The challenge of messaging for businesses is scale. How do we keep up with the volume of messages that our customers send over the many different messaging channels they use while simultaneously making it a positive, accurate, and quick experience?
The only way we can carry on a dialog with our customers at scale is through conversational automation. We stand at a crossroad of digital transformation, looking right at non-AI chatbots and then left at Conversational AI. Companies that truly want to provide customer experiences at scale are shifting their thinking to the left and embracing innovations in AI.
Instead of people handling every interaction, think about people designing interactions. We teach artificial intelligence systems how to understand and fulfill customers' needs using life experience in regard to how they interact with our customer service teams. And we enable the system to start learning on its own based on feedback from our customers. Finally, we tune in to our customers' moods and interaction style and we mirror that, so their conversation with a machine feels relevant, efficient and even connected.
What I just described may not sound like any chatbot or virtual assistant you've ever interacted with. Chatbots are somewhat unique in that everyone has had some kind of experience with an automated agent, and very often these experiences have been bad. For many of us, the primary experience we have with automated conversations is through Siri, Alexa or Google Assistant. In short, these interactions are magical (though somewhat stilted) if they work and frustrating if they don't. On the web, the experience with chatbots has fallen short in other ways. Instead of being of service, many chatbots provide limited real value.
Tom Petrocelli wrote an article on CMS Wire that summarized this experience nicely. He points out that chatbots need more structured inputs than people do; appear to be using trial and error rather than showing true understanding; are a transparent effort for companies to reduce cost; are less efficient than using a mobile app; and, the most pointed critique: chatbots yield the same results any search engine would but with more effort. But these experiences on your phone or on the web aren't truly virtual assistants. They are either voice command interfaces, in the case of your phone, or either web forms or search engines put into a different interface in the case of the web. Whatever you call it, the user experience isn't always great, and it causes us to enter into interactions with chatbots with some skepticism.
As business leaders we should be asking: How can I create an experience for my customers that they will prefer over human-to-human interactions? How can my organization realize cost savings from automation but really make it a win-win for the customers and employees who are using conversational interfaces? We want experiences that are intelligent, connected and that draw our users in and please them with results rather than frustrating and alienating them.
In that sense, we want to ideally create experiences that:
- Support our users' desires to interact through conversational interfaces – both messaging and voice
- Provide 24/7 coverage
- Help users accomplish goals through interactions that feel *intelligent*
And at the same time, these experiences should:
- Reduce customer effort
- Scale support while reducing operational costs
- Provide consistency of experience across channels
- Amplify the value of existing investments in automation
This is the domain of conversational AI automation and “intelligent” chatbots, or AI chatbots.
What differentiates conversational automation from simple chatbots are five key characteristics:
1) They use natural language understanding to determine what a user wants to do and important details they are sharing.
If the user says 'I was in a fender-bender in my truck yesterday and I have a big dent in my bumper' that virtual assistant should not ask:
"Do you want to file a claim?"
"When was the accident?"
"Which vehicle was damaged"
Or
"What kind of damage was there"
We've already shared some key information and an intelligent chatbot should recognize that.
2) Conversational automation understands a user's context before the conversation starts.
Intelligent chatbots should know whom they are talking to. They should be able to carry on a dialog with an awareness of who the user is, why they are starting a conversation and what they want to get done.
3) The scope of the bot should fundamentally be solving problems for users, not just acting as a search engine.
While providing information to users is valuable and you may have search technology as a component of your bot's intelligence, users should be able to accomplish more than they could by simply searching on Google.
4) Conversational automation is centered on connecting users with the systems and data they need to access.
From an end user's point of view, an intelligent chatbot can provide information and solutions. That means accessing information about a user's relationship and transactions with your organization and being able to take action.
5) Conversational automation takes advantage of existing and ongoing investments in automation technologies.
You may have invested in RPA or business process automation technologies to improve the efficiency of your customer support operations. Conversational automation takes advantage of the work you have already done to embed your business processes into the systems that make them faster and less error prone.
In short, conversational automation is:
- Intelligent
- Contextual
- Problem-solving
- Connected to systems
- Makes automation accessible
And all of this is in the effort of reducing customer effort by making your operations more efficient. Conversational AI helps you achieve your digital transformation goals by bringing intelligent automation to the customer experience.
About Cognigy
Cognigy is a global leader in Conversational AI to support customer service automation. Its low-code platform, Cognigy.AI, enables enterprises to automate contact centers for customer and employee communications using intelligent voice- and chatbots. With precise, reliable intent recognition, human-like dialogs and seamless integration into backend systems, Cognigy.AI creates superior user experiences and helps companies reduce support costs. Cognigy.AI is available in SaaS and on-premise environments and supports conversations in any language and on any channel including phone, webchat, SMS and mobile apps. Cognigy’s worldwide client portfolio includes Daimler, Bosch, Henkel, Lufthansa, Salzburg AG and many more. Learn more at cognigy.co
Author: Derek Roberti, VP of Technology, Cognigy