Customer expectations rise constantly and change frequently. Therefore, businesses need to keep pace. One of the most valuable tools to do this is customer sentiment analysis.
Even if it may sound complicated, integrating it into your processes will bring valuable insights. Helping your business to deliver better products and services and satisfy your customers’ requirements in a superior way.
What Is Customer Sentiment Analysis?
Customer sentiment is a metric used by businesses to determine the emotions, opinions and feelings of customers in relation to their brand, products and services.
Customer sentiment analysis is the automatic process of identifying customers’ emotions, what they really feel and think via advanced technology solutions.
In a word, thanks to customer sentiment analysis a company is able to examine the collective emotions, feelings and thoughts of its customers by investigating their reviews, feedback, social media posts, comments, etc.
And categorise the real sentiment as being positive, neutral or negative. To guide and fundament strategies and business development decisions.
Customer sentiment is more important than ever. Research has found that 88% of clients consider the experience as valuable as the product/service. 66% of consumers will avoid a brand in the future if they have a bad experience.
An unhappy client will share his experience with 16 other persons on average, while a happy customer will share his experience with just 9 people.
Nowadays there are powerful technologies like natural language processing and machine learning that execute the daunting task of processing huge amounts of text data to extract sentiment.
And algorithms learn to recognise patterns and correctly identify the nuances of human tones and expressions.
Now, customer sentiment analysis goes beyond individual words and is capable of detecting the context and even the intent expressed. It is able to spot irony, and sarcasm and make sense of various types of feedback.
Why is customer sentiment analysis important?
It heavily impacts business strategies and decisions.
Practically if you are listening to what customers say about your products/services, you can identify your strengths based on positive feedback. Because you know you are on the right path and you can capitalise on these advantages.
While the negative sentiments highlight where corrections are needed and what can be improved.
It helps enhance customer experience.
Let’s take a customer sentiment example. You run an ice cream boutique and via customer sentiment analysis you find out that clients are enchanted with the ice cream quality and flavours, but they are frustrated they have to wait too much time to be served.
Having this information you know that you have to take measures to minimise the waiting times and ameliorate service speed. Once this aspect is corrected, the overall experience will be much better and satisfied clients have higher chances of returning.
Most often customer sentiment analysis is a sort of magnifying glass that zooms in on details that reveal the pain points and impact the customer experience.
How to Do Customer Sentiment Analysis?
It is a process composed of the following steps:
Gather customer feedback
Collect feedback from all sources available surveys, reviews, social media comments and posts, customer support messages, etc.
But you need just the feedback that is related to the purpose for which you make the sentiment analysis. And remove the unrelated noise.
Clean the text data and process it
Now you have the data collected but it is not exactly ready for the analysis phase. You need to prepare it, and remove irrelevant items, and duplicates to get to the real valuable stuff.
You have to erase unrelated hashtags, emojis or links. Longer sentences need to be broken into smaller pieces (called tokens) to facilitate their understanding of the technology.
Plus, stemming the words (reducing words to their base for example “reading” will be reduced to “read”) to eliminate duplicates.
Classification of sentiments
The core part of the analysis means assigning the sentiment of the text into one of the following categories positive, neutral or negative.
It can be done via supervised learning or unsupervised learning.
Supervised learning means that you have to train the algorithm to recognise patterns by providing it examples from which it can learn.
Unsupervised learning means that the algorithm will figure out on its own these patterns. For example, it has to figure on its own that “great” and “decent” have different meanings.
Even more detailed sentiment analysis based on the aspect
Customer sentiment analysis has the capability of digging deep into the details beyond general sentiments.
Aspect-based sentiment analysis goes to more granular levels and delivers insights on specific aspects or parts of your product. For example, if you are a fashion house and you sell dresses for ladies, you might find out that customers love the models but are not enchanted by the fabrics.
In this case, context is paramount.
Identifying exactly what are people referring to when they say for instance “camera”. Are they talking about the features, the quality of images or the user-friendly interface? If you don’t know the context you might assume wrongly that they refer to photography when in reality they wonder about the quality of the focus.
The customer sentiment analysis process is like making a puzzle. You have to sort the pieces, arrange them in a certain way and dig into the details to understand how things merge.
Customer Sentiment Analysis Techniques
Executing a customer sentiment analysis is quite complex. But with the right methods and techniques, you can get actionable and high-value insights to drive your business expansion.
Rules-based customer sentiment analysis
Rules-based sentiment analysis is the most straightforward of the methods. The rules-based approach uses a lexical database compiled by language experts. With this approach, sentences are broken down into parts of speech and words are assigned a sentiment – either positive, neutral or negative.
