Chatbots have been used for lead gen and content marketing use cases for a number of years. Brands have used both rule-based and AI-powered chatbots to share relevant content, set up demos, guide customers through sign-up, and automate customer service workflows – all in an effort to deliver consistent, seamless experiences across channels and regions .
But that barely scratches the surface of what conversational chatbots can do. What’s next for Conversational AI (CAI)?
Embed Conversational AI in the Larger CX Strategy: Best Practices
Conversational AI solutions play a crucial role in connecting the dots between different elements of the customer experience (CX). But in delivering these solutions, brands need to focus on the journey across phases and channels, not technology, said Tony Lorentzen, general manager and senior vice president, Intelligent Engagement at Nuance Communications, a CAI company recently acquired by Microsoft. “Marketers tend to forget about the transition from one channel to the next. If a user is interacting with a chatbot online and requesting a call from a human agent, it is reasonable for that user to assume that the agent has a history of interactions with the chatbot. Unfortunately, this is not always the case. When embedding CAI solutions, brands need to understand how to create experiences that can seamlessly transition across all channels by collecting and using data from every interaction to inform future experiences. “That is, every channel (web , Cell phone, voice, chat, etc.) with a common intelligence pool.
While this is the dream state for any marketer, it is a good place to start, said Saurabh Kumar, CEO of Rezolve.ai, an AI-powered employee help desk company, in identifying pain points (or drop-off points) which customers tend to arrive (in the trip) and assess whether CAI can help smooth these transitions. CAI’s role, he said, is evolving from being a point transaction tool (setting up a demo meeting) to being a cognitive assistant wearing various hats like customer success, product support, data analyst and more, throughout the customer lifecycle using context and history as a guide. “What can begin as simply setting up lead capture / demo meetings can move into onboarding, setup, first-time user training, analytics and metrics assistance, proactive idle feature emergence, and ongoing service. Each of these areas can be redesigned with CAI to provide better experiences, reduce costs and increase satisfaction. ”
Arun Bhattacharya, Global VP of Product Marketing at Kore.ai, which automates dialog interactions, also warns against doing everything at once. “Start with a channel like voice or text and once you’ve achieved ‘minimally workable success’, expand your CX journey. This is the only proven way to successfully incorporate Conversational AI into your corporate CX strategy.”
Related article: Getting Started Designing Conversational AI
Beyond Greeter Bots: New Opportunities for B2B Use Cases
What makes CAI so attractive is its ability to influence not only efficiency and productivity, but also sales and profitability.
Since the Anything-as-a-Service subscription economy is geared towards growth, retention, renewal, and growth are critical. While great customer service helps build customer loyalty, growth requires more proactive measures. CAI can help here, especially if it supports the contextual understanding of intelligent cross-selling, upselling and customer loyalty, said Bhattacharya. “The value of Conversational AI goes far beyond just talking to the customer. The collection of data such as usage and profiles across all delivery channels in order to build up a contextual understanding of language, text and chat is a central new use case that is being driven by the increasing omni-channel nature of B2B marketing. “Other growth areas are Channel partner management and community management processes as well as market expansion via conversational AI, which enables multilingual interaction.
Kumar believes that promoting platform adoption, a leading indicator of retention, provides high levels of innovation and utility in the conversational AI field. “Just-in-time micro-learning while the user is in the workflow is a great application. For example, if a user is using a new SaaS platform, the question “How do I generate this report” can trigger a 5-minute “nibble”. Report generation training. “Such applications can greatly reduce the administrative burden of B2B platform administration and increase user productivity.
Related article: Conversational AI needs conversational design
Make your company fit for conversational AI
Just as B2B marketers want Conversational AI to be plug-and-play, there must be a number of surrounding processes and functions in place for it to pay off in a reasonable amount of time.
Organizations need the data readiness to build real-time CAI models. According to Roeland Jimenez, founder and CTO of Wave5, a data analytics and CAI company, data-driven companies benefit from faster time to value from CAI. “All departments within an organization should be data providers for the rest of the organization. This requires an accessible data platform with strong data governance and transparency. ”Unfortunately, data remains a challenge for CX in general. “Most large companies have different data sources and would need their own project to integrate the right data sources,” said Nuance’s Lorentzen.
Having clarity about the in-house skills and resources available, he added, will help determine the types of products, partners, and services required for a successful deployment. When it comes to conversational design willingness, Bhattacharya recommended hiring linguists as well as people with a creative writing background. “Conversational design is like imagining many variations of the same basic scene in a play without necessarily knowing the motivation of any of the actors. Building a bot with just traditional IT people won’t do it (and I’ll say that as one!) “
Connecting the data and processes with APIs so that AI can build context is another challenge. “Successful understanding of conversations is greatly enhanced by the ability to apply custom context to them. No conversation takes place in a bubble, a prior interaction or an implied context (time, place, environment) must always be applied. User A will assume that the bot is fully aware of their situation – which APIs allow – just like we do in human-to-human communication. ”
Ultimately, Kumar argued, marketers need to challenge conversational AI to do things for the user – not just answer questions. That means providing it with history and context, regardless of the channel.
Conversation – whether human or AI – is a mixture of art, science and context
The possibilities with conversational AI seem endless. Marketing leaders will focus on balancing machine and human elements as they design and deliver great, call-driven experiences across the customer’s lifecycle. But first we have to accept that conversation is a mix of art, science, and context.
“It is impossible to imagine everything a user could say or understand everything perfectly,” said Bhattacharya. “But the point of CX is not to be perfect, but to transact on a user’s terms, not a designer’s unique view. There is no one right answer.”