiment analysis will show that both these statements are negative, it fails to identify the reasons behind these negative responses.
Hence, if you wish to assess the overall brand perception, you can opt for the top document-level analysis.
Sentiment Analysis at the Paragraph Level
The paragraph-level sentiment analysis is more refined, as it is done at the paragraph level. So, the processor traverses different text paragraphs to find whether they have a negative or positive tone, both the tones or none of them.
It is perfect for digging into long paragraphs and lengthy text documents that come with multiple sections. Blogs, critic reviews, and detailed reviews are perfect examples.
The content consumption capability of modern customers is awe-inspiring. So, blogs continue to be a great hit among the masses. This calls for processing the blogs or other such written pieces that have long paragraphs. You can analyze the tone of paragraphs and gain a better understanding of whether the content will be a hit or flop among customers.
Word or Phrase Wise Sentiment Analysis
This is a fine-grained sentiment analysis process that works on independent lexical entities, such as words or phrases. Suppose you are the owner of a chain of multi-specialty hospitals. Now, you wish to know whether a branch is doing the right job or not. A word or phrase-level sentiment analysis can do the job perfectly.
It will look up for words such as “bills,” “broken bones,” “careless,” “medical staff,” “nurses,” and “doctors,” etc. While words such as “medical staff,” “nurses,” and “doctors,” etc., are detected as positive entities, the words – “bills” and “broken bones” are negative.
So, the processor offers a deep assessment of data blocks related to it.
Likewise, suppose you are a software company that offers bookkeeping tools. In that case, there are various phrases such as “language support,” “ease of use,” and “latest updates,” etc., that can be used to evaluate your product’s performance.
Hence, the word-level sentiment analysis offers a highly refined view of unstructured data blocks. Once you have this data pool, leverage it to improve your offerings and customer experience.
Use of Machine Learning for Sentiment Analysis
While the traditional software cannot associate individual words and phrases with a proper context, the advanced AI-powered sentiment analytics tools are equipped with contextual intelligence. Machine Learning powered analytics understand human emotions at more conceptual and relatable levels.
This proves highly beneficial in processing customer data gathered from social media where various emotions are pouring in every second.
Let’s take a few sentiment analysis examples.
The word “bloody” is generally associated with a negative sentiment.
Now, put it like this – “bloody awesome!!“; and the entire sentiment takes a 360-degree turn.
The ML-powered sentiment analysis offers an in-depth view of data as it has the ability to place individual phrases and words in different context-rich zones. It doesn’t only point out the black and white but can also distinguish between the various shades of grey-colored customer data.
Use Social Media for Sentiment Analysis
The social media population stands at 4.14 bn. This makes it a lucrative platform where you can find your customers, track their activities, and engage with prospects.
Social media is a vast data repository and one of the prominent communication channels that your customers use to ask questions, share experiences and opinions, and seek support.
A smart and intuitive social media monitoring tool like Mediatoolkit allows you to mine this business data and extract the various forms of customer sentiments.
You can gain actionable insights into the general brand perception and customer reactions to your marketing campaigns. You can even gather crucial customer feedback in the form of direct and passive social media mentions.
Take a look at the various benefits of social media monitoring in the image below.
Further, social media is a place where situations and tags can escalate in no time. Hence, it is important to respond quickly, personally, and effectively to customer concerns on social media. This keeps ugly situations at bay or even turns them into positive experiences.
Considering the immense number of social media mentions and their diverse nature, an AI-based approach like sentiment analysis is your best bet.
Real-time analysis of customer sentiments puts you one step ahead of a potential crisis and allows you to take action even before a poor customer experience goes viral.
Make Use of Customer Feedback Software
The customer feedback software allows you to collect customer feedback for sentiment analysis. NPS surveys and CSAT surveys are some of the most popular options to gather inputs for a product or service.
When you perform sentiment analysis on NPS surveys, you can go beyond the raw numbers, scores, and groups and obtain more insights-driven results. So, all you have to do is, integrate these tools into your business ecosystem and get actionable insights from customer data.
Here are some of the best customer feedback tools that might help you:
Qualaroo is one of the best sentiment analysis tools for businesses of all types. It is 10x more powerful than email surveys. The tool empowers you to ask the right questions to your customers at the right time. For example, when your customers are at the pricing page or when they show exit intent.
Qualaroo understands customer feedback to the core, categorizes the results, and offers unparalleled insights.
It does all the hard work of analyzing loads of data, so you just have to focus on responding to your customers in real-time. The analysis is also instrumental in identifying customers who are happy with your brand and those who are not satisfied with your services.
ProProfs Survey Maker comes with many powerful features, such as NPS surveys, polls, in-app surveys, reports, skip logic, and many more. It allows you to create delightful surveys in just a few minutes.