Rules-based models are time-consuming to build, and they can’t adapt and evolve with changes in language, for example. Plus, the accuracy is highly dependent on the quality of the established rules. Considering this aspect, they’re very limited.
Types of rules-based methods:
- Emotion analysis
- Lexicon-based customer sentiment analysis
Emotion Analysis
Emotion analysis enables you to get more specific insights into exactly how your audience feels about different aspects of your business. It aims to identify certain emotions like anger, fear, surprise, joy, disgust, sadness, etc.
High emotionality is also an indicator of customer loyalty. Those who express strong positive sentiments and emotions are likely to remain loyal to your business.
You can use this information to identify key influencers – those with a strong positive emotional connection to your brand and a high social capital can help spread the word about your business.
Conversely, those who express strong negative sentiments and emotions have the highest risk of churning. Use emotion analysis to alert your customer support of at-risk clients so they can take action to save the customer relationship (or at least try).
Lexicon-based customer sentiment analysis
This type of analysis uses predefined libraries of words, where each word is associated with a sentiment score.
The algorithm checks these scores and adds them up to calculate the sentiment of a text.
Psychographic Analysis
Psychographics is the study of personality, attitudes, values, interests and lifestyles. When sentiment is combined with psychographic analysis, businesses can get a more complete understanding of their customers.
For example, you can discover how the various segments of your audience respond differently to your offerings or marketing efforts.
Sentiment analysis is a valuable tool for customer analytics, but it’s only one piece of the puzzle. To get the most insights into your audience, combine sentiment with other types of customer data such as emotion and psychographics.
Doing so will give you a well-rounded view of your customers that can be used to improve the customer experience.
Statistical sentiment analysis
Statistical (or machine learning-based) sentiment analysis, on the other hand, is a data-driven approach that builds models to identify sentiment.
In this approach, developers train the computer by feeding it examples and assigning them meaning. The computer then deciphers different patterns and maps them to a concept such as semantics or intent.
Supervised and semi-supervised models (which use some labelled data for their classification models) improve classification accuracy. But unsupervised models are getting more sophisticated.
While this approach requires initial training, it ultimately learns by itself. Unlike the rules-based approach, statistical sentiment analysis can adapt to new conditions.
It can also infer meaning from context, and compensate for errors in grammar and syntax, improving its overall accuracy.
The future of machine-learning-based sentiment analysis is increasingly accurate, and increasingly the preferred option as advanced models become more accessible.
Some examples of machine learning-based customer sentiment analysis methods are:
- Naive Bayes Classifier – a well-known algorithm that employs probabilities to categorise text.
- Support Vector Machines – it’s an emotional graph used for classification and outlier identification. One of its strengths is its capacity to set boundaries among categories.
- Neural Networks – they mimic our human brain to identify connections and links between sentiments and words.
Natural Language Processing (NLP) Methods
NLP models’ mission is to gauge the real meaning of the words. Their capabilities develop day passing and now they are able to grasp context, grammar, orthography and even feelings like irony.
Some popular NLP models are:
- Word Embeddings – they transform words into formats and formulas that algorithms can understand. Something like translating emotions into numbers. Such embeddings help technology understand the nuances of words.
- Recurrent Neural Networks – they are neural networks capable of understanding sequences of words and how a word influences the next one. Somewhat similar to reading the chapters of a book to get the context.
- Transformer-based models – these are advanced deep learning models able to predict the next word in a phrase. And are also capable of depicting context and getting the sense of mixed sentiments like “she likes the dress but can’t stand the material”.
Customer Sentiment Analysis Benefits
Customer sentiment analysis is a helpful tool to decode customers’ feedback. And bring significant benefits to your business:
Superior understanding of your target audience’s behaviour
The sentiment analysis’s main purpose is to depict what customers really want, their preferences and what makes them take action.
Deciphering clients’ sentiments offers clear information regarding their needs and pain points. Thus, you are able to make the necessary decisions to tailor your products and services accordingly.
Data-based business decisions made in real-time
Knowing how your audience responds to your products and services in real-time offers the possibility to react to changes in due time. Make the right decisions at the right time when trends are changing and when opportunities appear.
Improve customer care and customer experience
In our online world, unhappy clients spread the word quickly. Spotting negative sentiment early empowers your business to act promptly and solve the issues before they evolve into real problems. And turn discontented clients into loyal ones.
Keeping a close eye on the competition
Knowing what your competitors are up to and identifying their weaknesses and strengths may offer you a formidable edge. Enabling your business to adequately respond and polish your strategies.
Tailored Marketing Actions
Personalising your marketing campaigns is not a nice to have, it’s a must. And customer sentiment gives you a hand with that. Based on the insights delivered by its analysis you have the ability to craft messages that resonate with the audience’s emotions.
