The study of natural language processing, NLP, began several decades ago, but it’s only recently that it’s reached a high enough level of precision to bring real added value to companies and their customers. This form of artificial intelligence surrounds you on a daily basis, more than you think: with voice assistants like Siri, your iPhone spell check, automatic suggestions when you type a search in Google...
And even in the domain of customer service, where each day, conversational chatbots rely on NLP to respond automatically to a huge amount of user requests, constantly improving themselves to propose more precise human-machine interactions.
In this article, we explain in simple terms everything you need to know about NLP. Ready to discover this technology, at the crossroads of artificial intelligence and linguistics? Follow our guide!
A simple definition of NLP
What is NLP? NLP is the acronym for Natural Language Processing. This technology is a type of artificial intelligence and a branch of Data Science. It allows computers to analyze and to understand human language, and to generate interactions, by transforming raw data into intelligent conversation.
This system allows humans and machines to speak the same language. Concretely, thanks to NLP, companies can automatically analyze sentences produced by a human in order to make the most adapted decisions.
Within NLP, different subparts coexist. Notably, Natural Language Processing includes:
- NLU, Natural Language Understanding. The role of NLU is to understand in-depth exchanges and data, in order to identify the intentions of human speech or writing.
- NLG, Natural Language Generation. Its role is to automatically create and generate exchanges in a specific language, thanks to artificial intelligence. In this way, data is transformed into text and companies can automate certain manual processes.
You’ve got it: NLP allows for the analysis and comprehension of human interactions thanks to NLU, and it can automatically generate content thanks to NLG. Let’s move on from the definition to some practical elements.
How does NLP work?
To understand the way NLP works, let’s take a step back. Its history begins in the 50s with the automatic translation of simple sentences. In the midst of the Cold War, Americans tried to analyze Soviet communication: and that’s how Natural Language Processing was born.
In the 60s and 70s, the first chatbot models saw the light of day (like ELIZA in 1964). It wasn’t until the end of the 80s, however, that research achieved the first Machine Learning algorithms.
With those algorithms, language processing and recognition was able to perfect itself. And today, NLP continues its evolution thanks to “machine learning,” made possible by Deep Learning (powerful learning capabilities).
So, the functioning of NLP relies on deep learning techniques. This is how its use breaks down:
1. The human interacts with the machine
Most often, the request is made via chatbot, voicebot, dynamic FAQ or a voice assistant...Whether written or oral, the user's intention is expressed in natural language.
2. The machine captures the request, in order to analyze and better understand it
At this step, NLP algorithms use different techniques, like syntactic analysis (to identify grammatical rules) or semantic analysis (to decode the meaning of the text).
The request is studied completely (in terms of form and meaning of the text, the context of the question, the sentiment of the speaker, etc.). The text is also compared in real-time with the data already saved by the company.
Past conversations and customer databases are analyzed. That’s when Machine Learning or Deep Learning come into play:
Machine Learning is a subset of artificial intelligence (AI) that is focused on the creation of systems that learn or improve performance based on the data they process.
- Deep Learning is a type of artificial intelligence derived from machine learning where the machine is capable of learning on its own, as opposed to being programmed, wherein it would be satisfied by perfectly following predetermined rules.
The system then opts for the best decision, before formulating its response.
3. The machine responds to the human
This response can be written or oral, depending on the interaction channel. Thanks to the work of NLP, the generated response is clear and coherent. It relies on natural and human language.
One or several actions can be triggered: a proposal to continue the conversation with a customer service agent via the contact escalation channel; redirection to a specific page on your website; recommendation to download personalized documents, etc. The idea is to offer the best user experience, by proposing a relevant response in the language of the speaker.
What are the advantages of NLP for customer service?
Thanks to NLP, technology is put in service of the customer and the customer service agent, to better understand and serve them. The benefits of such a process are as numerous for users as for companies and their employees:
NLP to improve the customer experience
The main opportunity offered by NLP is to considerably improve the customer experience. This technology responds to the new needs of modern consumers: autonomy, immediacy and accessibility. For example, a customer self-service solution like the dynamic FAQ or the chatbot, which relies on NLP, can respond to customer questions in real-time, 24/7.
All along the customer journey, NLP allows you to propose a personalized and optimized conversational experience. Before, during or after a purchase, customer knowledge is reinforced by your solutions which capture the natural language of your customers and prospects in order to continuously improve your knowledge thanks to their precise feedback. Your customer satisfaction will be even more valued!
