Top Customer Service Chatbot Use Cases & Examples


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Customers have become more demanding in recent times. Now, they have access to multiple information sources. In addition, the widespread use of sharing mediums allows them to exchange ideas faster.

Businesses are now evolving to cater to a customer-centric environment to stay relevant. As a result, innovative technological introductions like the use of AI and ML tools are becoming commonplace for these companies to provide adequate customer support.

One of the most frequently used techniques today in the context of customer support is automated chatbots. These strategies leverage AI, ML, and NLP to offer businesses support without compromising the customer experience.

Subsequently, they can also be integrated with the existing customer relationship management software and help improve customer expectations by a notch.

Almost 85% of interactions that customers have with businesses are now being handled without any involvement of human agents.

However, before onboarding a customer-centric AI chatbot platform, businesses must know what they are getting into. Enterprises are already embracing a self-service model to enhance customer support. As a result, they can leverage these tools to respond to customer queries in real-time and offer immediate support.

In addition, the ever-evolving technological advancements are helping to make chatbots more sophisticated. This article will focus on a few AI tools that offer automated customer service using machine learning techniques to analyze multiple query types and consider customer history.

Before we move on to the chatbots, let’s delve into some of their benefits. It will help us realize the potential of the chatbots much better.

Benefits of AI-based Chatbots

The prime benefit of deploying a chatbot is to help the customer care executive free up their engagement in answering basic and repetitive customer queries.

Chatbots can be used to automate the answers and offers a scalable yet practical solution to improve the customer support experience.

AI-based chatbots are also faster than their human counterparts. Moreover, since ML capabilities allow the chatbot to learn customer behaviour continuously, it helps reach a solution quicker than any human.

The tools analyze customer patterns, keywords, and history at lightning speed and offer a relevant response to the customer questions.

Another reason why businesses are adding chatbots into their customer center operations is because of their cost impact. AI-based chatbots are cheaper than human agents since they take no salary.

Therefore the bots can be scaled according to requirements without draining more capital. The round-the-clock availability also ensures that customer queries are resolved during non-working hours.

Therefore, these bots help businesses scale their services using a cost-effective approach.

Now that the case for chatbots is built, let’s look at some of the most famous examples of real-life AI tools used by businesses today. First, however, there are specific criteria used for analyzing the tools. So let’s find out what they are.

Comparison Analyzer: AI Chatbot used to offer enhanced Customer Service
Several features make a chatbot interactive and unique. However, some underlying features are always standard among them. While evaluating the top chatbots, a few of those criteria are chosen.

These are:
User Interface (UI) and User Experience (UX): Chatbots must have an interactive and easy-to-use interface for both agents and customers. Further, it should deliver a seamless experience in terms of chat history, autoresponders for commonly asked questions, and an interactive chat interface.

Usability and back-end support: Chatbots work efficiently when the back-end services smoothly. Therefore, analyzing the usability and learning models becomes essential while evaluating chatbots.

It should further have robust tech and user support, the capability to integrate with the existing CRM and allow fast responses.

Scalability and Integrations: A modern-day customer response system needs to evaluate customer data for scaling up operations.

Therefore considering the availability of integration, both pre-built and customizable, is crucial for determining the ranking of the chatbots.

Pricing: Another criterion considered while evaluating chatbot impact is understanding the practical value addition and comparing it by analyzing the features and capabilities.

After all, it is crucial to understand the validity of the cost of the tool and the effectiveness it will bring.

Other than the criteria mentioned above, some other vital features determine a chatbot’s efficiency. First, the NLP and ML usage in the background and learning models they are trained in play a massive role in developing an interactive chatbot.

Further, it should be connected to the entire data silo to have a combined view of the customers. Finally, it allows chatbots to leverage customer behaviour and other relevant information to offer customized solutions to them.

The data collection itself should be across platforms and customer touchpoints. It improves functionality and provides a better service experience to the customers by reducing the overall wait time while providing a solution to their issues.

The AI-based lead collection system allows its customers to have all the insights into their customers and efficiently use the resources to improve the experience.

The interactive dashboards help understand and analyze the process better and optimize it further.

Wrapping Up

Chatbots that organize and help minimize human interventions can improve reaction and resolution times. As a result, they can further enhance customer expectations by allowing customers to resolve their issues faster.

Businesses using chatbots can also improve the workload management of the agents more effectively.


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