Computers are logical. Human beings can be logical too, though the way we talk doesn’t always reflect that. So how can these two entities effectively communicate with one another? That gap is bridged through conversational AI and its component parts (e.g. natural-language processing, natural language generation, and dialog management). Collectively, they can create a conversational dialog that helps people get answers to their questions. But the foundation for any effective conversational AI experience starts with having the right strategy—and developing the right strategy begins with knowing which questions to ask and steps to take. Following are nine pillars to a conversational AI strategy that fosters sustainable, useful, and efficient customer engagement.
1. Know your audience
Just as when building a website or other engagement platform, developing a conversational AI strategy starts with asking the following questions: Who are we targeting? What do they need? How can we help them? What do we want them to know? Drilling down to these particulars will help crystallize your thinking in terms of the scope of the project, your audience’s need and how a conversational AI experience can meet them.
2. Support multiple languages
If your target market includes users who speak a language other than English, expanding your Conversational AI (CAI) solution into multiple languages should be part of your strategy. CAI can easily, and quite accurately, detect a person’s preferred language, based upon their IP address, browser settings or HTML attributes. And while many people speak more than one language, the ability to interact with a chatbot in a user’s mother tongue goes a long way toward developing goodwill and fostering brand loyalty. Some CAI solutions use translation engines to respond to users in their preferred language, while others may require that responses be translated and fed to them in advance. If you need third-party support, for accuracy’s sake, look for a partner that can source data from native speakers.
3. Dissect your FAQs
Conversational AI is yet another platform for employees and customers to get their questions answered and problems solved. As such, your conversational AI strategy should include a thorough study of company FAQs, revealing the most common issues and questions to which your conversational AI solution will need to respond. Furthermore, FAQs will highlight the named entities that a solution’s natural language processing algorithm should be trained to recognize. These entities are clues about the topic at hand, and give your conversational AI solution a cue about how to respond.
4. Create automated workflows
By its very nature, an FAQ doc highlights the most common questions. As such, creating automated workflows for each of these questions and potential outcomes can help streamline employee and customer support, saving your company time and money. Furthermore, mapping out the different outcomes for each question or issue may help identify gaps where additional knowledge base content needs to be created or company policies need to be clarified in order to provide a consistent level of support.
5. Create consumable content
Your conversational AI strategy should also incorporate customer support content. For example, a truly useful CAI solution must be backed by informative, easy-to-understand knowledge base (KB) content. But don’t boil the ocean. Oftentimes, a person’s question or performance issue stems from one thing in particular. We recommend creating succinct chatbot flows that are backed up by equally focused KB articles, that include links to relevant content.
6. Empower your service agents
Even the most well-developed AI application can’t always answer a person’s question. In fact, what often sets apart the most effective CAI experiences is knowing when, and how, to seamlessly handoff someone to a service agent, giving agents the necessary context of a service ticket, and empowering them to help resolve calls by surfacing relevant KB articles. At this point, the CAI application on the front end effectively becomes an augmented intelligence solution on the back end. Enabling this functionality requires a well thought out training process for entity recognition and metadata tagging of support content.
7. Establish a rigorous data management practice
The successes of tomorrow are built on the consistent business practices of today. With that in mind, it’s critical to develop a data management strategy and correlating disciplines that ensure you have access to quality datasets, which can help fine tune the performance of your AI application. Learn more on how to establish a solid data foundation for Conversational AI in our eBook.
8. Stay compliant
There are numerous potential points of exposure for user data, and there are compliance requirements from government/industry regulators on multiple fronts, e.g. GDPR, HIPAA, SOC2, etc. Furthermore, the use and storage of information related to areas such as identity, health, and online activity is being watched more closely than ever. As such, it’s essential that you develop a compliance strategy that’s robust, but which can also be easily adapted to unforeseen compliance issues.
9. Track performance data and retrain your AI
An effective CAI application is not static. Rather, it is constantly being retrained to reflect a company’s latest programs, products and features, as well as changes in the vernacular of a language. Furthermore, it’s essential that companies track the resolution rate of their CAI, i.e. its ability to resolve a service ticket. Tracking its performance will help identify where additional algorithm training and support are needed.
Conversational AI has amazing potential to streamline costs and successfully resolve customer service calls. Success relies on having a well-thought out conversational AI strategy, along with complementary business and data management processes, that foster useful and efficient engagement.