NLP to support your customer service
NLP is a support lever for your various entities...but it’s not meant to replace your customer service teams!
In fact, Natural Language Processing is a high-quality internal support. This intelligent tool delivers precious quantitative and qualitative information. In the end, it helps customer service to better understand and analyze the emotions of users of your brand.
Furthermore, NLP offers considerable saved time to your customer service. Tasks of understanding, analysis and responses are automated, thanks to artificial intelligence and Deep Learning. Your teams can dedicate themselves to more complex missions and leave the NLP of your self-service technologies, dynamic FAQs or chatbots to treat the more simple exchanges that don’t require a human agent.
Finally, NLP is not just an opportunity for teams dedicated to after-sales service. Marketing and sales teams can benefit from such a tool, improving their strategy, brand image and competitive positioning.
For example, if a competitor is mentioned by one of your clients during an exchange with a chatbot, the semantic and syntactic analyses will be extremely helpful. In this way, you can identify the user’s feelings towards the competition and potentially act to create a preference in your favor.
In theory, NLP seems to be the ideal tool for your clients and your teams. But in practice...is it really a perfect technology?
The importance of training for NLP
Despite the numerous advantages of NLP, there are certain limits to highlight. NLP is a complex process entrusted to a computer that doesn’t always grasp the nuances of human language. In fact, human language isn’t always precise. It’s sometimes ambiguous and its linguistic structure depends on many variables that are often complex, like slang, regional dialect or social context.
For example, when a user uses humor or sarcasm, the algorithms don’t always understand. Similarly, synonyms or certain spelling mistakes are subtleties that are difficult to master, even with NLP Machine Learning. For example, the word “ticket” can be used in the vocabulary of transport, entertainment, reporting (i.e. a customer service ticket)...or even getting fined (as in a traffic ticket).
Furthermore, all languages are unique and have their own specificities. Artificial intelligence must therefore adapt to different languages with different processes...which comes at a cost. And the cost can be serious for international companies that wish to rely on Natural Language Processing.
But to alleviate the limits, a solution exists, training. In order to understand, analyze and process an intention, the NLP model must be trained and fed with data that will make it grow.
For example, a conversational chatbot must be trained to understand and react to multiple requests in many subjects. Let’s take a classic from After Sales Service: “How can I get reimbursed for my purchase?”
Let’s be realistic: It’s not very likely that this question will be formulated as such. Customers can also ask the chatbot: “what are the conditions for reimbursement?”, “Get reimbursed,” or simply, “reimbursement.” Whatever the exact request, the chatbot must be trained to collect customer data in different forms, to better understand and correctly respond to customers and prospects alike.
With good, regular training, the algorithms will be able to more easily and quickly understand or even predict certain customers sentiments. Training facilitates the personalization of exchanges and even the prediction of certain requests...and it contributes to the emergence of solutions that propose high-quality interactions that optimize the customer experience!
NLP and customer service
Natural Language Processing can be integrated into a number of automated tools. Here are several examples of applications in the domains of customer services and digital:
- Processing complaint emails: Every day, your customer service receives a number of emails. Their treatment can be time-consuming, complex and therefore costly. Thanks to NLP and notably, semantic analysis, the comprehension of requests can be facilitated. Employees can send personalized responses that are generated with the help of artificial intelligence.
- Callbots: This self-service tool is programmed to receive and transmit messages during a telephone call, in complete autonomy. The callbot relies on voice recognition and vocal synthesis, made possible by NLP.
- Chatbots: Also called conversational agents or assistants, these customer service robots 2.0 allow for real-time conversation with users. Useful before, during or after purchase, they modernize customer relations with fluidity, bringing rapid and efficient solutions.
- Dynamic FAQs: these models of Frequently Asked Questions function like search engines. Users type in a question or keyword, and the self-service tool automatically detects their issue. Thanks to data processing, the company can therefore propose responses that are perfectly appropriate to the naturally formulated requests from your users.
These tools help to reduce the number of requests addressed to your customer service, to improve customer satisfaction, and to boost conversion on user journeys. All this with the support of a strong ally, which you will soon no longer be able to do without: NLP!
So, are you ready to integrate NLP into your customer relations and take a step towards the digital transformation of your customer service by discovering our customer self-service solutions?