This cloud-based tool comes with 100+ ready-made templates and a large number of ready-to-use questions that make survey creation a breeze.
Other functionalities include presentation-ready reports, intelligent analytics, and data security options.
Quick Search is a sentiment analysis tool that is a part of a broad customer service platform – Talkwalker. This is perfect for social media channels as it tells you exactly how people feel about your brand’s social media accounts. It looks at your mentions, engagements, comments, and other data to offer an extensive breakdown of your customers’ social media activity.
Hence, you can plan effective and more comprehensive marketing campaigns and target your audience in the best possible manner.
Repustate offers a sophisticated text-analysis API to assess customer sentiments accurately. It can also process the short-form texts and slang like “LOL,” “SMH,” and “ROFL,” etc. Another powerful feature is its ability to process the emojis and inform whether they are used positively or negatively.
You can also customize the API rules to filter for language specific to your industry. Such subtleties can be programmed into Repustate for a holistic control over sentiment analysis.
As suggested by its name, Lexalytics is a text-analysis tool focused on explaining the reason behind a specific type of customer behavior. It employs NLP for text parsing, followed by sentiment analysis to determine the customer intent.
All the information is then represented into a shareable display that is easy-to-read and gives you rich insights into customer attitudes.
Brandwatch comes with a unique feature – ‘image insights’ that identifies images related to your brand. For example, you can upload an image of your logo, and the software will surf the internet for images with that logo. The results are compiled into a list, and you have direct access to all the places, posts, and channels where your brand’s logo is getting mentioned.
Further, it offers impressive insights into each image, such as mentioning volume, latest activity, etc.
Use the Power of Live Chat
Live Chat sentiment analysis evaluates every chat session for negative and positive mood indicators. The analysis has a score from 1-100. This scoring system allows customers to rate your chat service based on the experience received.
The live chat sentiment analysis offers you a real-time view of your customers’ perceptions about your support service. Once the chat session has ended, an in-depth analysis is performed to understand customer data and generate a reflective and detailed sentiment score.
With this deep insight into customer behavior, you can improve your chat support and steer the overall customer experience in a more positive direction. Live chat sentiment analysis automatically monitors the keywords, syntactic effect, and tone within chat conversations.
This is done on a line-by-line basis for every incoming customer message. Hence, it ensures that no key mood indicators are missed while you are interacting with your customers.
Monitor Customer Sentiments via Product Reviews and Ratings
Public forums, review websites, and news websites are also popular places for online conversations and community discussions. Product reviews and ratings left by your customers on these platforms offer a large pool of unprocessed data.
If analyzed properly, this data can offer rich insights into the market perception of your brand.
A recent survey reveals that an average customer reads at least ten reviews before making the final purchase. Doing sentiment analysis of your reviews can help you modify and upgrade your business offerings as per your customer expectations.
Aspect-based analysis, a form of sentiment analysis, gives detailed insights about different features of your product. This means you can directly view the features loved by your customers and product features that your customers hate or dislike.
Connect with Your Customers via Call to Understand Sentiments
The AI and NLP-based sentiment analysis empower you to identify honest signals and genuine feelings of your customers while you are talking to them. This means even if your agents lack the emotional intelligence to grasp the underlying tones in your customers’ voice and speech, the sentiment analysis gets them covered.
This is a unique way of sentiment analysis. With the help of a powerful tool, you can identify and distinguish between 200 voice signals to learn about the customer’s mood at a time. Further, as the data accumulates over time, it can be processed to uncover hidden patterns, problems in on-call support, and patterns in customer behavior.
This type of customer sentiment analysis is perfect for call centers and business setups where the majority of customer support is provided via calls.
While your support agent interacts with a customer, he or she can simultaneously take cues from the alerts issued by your sentiment analysis tool to offer the best resolution. This approach also helps agents keep the communication in a positive direction.
Sentimental Analysis Use Cases
Focus on Individuals to Improve Service
By focusing on the individual customers, CX leaders can reach the roots of the problems.
For example, suppose Jack leaves a negative rating on his social media handle, and 400 people react to it. If your CX staff can tackle the situation right away and resolve the issue at hand, not only Jack feels valued and satisfied, but the 400 other people will feel moved. This is how public sentiment works.
Another example is of a condiments brand. The company found that 60% of its customers on social media were emotionally connected.
That’s when it started focusing on improving its social media engagement to develop an active online community. It also took steps to encourage its customers to visit the brand’s social media accounts for information on recipes and promotions.
This boosted the company’s growth just in a matter of months.
Track Sentiments Over Time
If evaluating sentiments regarding different business aspects were not important, NPS alone could have carried the torch for all sentiment analysis methods.
However, the ground reality is different. Tracking customer sentiment over time by attaching the same to different business aspects is crucial to understand your customers to the core.
