How would you feel about: a chatbot that understands your emotional intent; a voice bot that can recognize your tone of voice; a search engine that understands the intent of your search, thanks to the meaning of a sentence. You might be tempted to believe that this sounds like science fiction.
Well, think again! This technology is already in use and is analysing the emotion and meaning of exchanges between humans and machines. That’s right, we’re talking about semantic analysis. But what exactly is this technology and what are its related challenges? What are its advantages and impact on customer relations? And how can it be used in a customer experience strategy? Read on to find out more about this semantic analysis and its applications for customer service.
Table of content:
- What is semantic analysis?
- How does semantic analysis work?
- Semantic analysis helps customer service
- The advantages of semantic analysis
- Semantic analysis examples and use cases
What is semantic analysis?
Semantic analysis is a technique that can analyse the meaning of a text. Contrary to analysing the syntax or syntactic analysis, the challenge is not to analyse the grammatical structure of a sentence but rather its purpose, taking into account the feelings and emotions that dictate the meaning of a message called sentiment analysis.
Here are the differences to note:
- Syntactic analysis focuses on “form” and syntax, meaning the relationships between words in a sentence.
- Semantic analysis focuses on “meaning,” or the meaning of words together and not just a single word.
These two techniques can be used in the context of customer service to refine the comprehension of natural language and sentiment. This discipline is also called NLP or “natural language processing”.
As such, when a customer contacts customer services, a text analysis is performed and the role of semantic analysis is to detect all the subjective elements in an exchange: approach, positive feeling, dissatisfaction, impatience, etc. All of these elements feed the process of semantic analysis, notably by refining customer knowledge and by improving the quality of proposed solutions.
How does semantic analysis work?
Let’s take two examples of sentences:
- The customer, says Mister Johnson, is satisfied.
- The customer says: Mister Johnson is satisfied.
A simple change in punctuation is enough to change the meaning of this sentence. That’s the subtlety of semantic analysis: understanding the logical meaning that connects the parts of a sentence and that which impacts its meaning.
For us humans, there is nothing more simple than recognising the meaning of a sentence based on the punctuation or intonation used. But for machines, the challenge is scale. And that’s where semantic analysis tools are particularly useful.
Today, machine learning algorithms and NLP (natural language processing) technologies are the motors of semantic analysis tools. They allow computers to analyse, understand and treat different sentences.
To better understand how automated semantic analysis functions, here’s an example:
- A company receives an online review from a customer. The message is rich in client statements, can be sent via email, through a conversational chatbot, or posted directly on social networks. For example: “Contrary to my request, the hairstylist dyed my hair orange! I thought it was a joke, but no. I’ll never go back!”
- The company’s semantic analysis tool will proceed to the analysis of these statements. Here, artificial intelligence must understand the meaning of the words used. For example, “orange” should be analysed as a homonym and a polysemantic word (i.e. one with multiple meanings). The machine must recognise that the single word corresponds to a colour and not a fruit. The use of the word “joke” (with a generally positive connotation) must also be identifiable in a negative context. Finally, the tool must be able to identify the customer’s deep dissatisfaction that gives meaning to the message.
- These analyses allow for the classification of conversations or customer requests into different categories, by theme, feeling, intentions or risks. For example, faced with a negative review like the above, the message could be categorized as one of “dissatisfaction,” with a high level of risk. Internal teams can then be alerted and intervene as quickly as possible, preventing the unhappy customer from becoming a detractor.
Semantic analysis helps customer service
The challenge of semantic analysis is understanding a message by interpreting its tone, meaning, emotions and sentiment. Today, this method reconciles humans and technology, proposing efficient solutions, notably when it comes to a brand’s customer service.
Effectively, support services receive numerous multichannel requests every day. With a semantic analyser, this quantity of data can be treated and go through information retrieval and can be treated, analysed and categorised, not only to better understand customer expectations but also to respond efficiently.
Semantic analysis transforms data (written or verbal) into concrete action plans. Analyzing the meaning of the client’s words is a golden lever, deploying operational improvements and bringing services to the clientele.
Beyond customer service, semantic analysis can be a tool for the whole company. It’s not sufficient enough for customer service to analyse incoming emails or for community managers to decode the messages they receive on social networks. Semantic analysis can also provide a way to improve customer knowledge for the company as a whole.
