The Power of Chatbots in Customer Self-Service and Resolving Some of Their Challenges

Jan 28, 2022 | Customer Service, Customer Support

Chatbots are, in essence, automation representing a human conversation. Chatbots try to understand human communication and provide relevant answers to questions accurately. From the customer’s point of view, it should appear like they are speaking to an actual human being, or at least, so it seems. Here are the top ways chatbots can improve the customer experience:

Reduce time to service and always-on customer support

One of the most significant advantages is that chatbots are available 24/7 to help customers in a cost-efficient and yet potent way to provide essential support. Chatbots can reduce customers’ wait times and quickly get them what they want, in most cases. They provide a potential path to resolving your customers’ problems or queries, no matter what time of day. Some chatbots can sometimes even distinguish human emotions such as anger, confusion, fear, and joy. So that when it detects anger/frustration, it can transfer the interaction to an agent to take over.

They help optimize costs and improve customer satisfaction

Implementing a chatbot is a great way to automate customer service and improve the service provided by agents, leading to improved efficiency and cost optimization. Imagine your agents spending more time answering just the queries requiring more human intervention or unique requests. Additionally, customers like to have options on how they communicate with organizations, and one of those options is to self-serve their needs. Chatbots are a perfect complement to self-service and, as a result, should improve customer satisfaction scores when implemented to address many of the issues.

Gather business insights

By collecting data from the chatbot conversations, businesses can get valuable insights into user experience (UX) and be notified early regarding any issues and roadblocks that customers face. Organizations can become proactive, take corrective actions, and reach out to other customers before issues or problems surface to significant disturbances.

Limitations from rules-based and today’s versions of AI conversational chatbots

Chatbots often don’t live up to their potential due to the restrictive technology that makes them function. Most businesses frequently use rules-based/scripted chatbots to assist their customer support departments and operations. This chatbot is limited in facilitating the rich, conversational interactions necessary to keep users engaged and satisfied over the long term. It is problematic because it limits the interaction variety and the chatbot’s ability to assist the customer. For instance, it can’t guide users back to the subject being discussed, by ask qualifying questions, or deal with issues once they reach a certain level of complexity; this can lead to customer frustration. These rule-based chatbots match the user input to a rule pattern and select a predefined answer from a set of responses using Pattern Matching algorithms. The context of the query can also contribute to the rule selection and the response format. Rule-based systems, typically, do not create new answers because they are constrained to the knowledge used to form the conversational patterns. They have objects that contain topics with relevant categories around them. All categories are in the form of a tree, with its nodes representing the categories and its leaves representing responses. The more extensive the knowledge base is with the rules, the more capable a chatbot can answer the user’s questions. Depending on the type and number of queries, this chatbot can take thousands of rules to work correctly. 

As mentioned, the objective of scripted chatbots is to match user inputs to chatbot outputs using pattern matching discussed earlier. They possess an embedded tagger and parser that analyzes the user input to correct grammatical errors and justify the syntax and semantics. To create a better match, they also use concepts that compare groups of similar words that impact the meaning and parts of the speech. Some are also case-sensitive, able to detect the emotion in the user’s response when it is used for this purpose. They can sometimes include variables in their long-term memory to store specific user information that can be used directly or in conjunction with the logical conditionals to produce chatbot responses.

The downside of the pattern matching approach is that the answers are automated, repeated, and do not have the originality and spontaneity of human response. Additionally, there is the limitation of how many rules and responses can be created to make it work, before it becomes unmanageable and impractical, relegating it to a limited number of inquiries. Also, most rule-based chatbots utilize a single-turn communication, where the answer is selected, taking into account only the last response. It is unlike some traditional AI conversational chatbots, which use a multi-turn answer selection, where every response is used as feedback to choose an answer that is normal and appropriate to the entire context.

However, even traditional AI conversational chatbots with multi-turn answer selection have challenges. They require vast amounts of training data and highly skilled professionals to operate and maintain. The collecting and categorizing training data required to build a chatbot application is costly and time-consuming. The data needs to be very representative of the type of questions they may see and the acceptable responses, which constantly change over time and are pretty challenging to structure in high-tech and complex industries. As a result, they require ongoing review, constant maintenance, and continued optimization of their knowledge base. To ensure that the conversations hold true to the customers’ needs, they require tracking and analyzing every conversation/interaction, which is very difficult and requires immense effort. They also lack the sophistication to tie into all of the documents an organization possesses to carve out answers to any query, further debilitating their practicality and impact.

Quark.ai’s approach

Quark.ai has a unique chatbot called the Quark.ai Intelligent Bot. Our Intelligent Bot uses advanced NLP to break the inquiry into the fundamental essence of what is needed. Leveraging our advanced AI, it has capabilities to intelligibly interpret a customer’s inquiry, regardless of length, and pay attention to the keywords or phrases that embody the intent. We can lead the conversation by:

  • asking for more details
  • narrowing the focus to one area or product when it starts broadly
  • deciphering between different directions, to further clarify what the user needs
  • Keeping track of the conversation sequence to be able to construct or not lose sight of the intent and critical components of it.

Once there is a clear understanding of what is required, we can correlate the inquiry’s intent to an exact answer. We can do that because we have deciphered the documents on the back end, using our unique IP of Deep Learning, NLP, and Computer Vision. Providing the following capabilities:

AN AI/ML ENGINE developed using Deep Learning, NLP and CV, interprets a customer query to pull up answers from reference documents instantly. This engine includes Deep Learning Models already trained with millions of data sets for High Tech and Industrial markets. As a result, we can onboard a new customer in about a month. No additional training is required to onboard a new customer.

DATA INGESTION TECHNOLOGY that ingests all reference documents as-is from any data source (Web URL or application), without the need for any customization or tagging. The ingestion process also understands and preserves the formatting of the reference documents, thereby enabling answers (results for a query) to be displayed as they look in the source document.

In summary, the Quark.ai Intelligent Bot requires no scripting to handle inquiries and conversations, which with traditional chatbots limits the handling of different topics or the overall volume of different inquiry types. Our solution doesn’t require any additional training and none of the ongoing maintenance that you see with traditional chatbots. The Quark.ai solution is free-flowing in its conversation to handle any type of inquiry and guiding the customer to the answer. No other solution can interact, in an unconstructed way, as broadly as we can, on the different inquiry topics and get to a correct answer.