“I want to talk to a HUMAN”! How to give your Customers a human like Chatbot experience?
Implementing a Chatbot for an organization’s Customer Service Center might seem like a simple decision to make, until you are knees deep into creating decision trees, defining tasks, integrating with corporate systems, deciding between cloud or on-premise installations, digging into conversational archives and historical data, etc …
While the success of the new Digital Agent(AKA Chatbot) is evaluated indicators like Response Time, First Contact Resolution (FCR), Conversion Rate, Resolved Issues, Customer Retention, and Customer Satisfaction, among others. A key characteristic plays a major role in this success; the ability to provide a “human-like” conversational experience to customers to better handle their requests, and to avoid customer churn caused by the limiting User Interface (UI) menus or the inability to understand what the customer wants.
“humanlike:
[hyoo-muh n or, often, yoo‐] In a manner resembling that of a human or humankind.”
A conversation is defined as an exchange between two or more sides in which thoughts, feelings, and ideas are expressed, questions are asked and answered, or news and information is exchanged, and while many Chatbots today offer the possibility of answering question, and attending customers’ requests, few of them are able to provide these services without the need to limit user’s input by command based UI menus, or to follow strict decision trees logic.
This is where Free-Text Chatbots came to the rescue, offering customers a platform to enter free text in a conversational experience similar to what humans are used to have, this is where the term “human-like” conversational experience came from, and with different factors affecting the success of the new digital agent, (and by success I mean a satisfied customer, a returning customer, and a customer recommending the service for another customer), a few key characteristics help it offer a “human-like” conversational experience, and puts it on a trajectory to success in its new position.
“There isn’t a single reason behind successful chatbots, there are many!“
In this article, we’re going to discuss some of the key characteristics that help a “Customer Service Center Chatbot” in its journey to success in offering a “human-like” experience; from its ability to hold Context Based Conversations with customers, successful rates of Intent Recognition, the ability to handle Unrecognized Intent and learn from it, and the its personality and characteristics over different Channels of interaction with customers.
1. Context Understanding
In order to understand conversations, our brain is able to track and remember parts of the conversations, analyze external data like; location, time, preferences of people we are communicating with, mix them all together (we call this Conversational Context), and provide us with the ability to respond with educated responses, and valid information that keeps the conversation going.
Likewise, A successful conversational chatbot should be able to “understand”, “remember”, “analyze” all of the surrounding inputs and preferences, and answer according to the context of the conversation, track the logic and the flow of the conversation and answer accordingly in order to simulate “human-like” interactions.
“Context is your first key to success!”
2. Intent Recognition
The key to understanding what the user wants! The Chatbot needs to be able to extract relevant information from each sentence, word and verb, and understand the intention and the meaning behind it. In order to achieve this, Natural Language Processing “NLP” and Natural Language Understanding “NLU” engines need to be able to process and extract human intentions from different parts of the the conversations.
Many Chatbot service providers rely on command based input, the user is given a number of limited options to select from while interacting with the Chatbot, any text or request from the user that doesn’t fall within the conversation flow the chatbot is programmed on will result in an irrelevant answer from the Chatbot, or repetition of the last sentence. This behavior often leads to user’s frustration, where the user realizes they are talking to a machine, and requests to talk to a human, eventually increasing churn rate on this channel or on the service offered itself.
The introduction of free text chatbots allows the use of long, complex sentences by users, with which arises the need to understand and extract multiple intents from the free text conversations, which requires a higher level and complexity of NLP & NLU engines.
The quality and complexity of the NLP & NLU engines used by the chatbot is an important factor in its success to simulate “human-like” conversations, where the tasks of selecting the NLP & NLU engines that are optimal for the organization’s business use, and the decision between working with generic NLP & NLU services (such as those offered by IBM Watson and chatbot services built on them), or with chatbot services that offer custom in-house developed NLP & NLU engines that would give the organization a better flexibility and customization opportunities with industry specific terminology and terms.
3. Unrecognized intent
No Chatbot is perfect, and the ability of the Chatbot to understand and recognize everything that is said, is yet unachieved by today’s technology! But with better Conversational Artificial Intelligence engines being developed everyday, higher percentages of understanding are achieved.
Until perfect intent recognition is achieved, the ability of a Chatbot to handle the conversation with a number of unrecognized intents, and provide the user with a relevant answers that keeps the conversation going without disturbing the user’s experience (instead of providing an irrelevant answer that gets the user in a “I want to talk to a human” mode, is a very important factor in the selection process for any chatbot services.
When going through the process of selecting a chatbot service, make sure to look for the chatbot service that monitors and collects the unrecognized intentions from conversations with customers, and that provides your organization with a “Supervised learning” tool, with which the client can teach the chatbot what to respond in the case of an unrecognized intent.
4. Channel
Be where your customers are! If you want to engage your customers, you’ll have to meet them where they are and where its convenient for them to be! What is a great customer experience if your customers needs to put extra time and effort to locate and communicate with your organization?
Where you publish your chatbot, and the character and personality you decide to give it to reflect the corporate image, is imperative to the success of the chatbot. The number of ‘Returning Customers” is an indicator of how satisfied are your customers with your selection of chatbot and channel, and how many of them are coming back for that service, whether it is over SMS, Social Media, Email, Webchat, make sure the Chatbot you’re selecting will be able to support your goals of engagement and interaction with customers, support the channels you need to be on, and increase the number of satisfied customers coming back for the same level of service again and again!
With evolving technologies, and the variety of options available today for customers to pick from, being able to provide a unique experience to your customers to keep them coming back for your business isn’t an easy equation.
botique.ai is an enterprise platform that automates chat interactions using proprietary Conversational AI. We compose Chatbots using a wide selection of pre-built, pre-trained AI modules, which makes the integration process quick and easy.
