What are the capabilities available to tackle the issues and opportunities in customer service? It’s no secret that Machine Learning (where computers are given real-world examples of data to learn from, and they can then apply what they’ve learnt to new situations – just like humans do) has hit the mainstream, and is finding real business application across a range of industries and problems. One branch of Machine Learning called Natural Language Processing, in particular, has advanced spectacularly in recent years as deep learning neural network techniques have trickled down from image recognition, availability of computing power and data has exploded, meaning that language models trained on huge data sets like the whole of Wikipedia are available to be used commercially for natural language tasks.
This advance in Natural Language Processing capabilities brings the possibility of solving some of the challenges of Customer service, firstly by thinking about the way advisors interact with systems. The complexity of systems an advisor deals with screens, forms, processes, compliance – compounded by the number of systems and channels impacts training time, performance, quality, and satisfaction (both advisor and customer). What if some of this complexity could be taken away? What if systems, process, and compliance were invisible to the advisor?
This is where Conversational AI steps in, re-imagining the interface the customer service advisor uses as a conversational interface. Instead of screens and forms, dropdowns and fields, the advisor interacts with their systems conversationally in the same way they are dealing with the customer. They use a natural language interface to ask for the outcomes the customer is looking for and the conversational interface guides them through what is required to fulfil the need (to gather the information needed, to make the customer aware of the points required for compliance, to offer complementary services, to recommend next steps). The conversational interface is unifying – it doesn’t matter which “back end” is required to fulfil the action, the advisor sees only a single contiguous conversation, whilst on the other end of the interaction, the customer feels listened to.
A Natural Language interface is inherently intuitive, it’s the human way of communicating, learnt from birth. This means that training an advisor population to use a natural language interface is a very low effort activity. Processes or journeys that required specialist training and perhaps a call or contact hand off in the operation can be deployed to everyone through a natural language interface, reducing the need for handoffs and simplifying the capacity management planning for the operation.
Once a natural language interface to services, processes and journeys has been developed it remains evergreen, not subject to the trends and emerging best practices in UX. Admittedly there is a conversational design capability required to ensure the conversations flow well and are well written, but do this once and a great conversational design will remain great. As the vocabulary of the business expands (products and services are added, or changed) this is a matter of training the language model that sits at the heart of the natural language interface, rather than recoding. New services will require new development effort to build the fulfilment, but the natural language engine just needs to be retrained.
It is no great leap to think that an effective Natural language Interface being used by advisors for personal service could also be presented directly to customers through self-service channels in exactly the same way (there might be some “skills” that you only present to advisors). Build these interactions once and present them in every channel that is appropriate for customer service interaction. A Natural Language interface is inherently multi-channel, whether that channel is text (Webchat, Website, SMS, Async Messaging) or voice (IVR and speech to text transcription).
Presenting a skillful, transactional conversational interface to customers in self-service channels moves on from the chatbot model of Question and Answer or links to knowledge articles. The customer is typically looking to meet a need, rather than ask a question. Meeting the need directly without redirecting to a form on the website (or to a person) should be the goal of the interaction – move customers from asking “How do i…?” to saying “I want to…..”. Getting users to ask for the outcome they want to achieve, rather than the steps required to achieve it changes the dynamic of the interaction.
It is also very possible to operate a Natural Language Interface in an augmented personal service mode. Whereby the interface “listens” to the customer (either real-time transcribing voice to text, or text interaction) and predicts or recommends actions and automatically proposes the conversational interface skill and recognises key information passed without the advisor needing to transcribe. Direct access to customer interactions in this way also open the potential to detect sentiment and emotion to help the advisor and to provide insight into the way customers experience the service they are receiving
To find out more about how Utterworks think about the opportunity for Conversational AI in customer service take a look at our Fluent Converse product below
Have great conversations with customers. We believe that with Conversational AI you can simplify your interactions with customers, reduce process time, improve data quality, and increase customer and advisor satisfaction. It’s no secret that customer service is hard. Customer care systems are typically complex, particularly for enterprises in regulated industries with significant customer numbers and…