Thus, increasing the chances of getting their attention, improving engagement and attracting qualified leads.
Customer Sentiment Analysis Challenges
While sentiment analysis has valuable benefits, it also comes with difficulties that must be overcome.
Some of them are:
- The ambiguity of the context. Considering words may have various meanings in the function of the way they are employed, sentiment analysis has to correctly identify the sentiment based on the accompanying words. For instance, “brilliant” might refer to the stone with this name or might mean “smart”, depending on the context.
- Industry-specific nuances. Each niche has its proper sentiment nuances and language. And sentiment analysis needs chameleonic capacities to be able to detect the exact meaning of a word in the context of a specific industry. For example, the “hot spot” for a software engineer is technical stuff, and for an ice cream lover is the Italian gelateria from downtown.
- Irony and sarcasm. Algorithms are not exactly good with jokes, or not yet. However, sentiment analysis is in charge of deciphering the subtleties of humour and categorising the correct sentiment of a text.
- Analysing texts in multiple languages. Besides the existence of a plethora of languages and idioms, each has its variety of expressions and nuances. Sentiment analysis has to accurately translate and process them.
Customer Sentiment Analysis Tools
Today there is a myriad of customer sentiment analysis tools that propose a large variety of facilities. However, you have to choose one that fits your needs and has expertise in your industry.
You need a powerful ally to transform for you the art of sentiment analysis into science. And harmoniously merge data-based decisions with the human touch.
Among the sentiment analysis tools you can choose, there are:
- Social media listening tools
- Survey tools
- NLP Libraries
- Cloud-based NLP services
Symanto’s Customer Sentiment Analysis
Symanto is a next-generation text analysis technology dedicated to converting unstructured data from customers’ talks into actionable insights and critical metrics.
Our world-class AI technology delivers highly accurate sentiment analysis constantly refined by a top team of deep learning experts.
We offer an AI-powered customer sentiment analysis that is executed on two levels:
- Text level sentiment – considers the tonality of the overall text
- Topic level sentiment – identifies specific attitudes regarding a topic
And permits an instant text analysis and insights delivery, in order to empower you to improve business decisions immediately.
Main features of Symanto’s customer sentiment analysis
- AI-based: considers the whole context.
- Industry-specific models: pre-trained models that include the particular nuances of an industry.
- Enhanced accuracy: models trained on billions of texts, polished by domain experts and linguists that deliver accurate results and minimised false-neutral output.
- Topic-level sentiment analysis: uncover granular attitudes relative to a topic.
Customer sentiment analysis use cases:
We’re at the forefront of NLP research and technology. Thus, constantly exploring new ways to help businesses extract insights from their data. Our tools for customer sentiment and emotion detection, as well as our unique psychographics tools, are just some of the ways we can help you get more out of your customer data.
If you’re ready to take your customer sentiment analysis to the next level, get in touch or book your free personalised demonstration today.
Case Study
The real-world application of sentiment analysis highlights its impact on the future development of a business.
How Symanto delivers powerful insights into the competitive landscape for a digital fashion player
Zara revolutionised the fashion industry by launching a business model focusing on the latest fashion trends. A new wave of digital players pushed the limits further and come up with the concept of listing and delisting new products weekly.
One of these players approached Symanto to help them support their expansion with data-backed insights.
The methodology used:
- Executed a growth potential analysis based on online data sources like social media posts, site traffic, number of subscribers via the app, number of app downloads, etc. The detailed analysis revealed particular growth hacks used by the target competitor and its peers.
- Performed a customer sentiment analysis for each competitor based on the satisfaction drivers.
- Examined customer engagement and the impact of using influencers.
Valuable insights were uncovered:
- Current market leader was about to be replaced by a challenger in the next months. A view that was not exactly clear at the time.
- Challenger company disposed of obvious cost advantages and a leader position for some other satisfaction drivers. A total surprise for the client company.
- The growth hacks employed by each competitor were radically different compared with the others.
- Current leader used mostly celebrities while the challenger experienced better results hiring micro and nano influencers.
- Leader’s style was not flexible enough to permit expansion to new market segments.
Customer Sentiment Analysis Best Practices
As always, in business, the success of an action relies on choosing the right strategies. When executing a sentiment analysis respecting the following good practices it’s important:
- To get a holistic view combine the quantitative and qualitative aspects.
- Choose tools that update regularly their models with the current language trends.
- Improve accuracy by incorporating human validation into the process.
- NLP techniques are constantly evolving, make sure you use the newest versions.
Customer sentiment analysis gives you access to a world where customer satisfaction and business expansion party together. Make the most of it to support your future business growth.