The advantages of semantic analysis
That’s right, semantic analysis not only provides valuable help to customer service but also to all the teams in a company. Semantic analysis also has an impact on customer experience at many different levels. Here’s a recap of this technology’s main advantages:
1. Improving customer knowledge
2. Accelerating a customer-centric Strategy
3. Offering relevant solutions to improve the customer experience
4. Improving company performance
5. Refining an SEO strategy
6. Reinforcing the company’s customer self-service solutions
1. Improving customer knowledge
By analysing a query’s meaning, semantic analysis optimises customer knowledge as a whole. Beyond the concepts of satisfaction and dissatisfaction, it’s also tone and emotions that get reviewed. The comprehension of sentiments completes more traditional analyses of customer experience feedback (satisfaction surveys, emails, posts on social networks, online reviews, etc.).
2. Accelerating a customer-centric Strategy
Semantic analysis truly puts the customer at the heart of your business. Such a technology contributes to the deployment of a customer-centric strategy, where each decision is centred on the customer’s needs. This type of company culture makes it easier to identify satisfied customers, and to transform them into Ambassadors and brand advocates, while boosting customer loyalty.
3. Offering relevant solutions to improve the customer experience
Thanks to machine learning and natural language processing (NLP), semantic analysis includes the work of reading and sorting relevant interpretations. Artificial intelligence contributes to providing better solutions to customers when they contact customer service. These proposed solutions are more precise and help to accelerate resolution times.
4. Improving company performance
Semantic analysis offers considerable time saving for a company’s teams. The analysis of the data is automated and the customer service teams can therefore concentrate on more complex customer inquiries, which require human intervention and understanding. Further, digitised messages, received by a chatbot, on a social network or via email, can be analyzed in real-time by machines, improving employee productivity.
5. Refining an SEO strategy
Semantic analysis can also benefit SEO (search engine optimisation) by helping to decode the content of a users’ Google searches and to be able to offer optimised and correctly referenced content. The goal is to boost traffic, all while improving the relevance of results for the user. As such, semantic analysis helps position the content of a website based on a number of specific keywords (with expressions like “long tail” keywords) in order to multiply the available entry points to a certain page.
6. Reinforcing the company’s customer self-service solutions
Systems of semantic analysis allow machines to detect human emotions in order to extract valuable information from unstructured data. All of this is an advantage for the deployment of a customer self-service strategy. In effect, the goal of self-service is to treat certain customer inquiries autonomously and in real-time. Natural language processing can therefore be adopted by chatbots or dynamic FAQs. Thanks to semantic analysis technology, the solutions offered by chatbot tools can also take into account the form and meaning of a message in order to offer an exceptional digital customer experience!
Semantic analysis examples and use cases
Semantic analysis can help companies in numerous situations: handling customer reviews, messages with a chatbot, conversations with a call bot... Here are a few concrete examples:
1. Cdiscount and the semantic analysis of customer reviews
Since 2019, Cdiscount has been using a semantic analysis solution to process all of its customer reviews online. This kind of system can detect priority axes of improvement to put in place, based on post-purchase feedback. The company can therefore analyze the satisfaction and dissatisfaction of different consumers through the semantic analysis of its reviews.
As such, Cdiscount was able to implement actions aiming to reinforce the conditions around product returns and deliveries (two criteria mentioned often in customer feedback). Since then, the company enjoys more satisfied customers and less frustration.
2. Uber and social listening
In terms of semantic analysis, Uber’s strategy is the following: When the company releases a new version of its app, social networks and the feelings of users are analyzed scrupulously. Here we’re talking about “social listening,” which is listening on social networks to measure the degree of user satisfaction or dissatisfaction.
“At Uber, we use this approach daily to understand what the users feel in regards to the changes that we implement. As soon as we introduce a change, we know what is welcomed with enthusiasm and what needs to be improved.”
Krzysiek Radoszewski, Marketing Lead Eastern and Central Europe at Uber
3. Hummingbird, Google’s semantic algorithm
B2B and B2C companies are not the only ones to deploy systems of semantic analysis to optimize the customer experience. Google developed its own semantic tool to improve the understanding of user searchers.
Developed in 2013, the Hummingbird algorithm refines the relevance of results proposed by Google by analyzing the intentions of user searches. This algorithm also boosts natural or organic referencing (SEO) and benefits companies, who can stand to gain from integrating quality content on the pages of their website. They will be better referenced with “semantically” relevant keywords!
Semantic analysis and self-service work hand in hand to empower users
To improve your customer knowledge and customer experience: use customer self-service solutions!
Thanks to tools like chatbots and dynamic FAQs, your customer service is supported in its day-to-day management of customer inquiries. The semantic analysis technology behind these solutions provides a better understanding of users and user needs. These solutions can provide instantaneous and relevant solutions, autonomously and 24/7.
To learn more and launch your own customer self-service project, get in touch with our experts today.
