Conversational AI is an emerging market that is taking over the business world by storm. Indeed, interest in chatbots, voice bots, and other AI-driven virtual assistants is growing by leaps and bounds, so much so that the conversational AI market is expected to reach $18.02 billion by 2027, growing at a CAGR of 21.02%.
While chatbots and other digital assistants have immense potential to improve customer satisfaction (CSAT) levels, enhance employee engagement, empower organizations with exceptional experiences, and improve brand image, not every conversational AI project may prove to be successful.
That’s where an effective conversational AI design comes into the picture. Effective conversational AI design builds the user’s trust and boosts their confidence in the virtual assistant. It keeps them engaged and brings them back again and again.
When a user enters a chatbot, they may be apprehensive about the quality of interactions at first and whether the chatbot can resolve their issue. While chatbots automate conversations and offload human tasks, the discussions should still feel human and akin to having a one-on-one chat with the brand.
So, what are the dos and don’ts of conversational AI? What best practices can you keep in mind while designing and implementing a chatbot?
Here are some key chatbot considerations and DOs and DON’Ts that organizations must be mindful of –
While a chatbot can do many things for your organization, answering queries, executing simple tasks, or even pulling relevant information, it’s essential to ensure it achieves its actual purpose. Narrowing down the chatbot’s purpose can help the organization ensure that the scripts, content, and functions required to make the chatbot successful aren’t ambiguous. Answer questions like-
It’s imperative to set the right expectations when it comes to what your chatbot is supposed to do. Moreover, in large organizations with several departments, considering the needs of all business divisions may be tough and lead to delays in time-to-market. Rather, deploying separate conversational AI solutions for disparate use cases will be the wiser option.
Jumping into the world of conversational AI without the right expertise in your hands can be daunting and make the entire process extremely disorganized and uneconomical.
Instead of burdening your IT team with the responsibility of learning, designing, and optimizing conversational AI technology and embedding automated workflows, try choosing a conversational AI tool from a host of vendors available in the market.
Depending on your needs and intended use cases, you can either go for a custom solution, a specific service/functional offering, or else a platform-based approach.
A platform-based conversational AI offering leverages game-changing no-code or low-code solutions (LCAPs) that help non-technical users build, improve, and adjust apps based on needs with little or no coding.
Data privacy and cybersecurity are significant conversational AI implementation considerations. Since chatbots can handle sensitive customer information at times, incorporating robust security protocols is a “must-do.”
After all, a cyberattack on the PII of customers has an enormous financial impact and can significantly hamper the brand image of your organization.
Keep it simple for the user. Design the chatbot to minimize the list of asks for the user.
Forcing customers to interact with a chatbot in the name of modernizing IT, scaling operations, or reducing costs isn’t the best option. While providing an intelligent, natural language interface that simplifies problems, understands context, and allows users to speak to the bot on their own terms is the best option, it’s also necessary to build an escalation pathway that supports an effortless transition to a human agent.
So, when an issue arises beyond the bot’s scope, or if the customer seems irate (sensed through sentiment analysis) and willing to speak to a human agent only, the bot must be able to escalate the call to ensure quick resolutions seamlessly.
As conversational AI technology progresses and your organization needs mature responses to deal with different intent types, make sure your chatbot is equipped to harness user data and deliver more meaningful and personalized engagement.
If required, keep on adding new responses and natural language and machine learning capabilities to progressively make your bot better and more human-like.
Integrations with backend and legacy systems, such as ERP, CRM, or databases, are essential if you want to provide seamless support to your customers or employees.
Conversational AI platforms when integrated with other systems can help in checking the status of orders to answering queries, address problems from diagnosing a problem to resolving issues quickly. Also, with the data from the systems the Conversational AI platform can provide valuable insights. So based on your use case, identify what integrations are a must for your case.
Goal completion rates, bounce rates, and customer satisfaction scores are great examples of significant metrics and KPIs that determine the success/failure of conversational AI implementation.
As a result of monitoring, organizations can continually develop better and more seamless experiences for users and build a winning conversational AI strategy.
At Acuvate, we help clients develop and deploy powerful chatbots, voice bots, and digital assistants, keeping in mind the DOs, DON’Ts, and must-haves of conversational AI.
Our enterprise bot-building platform, BotCore, uses top-notch AI and natural language processing technologies to build intelligent bots and support exceptional customer and employee experiences.
To know more, please feel free to schedule a personalized consultation with our experts.