How AI is revolutionising customer service processes

While product and service quality is a key competitive factor these days, so too is optimal customer service. Artificial intelligence and natural language processing are helping to boost efficiency in the service centre and customer service – increasing customer satisfaction in the process.

‘Welcome to our customer centre. If you have questions about your bill, please press 1. For information about our products and services, please press 2.’ This or something similar is still how many torturous odysseys through customer centre labyrinths begin. Determining the right buttons to press often takes a lot of time and effort before – hopefully – the right specialist is reached. Customers can be hugely frustrated if they end up in the wrong place after this test of their patience. And the effects are felt by both parties: the customer still needs assistance, while all the service employee can do is pass them on without resolving the issue.

Today, customers expect their providers to help them quickly and efficiently – regardless of whether that is their telephone provider, their bank or an online shop. For happy customers, companies must ensure that their service centre works as efficiently as possible and has state-of-the-art systems.

NLP in the service centre: five key areas of application

One essential component of efficient customer service is the use of artificial intelligence (AI), especially natural language processing (NLP). NLP deals with the interaction between computers and human language and is used for language translation, text summarisation, mood analysis and more. This can reduce waiting times and improve customer satisfaction. The following five examples illustrate how NLP optimises customer contact:

1. Customer identification: Voicebots can identify and verify customers using identification features, voice biometrics or other two-factor authentication methods. This eliminates the need for customer advisers to query data manually, speeding up the process and getting customers to a resolution faster.

2. Conversational IVR and skill-based routing: With Conversational IVR (interactive voice response), customers can simply speak their queries or concerns instead of using the keypad to make a selection. With skill-based routing, customer requests can be assigned to the most suitable employees based on their skills and knowledge. Here’s an example: instead of choosing a number, the customer can say ‘I forgot my card PIN’ and be forwarded directly to the competent specialist. This improves call transfer accuracy and reduces internal forwarding to a minimum. Customers save time and receive the help they need more quickly.

3. Automation: The integration of NLP into workflows and core systems enables voicebots to provide direct information and perform tasks. In the example above, a bot might directly trigger the sending of a new PIN by letter or provide instructions on how to the customer can view their PIN in the self-service portal.

4. Agent assist: Through the use of assistance systems, customer service employees are supported with relevant information in real time, processes are initiated and admin tasks are handled by bots.

5. Voice of the customer: NLP’s automatic transcription and analysis of customer conversations enables companies to gain valuable insights and identify trends and patterns from customer feedback. This information can serve as a basis for customer adviser coaching and training and be directly incorporated into (further) development of products and services. This ability not only to solve customer problems, but also to learn from them, represents a decisive competitive advantage.

AI is already a reality

Such applications are commonplace today. The use of artificial intelligence in customer service has developed rapidly in recent years. More and more companies are recognising the potential of NLP, successfully implementing the technology in their service centres and significantly boosting their efficiency thanks to automated and improved call transfer.

In the future, AI systems will increasingly be able to identify customer concerns more precisely and even proactively point out potential problems before they actually arise. On a company website, for example, a reactive service bot may be supplemented by a sales bot that advises customers on offers and supports them in the purchase process. By networking with additional data sources, AI can also enable personalised interactions and provide valuable insights for improving service quality. Sentiment analysis, for example, can be used to detect mood and emotional state based on the content of conversations and develop suitable discussion guidelines – adapted to the individual situation.

So one thing is clear: companies that want to stay competitive need to implement these technologies in their service centres now to create the best possible customer experience. Organisations that miss the boat here run the risk of falling behind and no longer being able to meet the increasing demands of customers on their service centres. All the more so since AI has long been part of customers’ daily lives, for example in the form of voice assistants such as Siri, Alexa and Cortana.

Cultural change required

From a technical perspective, there’s not much standing in the way of implementing AI solutions such as NLP: access to low-cost and – thanks to Infrastructure as a Service – scalable processing power in the cloud enables the use of high-quality AI solutions with a wide range of features. At the same time, however, companies must invest in training their employees to ensure that they can deploy the new technologies as effectively as possible. Feedback analysis processes must also be introduced and established in the organisation so that customer interactions can be used to actively improve products and services. Thus, rolling out NLP systems in service centres requires a corporate strategy that goes beyond technological arrangements. Decision-makers need to ensure that AI is seen not just as a technical gimmick, but as a central part of customer service and product development. Technology partners can provide support here: Swisscom specialises in optimising and automating workflows and helps companies develop customised omnichannel solutions – for the best possible customer experience and more productive employees.