The Сonversational AI (CAI) space has come a long way over the last few years. As users prefer to communicate over digital channels, the demand for conversational AI continues to grow. Organizations worldwide are increasing their CAI investments in response to this trend and maturing how they leverage Conversational AI to supplement customer service agent interactions to deliver seamless customer experiences across a multitude of channels.
Customers’ expectations have also matured due to the proliferation and ubiquity of conversational interfaces and virtual assistants. They now demand easy, effective interactions that are personal and contextual to their current needs.
To truly elevate mundane conversations, improve engagement, and add value to their customers in 2022 and beyond, organizations need to focus more on the following practices to craft delightful experiences.
From FAQs, to transactional experiences
Conversational interface projects often start with a proof of concept involving launching a virtual assistant that can automate responses to frequently asked questions (FAQs) via chat or voice.
Organizations that want to increase customer satisfaction and achieve business goals need to start looking beyond just FAQs to reap the actual benefits of conversational AI.
Today’s advanced Conversational AI systems that utilize natural language understanding (NLU) can automate many complex transactions to make life easier for customers and internal teams. For example, banks could enable bill payments via virtual assistants instead of just navigating customers to a ‘how to pay’ webpage. A food retailer could allow customers to order food using a virtual agent rather than just navigating to a ‘menu’ page on their website. Check out more Use Cases of Conversational AI in the Finance industry to increase customer satisfaction and automate your processes.
Building a transactional virtual assistant does not necessarily mean total call center automation with all possible transactions. Organizations need to take a structured approach and use data to prioritize key transactions that are high-volume and high-impact. This will help them deliver more value to their customers and move them closer to meeting their business objectives.
From linear, to flexible and cyclic conversations
Organizations need to be mindful that they are creating experiences for real people who are on the other end of the virtual assistants. Therefore it is paramount to keep customers in mind during the entire process. This shift is profound and places the onus on organizations to deliver a seamless user experience to lessen the user’s cognitive burden.
To continue providing a fluid customer experience, organizations need to anticipate and map out every possible scenario, query, and customer response. They need to design flexible conversations so that customers can converse using their own words in addition to picking from pre-defined menus. They should also be able to change the direction of dialogue or request additional information along the conversation’s path. Lastly, the Conversation Design needs to be cyclical so customers can pivot and circle back to the conversation as per their preference without starting over. Human to human conversations themselves are not linear and neither should conversational interfaces.
Complete Guide to Conversational AI in CX
From release and forget, to iterating and tuning
Many organizations that build virtual assistants invest in upfront research and design to understand the customer journey and context. They sometimes, however, drop the ball on iterating and fine-tuning the experience after releasing the virtual assistants to actual customers.
It is crucial for organizations to monitor and evaluate actual conversations to really understand what is working and what isn’t. Reviewing user sessions to investigate errors and determine how to improve the experience should be an integral part of an ongoing sustainment plan.
Continuous iteration or ‘bot tuning’ is another critical practice for maintaining a balance of necessary intents and their training data. Tuning could involve various activities like adding, removing, or modifying utterances. Removing intents that don’t add value is just as important as creating new ones.
This results in customer experiences that are as seamless and as simple to navigate as possible. It also increases customer engagement and containment within the conversational experience.
From pre-defined answers, to Natural Language Understanding and Conversation Design
At its core, conversation design aims to mimic human conversations to make digital systems like virtual assistants easy and intuitive to use. The challenge is to make interactions with these systems feel less robotic by understanding the context and purpose of the customer in order to direct them to relevant solutions.
Many organizations, however, still employ hard-coded or rule-based pattern matching with small rule-sets for their conversational interfaces. This results in higher abandonment rates, low engagement, and perceived project failures.
Natural Language Understanding (NLU) technologies utilize machine learning and training data that allows them to understand user utterances without the need to manually hard code all the pattern matching logic. NLU platforms also provide hooks into domain-specific knowledge bases and forums.
By integrating and maximizing the power of NLU platforms, organizations can enable virtual assistants to respond to human queries efficiently and effectively, improving customer engagement and providing an overall positive customer service experience.
From disjointed multiple bots, to a seamlessly integrated omnichannel experience
With an increase in the development of virtual agents, some larger organizations are facing a new challenge. Individual departments are creating conversational interfaces with a narrow scope of handling queries related to very specific use-cases or business functions such as HR or IT. As a result, accessing and discoverability of the numerous virtual assistants becomes a challenge for users.
When appropriate for their situation, organizations can overcome this challenge with the introduction of a “master virtual assistant”. This assistant can be made responsible for handling a range of tasks for the customer by understanding their intent and routing the request to the use-case-specific virtual agent. For example, a financial institution may have separate chatbots to handle commercial and consumer mortgage use cases and a master chatbot that seamlessly manages the interactions across them.
Read more about which processes that could be automated for HR with help of AI.
Is your organization ready to level up its conversational AI experience?
Organizations need to remember that launching a virtual assistant isn’t the destination but a journey. It’s essential to keep in mind that success with conversational AI depends on more than just technology. An elegant conversation design based on research and continuous optimization is also crucial to make virtual assistants more intelligent, intuitive, and engaging.
Whether you’re looking to develop the knowledge and capabilities to scale your conversational AI strategy in-house or find a partner to work with – MOC is available to assist.
MOC partners with the world’s leading companies to design and develop conversational experiences. Let us help you connect your brand with customers where they communicate today – voice or chat.