For example, suppose a sports gear manufacturing company has received five reviews related to customer service, of which two are connected to product deliveries, one is related to return policies, and two are related to new designs.
A thorough sentiment analysis tracking done over time will indicate all these reasons, helping you offer a better customer experience.
Identifying the Detractors and Promoters
A detractor is an unsatisfied customer who is likely to post a negative review. On the other hand, promoters are the customers who are happy with your brand and willingly share good experiences in their social circle.
Using sentiment analysis, you can easily identify your promoters and detractors and devise strategies that can work wonders for both types of customers.
Identify the Customer Clusters With Deeper Emotional Attachment
Customer segmentation can reveal the clusters of customers that feel more strongly about your company and its offerings. By focusing on the sentiments of such customers, you can deliver highly personalized experiences and earn some loyal brand advocates.
Further, by doing a thorough evaluation of customer sentiment in weak clusters, you can revise the brand portfolios to arouse interest and spark new trends.
Keep Track of Customer Sentiment After Rolling-out Changes
A highly famous and reputed brand without customer intelligence fears the updates and changes.
Suppose you have a bakery business that sells almond walnut cakes. Now, one day, you think about rolling out a change by adding cherries to the cake.
You prepare a big batch of such cakes, but more than 50% of them go stale, as a majority of your customers hate cherries. Besides hitting your sales, it also reduces customers’ trust in your brand.
However, if you do a sentiment analysis by gathering data about your customers’ eating preferences, you can be more confident about any such change.
Now, let us take a look at the various challenges of sentiment analysis.
Challenges Involved in Sentiment Analysis
Let us take a sentiment analysis example:
Arabella: I just love their customer support… It’s bloody awesome how they issued instant compensation for my ruined camping trip.
Dean: The camping kit was a big mess, and my camping trip was a bloody failure!!
It is common for customers to use extreme words, slang, and sarcasm while leaving comments on your brand offerings. In the example shared above, the single adjective “bloody” has been used to convey two entirely different human emotions.
However, if the sentiment analysis applications you have, fail to distinguish between the two, you will not be able to find the right customer feedback.
Apart from the challenge ambiguity discussed here, there are many other pitfalls, such as sarcasm, multipolarity, negation type, and irony, that are hard to decode. That’s because human speech and text is a highly challenging and noisy input for any type of sentiment analysis tool.
Let us take some sentiment analysis examples to elaborate on all these challenges.
“This phone has an amazing battery back-up of 20 hours.” (Non-sarcastic)
“This awesome phone has an amazing battery back-up of 3 hours.” (Sarcastic)
Negation comes in various forms:
- Words with prefixes, such as “dis-,” “non-,” and suffixes, such as “-less.”
- Explicit negation, such as “this is not good at all.”
- Implicit negation, such as “with this product, this is the last nail in the coffin for this brand.”
“While the battery back-up is amazing, and audio quality is good, the display takes away some marks from the final score.”
Such statements are highly confusing and require deep analysis of the text and speech, etc.
Finally, the amount of customer data and incoming communication from all the channels is enormous. It calls for highly efficient and advanced processors that only large businesses might be able to afford.
Sentimental Analysis Template
Check out the step-by-step process for creating sentiment analysis templates.
- Prepare training data. Training data refers to the text sample you use to train your model, such as a set of tweets. You have to export this set of tweets into an Excel file.
- Now, you have to divide this set into smaller data chunks, or “opinion units.”
- Choose a model type, such as “Extractor” or “Classifier.”
- Select a classifier type, such as “Topic classification,” “Intent classification,” and “Sentiment Analysis,” etc.
- Upload your data (Excel file with extracted opinion units).
- Train your sentiment analysis classifier by manually tagging each opinion. The model will learn by your inputs and the NLP techniques and decipher the patterns in the next batch.
- Put the model to test. Input text into the classifier and evaluate the performance. You can go back and tag more examples to train your classifier if the results are not promising.
- Finally, you can put the model to work with your own data sets and let it work on its own.
Sentiment Analysis: A Data-driven process of decoding Customer Success
By now you might have gained a good idea of how to conduct sentiment analysis.
The modern business landscape is becoming more competitive with each passing hour, and online marketplaces are becoming overcrowded with brands willing to go beyond the “new normals” of customer service.
In such a scenario, the importance of customer experience can just not be fathomed!
Providing quality products and services no longer suffice. It is now critical to understand customer preferences, emotions, reviews, and sentiments for a comprehensive pulse-check.
Among the plethora of tools and techniques available for customer insights and business intelligence, sentiment analysis is a robust dime to put your bet on! It is a powerful tool to unlock customer attitudes and decode customer success with surety!
Choose any of these sentiment analysis techniques and evaluate them against your business needs. Get the right tool with relevant sentiment analysis features and give your business a right head-start.