How can you leverage natural language processing to improve customer support services?

Natural language processing, or NLP, represents an interdisciplinary fusion of computer science, artificial intelligence, and linguistics, aimed at empowering computers to comprehend and interact with human language. In the realm of customer support, NLP plays a pivotal role by facilitating accurate interpretation of customer inquiries and delivering pertinent responses. Through the utilization of sophisticated algorithms capable of analyzing intricate language patterns, NLP systems can not only grasp the nuances of requests but also categorize them and discern underlying sentiments. This capability holds immense significance in streamlining customer service processes, as it enables automation of responses to routine inquiries.

I think we can all agree that Chatbots and Virtual Assistants are the way of today and also of the future. It is very important to continue to train them over time with new data to ensure their usefulness. Also, the best friend of every chatbot is an escape route to a live body when things go wrong.

- Chatbots: NLP powers chatbots to accurately interpret customer queries, ensuring immediate, accurate responses, and reducing customer wait times. - Sentiment Analysis: NLP aids in detecting subtle emotions in customer communications which can be used to address concerns proactively. - Customer Profiling: Using NLP, we can analyze customer interactions to understand their specific needs, allowing for more targeted and personalized service.

NLP is crucial for chatbots, whether text or voice-based, forming the core of human-bot interaction. In customer support, NLP offers several benefits: Efficiency: By handling FAQs, NLP chatbots free up human agents. For instance, a user can instruct a chatbot to freeze their card, streamlining banking and insurance services. Many BFSI companies now use NLP chatbots as the primary contact for query resolution. Accuracy: NLP chatbots adhere strictly to rules, policies, and standards, ensuring error-free information. Cognitive Health: NLP chatbots are proving beneficial for individuals with depression, offering consistent support and interaction. NLP is transforming customer support, making it more efficient, accurate, and supportive.

Los chatbots mejorados PLN transforman la primera línea de atención al cliente, haciéndola saltar al próximo nivel de atención. Los asistentes virtuales: 📌entablan conversaciones naturales con los clientes, 📌responden preguntas, 📌resuelven problemas y, 📌brindan soporte de manera oportuna y precisa. ✍️Un chatbot comprende el contexto de una conversación, identifica las emociones del cliente y adapta su respuesta a él. ✅El chatbot debe ofrecer opciones claras para que los clientes puedan escalar sus problemas a agentes humanos cuando sea necesario, Esto es útil en: 📌casos complejos que requieren intervención humana o, 📌cuando el cliente prefiere interactuar con un representante en persona. Éxitos!👍

Hoje em dia é algo que tá bem em alta com o uso de IA Generativa (que faz parte do NLP, utilizando Transformers), procurem utilizar IA Generativa para tornar seus chatbots mais inteligentes, personalizados e "humanizados"!

Neste tópico vale o comentário que fiz acima, pois a cada dia surgem novos modelos e ferramentas que facilitam os processos de comunicação corporativa com clientes, fornecedores e colaboradores. Sendo mais fáceis de implementar e utilizar recomendo às organizações, que as testem e implementem com o apoio de profissionais especializados.

The role of chatbots in a customer's journey has never been conceived as a replacement for direct human interaction but as an integration of a channel to provide immediate, simple answers and to understand the steps to follow for any need. Often, these channels have created high expectations among companies in terms of cost reduction. Today the chatbot takes on a different perspective, more precise, more powerful, and less impersonal . Despite this, it will always have the function of providing quick, polite responses and following up with the customer. In a sense, nothing new but with the potential to do it in a more natural way and with a truly useful base of responses and knowledge for the purpose it was designed for

RAG Architekturen sind eine gute Methode um Chatbots durch LLMs zu verbessern und dabei auch auf Informationsintegrität zu achten. Dennoch ist es ratsam sich genau den Kundensupportprozess anzuschauen. Es kommt nach wie vor zu Fehlern, Falschinformationen und Halluzinationen. Hier ist es ratsam eher den Prozess zu ändern oder Mitarbeitenden Tools an die Hand zu geben die es ihnen ermöglicht historische und real-time Informationen zu erhalten. Auch voice interfaces und das simultane transkribieren von Anfragen kann hier sehr sinnvoll sein

We have all experienced that often emails have been a source of inefficiency and time wastage. In some organizations, internal processes have been initiated to reduce the number of emails sent. Emails have proven to be an often inefficient asynchronous communication tool when there are frequently only two parties involved (time to think about the communication, time to write, reread, read and understand the response, plus all the misunderstandings that this message often brings). NLP can support a much more efficient and concise model of managing and retrieving conversations, providing pre-processed answers or content. Despite this, I believe that emails will remain an inefficient channel of communication.

Unfortunately, our experience has shown that we are not yet able to fully automate communication with consumers. While receiving some automated suggestions was helpful, we still had to review and fine-tune everything. Overall, while automation can reduce some of the effort involved in communication, our experience indicates that this reduction is only around 20%.

More than categorizing automatic responses, it is important to have automation intelligence that can resolve problems and forward other simpler requests. Monitoring this type of automation can quickly promote a very efficient automated email initiative.

