Published date: September 27, 2024
Expert analysis by Dmitry Kindrya, COO
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The world of customer support is currently being reshaped in an epochal way, through the combination of machine learning (ML) and natural language processing (NLP). In the high-speed, on-demand world we now live in–where customers want personalised, relevant experiences with every interaction – these technologies are required. They are the engines that will drive a new generation of customer service; one marked by leaner processes, higher efficiency and – most importantly a more engaged customer.
Smart Chatbots and Virtual Assistants
AI-powered chatbots and digital assistants are able to understand and speak human languages, leading the transforming effect quite well by listening to customers’ queries and responding accordingly, in real time for that matter; providing immediate assistance. It will enable human agents to attend to much more complex issues. According to a Gartner 2023 report, chatbots will be handling approximately 85% of all customer service interactions by the year 2025.
Real-World Example: Sephora has developed a chatbot on Kik-a virtual assistant powered by artificial intelligence-that helps users find their perfect makeup products, suggesting personalised recommendations based on individual skin tones and preferences. Conclusively, this was able to generate a significant customer reach with sales upside for Sephora.
By deploying AI chatbots, companies can manage thousands of customer interactions simultaneously, making sure that no question goes unanswered and enhancing overall satisfaction. According to Talkdesk, 79% of consumers prefer self-service options for simple inquiries, highlighting the demand for responsive, AI-enabled solutions that meet customer needs in real time.
Proactive Analysis of Customer Support Sentiment
Sentiment analysis, a technique from NLP, makes an organisation aware of customer sentiment across interactions: emails, social media postings, or chat conversations. Early-stage identification of negative feelings or sentiments can, therefore, help businesses address the issue proactively and prevent escalations.
For instance, KLM Airlines uses sentiment analysis to track down the conversation on social media and gauge which customer is in a struggle. To this end, the proactive strategy enables them to reach out to that customer for assistance and change that negative experience into a positive one. Moreover, in a study conducted by McKinsey in 2023, sentiment analysis helped increase customer satisfaction ratings by 10-15%.
Personalisation at Scale
The one-size-fits-all era of support interaction is passing. Machine learning algorithms will drive an age of extreme personalisation, leaving each customer with the feeling of truly being recognized, heard, and understood. Imagine a support experience designed, in fact, by taking into account various forms of preference, purchase history, behaviour, even the emotional state of the customer at that moment.
This level of personalization outpaces even efficient customer needs; it builds relationships. In fact, a staggering 80%, according to independent research by Epsilon, of consumers are more likely to do business with a company that offers personalised experiences. Machine learning algorithms can churn through a huge volume of customer information in identifying previously undetected patterns and preferences that will enable businesses to provide proactive and anticipatory support intuitively and easily to the consumer.
In addition, Amazon uses its recommendation engine, powered by machine learning, to look through the customer’s browsing and purchase history and recommend other products in which they might be interested. One of the major drivers of Amazon’s success is that personal recommendations are responsible for 35% of sales.
Agent Augmentation for Increased Productivity
NLP and ML can involve learning, augmenting even more human agent capabilities to give real-time guidance and suggestions as they interact with the customer. All this brings down the resolution time and increases customer satisfaction.
For example, Salesforce uses its AI Einstein to offer real-time recommendations on the resolution of customer issues by agents, drawing from past interactions and knowledge articles. This has helped Salesforce’s customers reduce case resolution time by 30%.
According to a study conducted by Harvard Business Review in 2023, companies have already experienced a 40% increase in agent productivity when utilising AI to enhance their customer service representatives.
Multilingual support for a global reach
By using translation tools powered by NLP, organisations can provide customer service in their customers’ native languages and, therefore, be able to serve large numbers of customers. Unbabel is an AI-powered translation platform built to help companies deliver multilingual support. It brings together AI-powered machine translations with human post-editing to achieve high-quality translations. According to an independent 2024 study by Common Sense Advisory, 76% of people are more likely to buy a product if the product information is in their own language.
Case Studies: Real-World Transformations
The case studies below show the transformative power of ML and NLP in customer service:
Case Study 1: KLM Royal Dutch Airlines – To fly higher than customer expectations
The challenge: KLM, one of the world’s leading airlines, receives a staggering volume of social media mentions every day, making it a challenge to manage customer interactions efficiently across multiple platforms.
The Solution: KLM embraced an AI-powered social media customer service solution that leverages NLP to understand and categorise customer queries and sentiment analysis to prioritise urgent issues. As a result, The impact was remarkable. KLM witnessed a significant reduction in response times, leading to enhanced customer satisfaction. They were able to handle a larger volume of inquiries with fewer agents, resulting in cost savings and improved operational efficiency.
Case Study 2: Autodesk – Enabling a World of Customers.
The challenge: Autodesk, a global software company, faced the daunting task of providing timely support to its vast and diverse customer base spread across different time zones and languages.
The solution: Autodesk implemented an AI-powered chatbot equipped with NLP capabilities to understand customer queries in multiple languages and ML algorithms to deliver personalised support. As a result, the chatbot became a valuable asset, handling a substantial portion of customer inquiries and freeing up human agents to focus on complex and high-value interactions. This led to increased customer satisfaction, reduced support costs, and a more scalable support model.
The Road Ahead
The integration of ML and NLP in customer support moves along a well-trodden path and continues in its colourful journey toward a future where technology and human contact are married to create an amazing customer experience. These technologies are moving at warp speed; we have now reached the portal, if you will, of a whole new dimension for customer service: limitless possibility.
Hyper-Personalization: Assistance beyond One-Size-Fits-All
The one-size-fits-all general support interactions are gradually becoming things of the past. Machine learning algorithms are now all set to chart a new frontier of hyper-personalization—to actually understand and value each customer. Consider the scenario in which customer service can change its needs dynamically with respect to uniqueness, preference, purchase history, behaviour pattern, and even an emotional state.
Multilingual Support: Learn How to Overcome Language Barrier
The world has gone global and customer base for business entities cut across the language and culture barriers. This is becoming a reality via various NLP progresses. Once a time comes when language is no longer a barrier to deliver excellent customer support.
Emotion AI: The Empathetic Listener
The human touch is irreplaceable in customer support, especially when it comes to complex issues and delicate situations. However, AI is catching up fast, even in the realm of emotions. Emotion AI, powered by ML and NLP, is enabling machines to detect and respond to subtle emotional cues in customer interactions.
Conclusion
The future developments of customer support are expected by the limitless opportunities of ML and NLP. As we can see, these technologies are not just improving the quality of the service provided to the customers but defining the new paradigms of the latter. They are enabling organisations to move beyond such constraints and realise experiences which are relevant, inclusive, and evocative.
The future of customer support means that the expertise of a digitised customer relationship will be established in the long-term. It is a future where customers are identified, appreciated and given an ability to make their own decisions. This is a future where organisations sustain themselves by providing special customer care that makes organisations unique in today’s market. For us it can mean only one thing – practically unlimited opportunities for innovation and change as we embark on this fascinating and promising journey.
