From the early days of command line interfaces to the clunky chatbots of the early 2000s, the evolution of conversational interfaces has gone a long way to becoming a part of our everyday lives. There is an obvious reason for that – humans find it easier to communicate through natural language rather than a series of user interface button clicks and selectors. It’s still not perfect, however, and even though the current market of software tools to enable conversational experiences is growing day by day, we are still at the early days of the adoption of such communication interfaces.
The evolution of conversational experience
There are two main ways conversational experiences were utilized in the past, it was either primarily a keywords guided conversation that followed some decision tree-type of logic or simply recommendation engines that would search for things that were the most relevant to the user query.
Nowadays it’s more flexible than that. Chatbots are used for a variety of assistant-like tasks where natural language processing enables various levels of dynamic extraction of entities and values from unstructured text. You are able to place orders, set alarms or inquire about specific topics within the knowledge base. Conversation is becoming a ubiquitous mode of communication that has the potential of bringing much more efficient management of the customer journey.
The value of data
What’s more important than the mode of conversing, however, is the integration of conversation data into the overall user experience across the lifecycle of the customers. Conversational data is becoming an asset that many businesses are starting to utilize as a valuable data point. Everything from sentiment analysis for customer support quality assurance, to adjusting the service experience based on historical metrics. Conversation is gradually becoming a mode of data transfer, much more seamless than website forms and text inputs.
In this way, businesses are also able to build a much more gradual extraction of the required data points without being overly intrusive. Be it chatbots for weight loss or general financial literacy applications, conversational interfaces are serving as a much needed transition between a soul-less user interface without a character to something you feel much comfortable sharing your personal data with.
Obviously, privacy concerns are still in place and businesses have to make sure that things like privacy policy and terms of use are always available to the customers whenever they feel cautious about sharing their data within conversational experiences, simply because there are no checkboxes to agree with on each message that comes in. This also brings another aspect of the continuous data exchange, one where there is no finite submission of the data following persistent storage of that submission. Instead, it becomes a stream that is being captured as the conversation continues.
Why implementation should be carefully considered
Though conversational interfaces are becoming a usual mode of interaction with certain businesses, for others it’s still a new experience that customers are not used to. That’s why it’s crucial to manage consumer perception and evaluate the best rollout strategy for the new conversational implementations.
There is also a delicate balance to keep to ensure customers are not routed between endless redirects from one point of automated experience to another and to set expectations for the limitations of the automated experience. With an abundance of conversational use cases across the web, it’s becoming a modern day equivalent of the phone line to get in touch with a business.
The omnichannel aspect of this adoption is also not to be neglected. Customers already assume that different systems are able to talk to each other behind the scenes to make their experience seamless, whether a person is placing an order via messenger chatbot or conversing with a website live chat or email. This creates a never-before-seen fluidity of the customer journey, which previously was channeled into one specific mode of communication. If there is a disruption in the links between those modes, it can become a disaster. That’s why finding the right software infrastructure to support these use cases is one of the key aspects of implementation.
For some businesses, the best approach is to build their own infrastructure to support conversational experiences, but for most, it’s a question of finding the right vendor. No matter how simple the implementation may sound, it will still require some level of sophistication when it comes to machine learning and conversational interfaces overall.
Hiring qualified data scientists for in-house expertise is becoming extremely challenging due to the competitive nature of the market so the right strategy comes down to figuring out how to deliver the most seamless experience with the least budget spent. Thankfully there are plenty of vendors that offer out-of-the-box conversational experiences that are able to support a variety of use cases across different modes of communication, so the most challenging part is figuring out the right vendor and dedicating resources to support the out-of-the-box solution.
Omnichannel conversational AI can be a key competitive driver for your business, but it must be managed thoughtfully and with consideration for how best to use the data to improve the overall customer journey. Organizations that focus on getting it right across every medium are the ones who will succeed in our digital-first economy.