NLP can greatly enhance the efficiency of email support. By analyzing keywords and context in incoming emails, NLP tools can automatically categorize and prioritize messages. This ensures urgent emails are addressed quickly, while routine inquiries receive automated responses or are directed to the right department. This approach speeds up response times and helps support teams manage their workload more effectively.

Los modelos de lenguaje pueden detectar automáticamente la urgencia o el sentimiento de cualquier interacción. No solo esto, sino que también pueden identificar el idioma, determinar si se trata de un contacto tipo ‘spam’ o no, y si es un contacto de postventa o comercial. Con todo esto, y el uso adecuado de herramientas que lo permiten, podemos pre-categorizar los casos cuando llegan a nuestros programas de atención al cliente (Freshdesk, Gorgias, Zendesk), para luego distribuir el trabajo entre los agentes y priorizarlo adecuadamente.

Being able to gauge the level of frustration and/or urgency is key to prioritize and handle customer support and more importantly to provide opportune recommendations to prevent customer churn. NLP plays a key role in providing an accurate diagnostic that in tandem with a good prognostic (churn likelihood) can feed a recommendation engine in order to obtain the proper prescriptions based on the issue criticality, churn risk level and of course, customer knowledge (personalization).

Sentiment analysis uses NLP to understand the emotions behind customer messages. By examining support tickets, social media posts, or chats, it can tell if a customer is happy, frustrated, or angry. This helps support reps respond more effectively, addressing issues before they escalate and improving the customer experience.

By using NLP, customer support agents can gain a deeper understanding of customer emotions and respond with empathy and precision. Sentiment analysis enables agents to detect: • A frustrated customer might use strong language or express dissatisfaction. • A satisfied customer may use positive words or emojis. • An anxious customer might ask urgent questions.

Every customer agent's dream is to understand the sentiment of the customer to better direct the type of response. This is not always simple because it depends on the culture, language, communication channel, age, and education level of the customer. NLP is a fantastic tool because it can analyze the conversation and statistically compare it with a wide variety of situations, providing the customer agent with the right feeling. This will certainly support greater efficiency in communication but above all the definition of the right answers, the right tone, allowing to preserve relationships with the right customers and then build and improve the historical base as well.

Data from sentiment analysis must be treated in such a way that it can be enriched with other information for a more efficient result!

he high computational capacity offered by recent NLP models allows for real-time operation during a conversation with one’s client. Naturally, the ability to interpret, predict, and support the customer agent’s conversation is an additional level of efficiency, especially where one operates in multilingual contexts and comprehension times can affect the handling time of interactions.

AI tools can transcribe in real time the words spokes into text, understand the context and then search vast database of content, faq’s, journals and can accurately predict customer queries and issues. It can help provide answers to those questions which can help agents handling the calls, improve customer experience and reduce call time. It can even do sentiment analysis and guide agent on how to handle the customer and in specific cases even recommend engaging supervisor or above. Organizations who use to rely on months of training and years of experience to better customer experience can effectively use these tools to take user experience to next level.

It is certainly true that NLP tools learn and process more data more effectively as they become smarter. However, the efforts required to train them properly are often underestimated. Unfortunately, significant effort is still needed to ensure they learn correctly. Additionally, you must develop a strategy to verify that they have learned accurately and are functioning correctly.

Integrating natural language processing (NLP) into customer support can be effective without the high costs of custom-tuned models. Augmented generation solutions are straightforward and cost-effective, enhancing pre-trained models with data from vector stores. This approach requires less resources than repeatedly retraining a dedicated model, as updates or adjustments can be made by simply adding or removing documents from a vector store, avoiding extensive retraining and the need for a dedicated MMLOps team managing the model and it's responses. Additionally, augmented generation offers better transparency in response generation, allowing for clearer understanding and management of output and model agnosticism.

The power of NLP in customer support lies in its continuous learning. As NLP tools process more data, they become more adept at understanding customer needs, leading to more personalized and efficient support experiences.

The current issue is about accuracy and hallucination. This is where the current frontier is and that requires that we think about process design not just tools, and individual people skills. We have always had issues with quality control of people and that’s the reason why we have created sophisticated process design methodologies, including lean, and six sigma. The same should be done here - as part of the human-machine interaction design. I have written about it here https://www.linkedin.com/pulse/beyond-human-loop-reliable-ai-enterprise-workflows-gianni-giacomelli-kzahf

Technologische Lösungen zu implementieren bringen auch immer Implikationen für Prozesse mit sich. Es ist wichtig vor allem mit den Domänen-Experten diese Tevhnologien zu entwickeln, zu testen und auch zu verbessern.

Aunque hoy en día podemos beneficiarnos de todas estas ventajas mediante herramientas No-Code, que permiten construir casuísticas muy potentes, sigue siendo una asignatura pendiente para los CRMs y programas de atención al cliente el incorporar nativamente funcionalidades que permitan utilizar la IA directamente desde dentro. Esto sin duda eliminará muchas barreras de entrada y popularizará el uso de la IA en la atención al cliente de las empresas. Para mi gusto, muchas están siendo algo lentas en incorporar este tipo de servicios y tienen una oportunidad de upselling enorme.

I am confident that NLP tools will soon greatly enhance your support process. I recommend starting to test and experiment with them now. This field is growing at an incredible pace, and the sooner you begin, the easier it will be to integrate these tools effectively in the future.