Conversational-AI-Customer-Experience

Conversational AI Powers the Customer Experience of the Future

We used to think of customer experience as a priority for just a narrow band of industries – retail, hospitality, travel. But today, customer experience has become the holy grail for enterprises across sectors. From banks to utilities, insurance, healthcare, telecommunications, even the public sector –organizations of all shapes and sizes are prioritizing customer experience to engage and retain their existing and future customers.

Over the years, many businesses have implemented chatbots to improve customer experience, speed responsiveness and improve brand stickiness, but chatbots are often siloed in specific portions of the enterprise and lack connection to the data they need to answer customers’ questions. They are difficult to scale across the enterprise and expensive to rip and replace as business needs and customer expectations change.

Personalization is key to the way we think about customer service today. Conversational artificial intelligence (AI) uses automation to provide personalized experiences, understand customer inputs, and build relationships with customers. With the right supporting technologies, it can facilitate one-on-one conversations that are context-aware, informed by past interactions and fully comprehend complex customer inputs.

What Is Conversational AI?

Conversational AI technology allows people to use every day spoken language when interacting with applications and devices. Conversational AI uses natural language processing (NLP) to arrange and analyze a communication that can generate the best possible automated response. It can switch contexts as the human does and anticipate needs based on prior interaction.

Conversational AI can manage multiple requests, circling back to address one after it has resolved another. Because it is capable of switching topics, the customer can ask multiple questions and the AI can eventually bring it back to the primary issue. This eliminates the need for customers to repeat themselves and reduces the chances of forcing a call back or a human handoff.

With conversational AI, you can create chat interfaces and virtual assistants that draw from the same pool of data across your enterprise. Customer interactions with automated platforms will have more consistent quality, whether customers are calling in an order or texting tech support to resolve an issue.

Accessing and Comprehending Relevant Data for Personalization

An important – and often overlooked – piece of the customer engagement puzzle – is the operational data layer (ODL), also known as a Data Fabric. The ODL is an architectural pattern that integrates and organizes enterprise data currently trapped in silos. It pools together data resting in mainframes, SQL databases, legacy systems and other programs by acting as a layer over the data sources and providing the data to systems that need it. The function of the ODL is to simplify and centralize access to your business data so it can be used in AI-driven conversations.

The data unlocked by accessing your ODL isn’t always easy to comprehend by conversational AI. A variety of tools and platforms – including deep learning, NLP, optical character recognition (OCR), machine vision and machine learning (ML) – leverage AI technology to pre-process, classify, extract and validate both structured and unstructured data. Structured data is information that is organized and straightforward in nature. It usually comes in the form of letters and numbers arranged in rows and columns and commonly exists in SQL database tables and spreadsheets. Unstructured data comes in a wide range of formats, including text files, images, audio and video. 

An example of an AI-powered tool that processes unstructured data is Amazon Comprehend. It works with Amazon Textract to classify, identify and read documents. It comprehends key phrases and customer sentiment and relates them to other pieces of data in an interconnected graph. That data is stored in a graph database and can be called upon by the analytics or conversational AI tools to be used in reports or customer interactions.

Digital Assistant Onboards New Customers and Reduces Manual Effort

Here’s a real-life example. When a customer buys an automobile insurance policy with a large insurance provider, they receive an email or a letter requesting required documents and pertinent information. The customer often needs to provide, for example, a copy of their driver’s license, proof of prior insurance, the VIN, car registration and payment information. These documents are usually submitted as attachments over several emails.

For the insurance provider, the process typically involves a great deal of manual work, sorting and typing data into the company’s policy systems. Because it is manual, it often results in delays, errors and extra costs.

A conversational AI-powered digital assistant can help reduce the manual effort involved in managing the onboarding of a new customer, improve the customer experience and eliminate costs and delays.

In this case, a customer-onboarding digital assistant can interface with the customer using conversational AI to accomplish the following tasks:

  1. Capture and upload documents. The digital assistant will guide the customer through multiple steps in the onboarding process, capturing information and validating uploaded documents or other unstructured data through image and OCR technology. It will classify, comprehend and store that data for access by policy and claims systems.
  2. Automate FAQs. This is the classic chatbot use case, with a digital assistant answering frequently asked customer questions within the chat, eliminating the need for those requests to be handled by an agent. Customers can enter their questions in everyday language via the messenger area in the virtual assistant, at any time throughout the session. This deflects significant traffic from human staff who are better able to answer more complex questions.
  3. Human chat handover. At any stage in the conversation, if the virtual assistant cannot manage a query or if a customer requests it, it can hand over the conversation to a human agent. The onboarding bot is integrated with the company’s live chat system so this can happen seamlessly during office hours. For out-of-hours operation, the virtual assistant can schedule a call back, so a human agent is able to follow up the next business day.
  4. Secure integration with backend systems. The digital assistant is integrated securely with the company’s legacy customer policy systems through RPA or an ODL, so information can be added to or accessed from the customer’s record without any need for staff to key in information to the system. Keeping customer data protected using multiple security measures is an important piece of planning for this feature.
  5. Chasing the customer. The virtual assistant can reach out to customers who are still in the onboarding process via a message with a link to a secure messenger that runs on a phone’s browser or a dedicated app. Using that link, the customer can continue to interact securely, upload documents and enter personal information.
  6. Provide incentives. Since the digital assistant is proactive, it can reach out at any desired time or as frequently as needed to remind the customer to submit their documents and answer any remaining questions. This means human agents do not have to do as much follow-up. On completion, the assistant can send a promotional offer to the customer to serve as a welcome reward and as an opportunity to market other insurance products.

Getting Started with Conversational AI

Conversational AI makes it easy for customers to find information and engage with the company, it offers customers personalized products, recommendations and content more likely to meet their needs, it can provide agent decision-support with next-best offer suggestions, and it can scale up and down to serve new customers in a cost-efficient way.

The conversational AI market is young and changing fast. It is difficult to know which tools are right for your IT environment and your business. ISG can help you define a strategy for conversational AI and find the right partners to help you integrate with other channels and design interfaces that work for your customers. Contact us today to find out how we can help.

Read about how we are bringing the benefits of conversational AI to our clients in our recent partnership with Cognigy.

About the author

Cass Bishop is a Director in the ISG Automation unit. He is a dedicated leader who implements global client-focused solutions which drive dramatic improvements to business process and automation lifecycles.  Cass brings over 20 years of Technical Implementation and Program Management experience in transformative technologies with a focus on System Development, IT Automation, Cloud, IT Service Management and Business Process Automation.