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Nathan Sinnott December 6th, 2021

Introduction

Customer Experience (CX) decision makers are re-evaluating their CX strategies to remove inefficiencies in their processes.

CX leaders are realising the need (and business benefits) to automate repetitive tasks, to analyse data to help make time-sensitive decisions,  and to use automation to improve operational efficiency.

Research suggests that nearly 50% of CX decision makers in Australia and New Zealand alone plan to roll-out Conversational AI over the next 12 months, to help lower costs, resolve inefficiencies, and promote self-service, empowering customers with alternative channels of engagement.

The journey to Conversational AI is one that must be carefully considered, and executed with precision. We have encountered a number of businesses that have rushed to implement a solution while failing to give proper and deserving though to all elements of the Conversational AI roll-out which ultimately leads to disappointing outcomes for both the customer and the business. Poor planning, and poor execution almost always leads to a total re-design of the solutions and re-deployment.

When designed and implemented well, these projects deliver very positive outcomes for both businesses and customers alike. Customer and employee experiences are vastly improved as inbound queries can be handled through Conversational AI solutions, thus allowing agents to focus on complex cases. When blended, across Conversational AI and actual agents, businesses deliver on the promise of offering superior customer service across any channel. Successful deployments drive increased customer engagement, deeper customer insights, higher customer satisfaction, and better opportunities for revenue generation.

What is conversational AI?

Conversational Artificial Intelligence (AI) refers to technologies such as chatbots or virtual agents, which users can talk to, in place of speaking with an agent/human. The technology uses large volumes of data, machine learning, and natural language processing to help imitate human interactions, recognising speech and text inputs and translating their meanings across various languages.

Components of conversational AI

Conversational AI combines natural language processing (NLP) with machine learning. These NLP processes flow into a constant feedback loop with machine learning processes to continuously improve AI algorithms. Conversational AI has principle components that allow it to process, understand, and generate responses in a natural way, as if an agent/human were on the other end of the engagement.

Machine Learning (ML) is a sub-field of artificial intelligence, made up of a set of algorithms, features, and data sets that continuously improve themselves with experience. As the input grows, the AI platform machine gets better at recognising patterns and uses it to make predictions.

Natural language processing is the current method of analysing language with the help of machine learning used in conversational AI. Before machine learning, the evolution of language processing methodologies went from linguistics to computational linguistics to statistical natural language processing. In the future, it is expected deep learning will advance the natural language processing capabilities of conversational AI further.

NLP consists of four steps: Input generation, input analysis, output generation, and reinforcement learning. Unstructured data transformed into a format that can be read by a computer, which is then analysed to generate an appropriate response. Underlying ML algorithms improve response quality over time as it learns. These four NLP steps can be broken down further below:

How to create conversational AI

Conversational AI begins with considering how your customers and users interact with your product or service and the key questions they may have. You then use conversational AI tools to help route these groups to relevant information.

Identify the list of frequently asked questions (FAQs) for your customers and users

Frequently asked questions are the foundation of the conversational AI development process. These assist you in defining the key needs and concerns of your customers and users, which in turn helps reduce call volume and human to human interaction.

For example, if you are an eCommerce retailer, your initial list of FAQs might be:

Think of this list as one that can grow with time. Identify and get the first smaller list working, and then expand it as you go.

Use FAQs to develop goals in your conversational AI solution

Your FAQs form the basis of goals, or intents, expressed in the user’s input. You will need to specify your goals. Then, these can be plugged into your solution.

This is where you now begin teaching your conversational AI tool/solution the various ways a customer or user might phrase or ask for information. So, take purchase arrival date as an example from the list above. You can have a handful of ways that can be phrased or asked. “When will my purchase arrive”, “what date will my <product> arrive”, “when will I get what I purchased”, “how long does shipping take” and so on.

Chatbot analytics will also give great insights into how customers or users are asking questions, so that you can refine your approach and broaden the teaching of your tool.

Use goals to understand and build out relevant nouns and keywords

Think of nouns, or entities, that surround your intents. In our example, we’re using the delivery timeframe for a purchase. So, we would  create an entity around shipping and delivery dates.

A number of values might fall into this category of information, such as “order number”, “shipping type selected”, “address”, etc.

Combined, to offer helpful and efficient engagement with the customer

Each of the elements above combine to deliver a conversation with your customer. Nouns/Intents allow a machine to understand what the customer is asking, and entities help form suitable replies.

Together, goals and nouns work to build logical conversation flow based on the customers inquiry & needs.

Conversational AI uses

Chatbots and voice assistants come to mind when most of us consider conversational AI. And while an AI chatbot is the most popular form of conversational AI, there are many other use cases worth considering:

The Benefits of conversational AI

Cost efficiency

Unfortunately, the human capital cost of a support, customer service or help team/department can be pricey. This price rises significantly when you begin offering responses to queries outside of normal business hours, on weekends, or public holidays.

Offering these functions however via conversational AI heavily reduces cost, both across human/staffing costs, office space requirements, training, annual leave, overtime or after hours rates, and a range of other cost centers.

Boosting sales and engagement with customers

Customers seek instant gratification in their engagements with businesses these days. And being able to deliver this is a big step toward providing class leading customer service.

Chatbots have the ability of responding immediately, without the need to queue customers, and are available 24/7.

When customers are happy, and receive the service they are seeking, fast, they tend to be open to buying more, which is a great opportunity to use your bots to offer suggestions for cross and up-selling.

Scale. Fast. Easy.

Scaling conversational AI is easy, and cheaper than hiring, training and retaining staff as you grow. It’s also faster, when you have the right partner helping you.

Growth Signals

Newpath Web and AI

AI requires a high-performance blend of humans & machines to achieve great outcomes. Newpath Web can translate your business goals into reality through our services, solutions, and capabilities.

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