There are thousands of chatting and messaging tools to measure the effectiveness of communication in our day-to-day business, but only a handful of them are actually proficient in using AI ML for their workflow. If you are looking for a classy chatbot management project, here’s your chance to acquire the skills through the Artificial Intelligence course in Bangalore.
Running the Big Data of Chats
Being data-driven is the biggest source of productivity for any business owner now. It not only ensures that the business stays successful and profitable in the long course of its existence but also impacts the way they adopt new emerging technologies to improve various key operations. Customer chat centers and service teams are one of these essential centers that face users directly and need to provide answers to the customers in real-time. That’s why we have witnessed the highest rate of adoption of Artificial Intelligence and Automation in customer service operations.
In this article, I have showcased a few business-centric examples of how Chat Analytics works and how it became the quintessential aspect of doing business.
What is Chat Analytics
Chat analytics is a series of KPI measurements and statistics that are reported in the form of numbers, pie charts, bar graphs, and other data visualization tools to show how the chat history progressed and evaluate the performance of a chat executive.
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The practice of chat analytics started with Tele calling and chatbot deployment teams but today marks the application across all departments, including Finance, HR and Marketing, and Sales, where the analysts dig deep into the analytics to measure how different members of the teamwork together to solve a complex problem using the digital infrastructure.
According to the latest report on chatbot applications, 54% of the customer-facing teams are either using chat analytics tools to improve the performance of their customer support and service operations, while only 4% of these teams are using the same suite for their own evaluation of Marketing, Sales and Customer Service teams.
Given the monopoly of AI ML tools in the current context of business, 46% of the chat analytics tools are now used to measure the performance of Virtual Assistants.
Where Chat Bot Analytics for Messengers are Used
There are many ways chat analytics are used to measure the proficiency of the tools.
Here are some of the common examples where AI training objectives come in handy in measurements.
How much time does it take on an average for a chat virtual assistant to reply to an inquiry, versus the time taken by a human executive to complete the same tasks
What are the various processes could we automate with chatbot messengers
How much time, on average, does a chat analytics team spend in providing an answer to the business decision-makers
Does the chat analytics solve any major problem or provide any detailed information or explanation in finding issues with human-driven processes
Can the chat analytics solve problems during inventory management, or for paying bills at checkout
Can analytics inspire the business analytics team to improve the overall efficiency of the processes that are currently in place
AI’s revamp to simplify Chat Messaging for Businesses
Today’s chatbots are finding a new groove. The analysts from popular artificial intelligence courses in Bangalore are hired to make chatbots more human. According to a survey by a top chat analytics peer research group, the market for human-based chat teams is shrinking at a 55% rate year on year. By 2025, 99% of the chat messenger teams around the world would be completely robotized. There would be 6 billion chatbots in businesses, nearly 1 chatbot for every human being! That means every human on this planet would get a personalized chatbot for him/her.
The power of AI and NLP are at the center of this development even as 45% of the global chat analytics results show that customers still prefer to talk to a human representative and let them handle their cases. The use of Intelligent Automation / Intelligent assistants at some point of the chat messaging process can streamline the process, but machines mimicking human voice and sentiments would definitely rule the roost in the coming years! That’s why we need more analysts and engineers who can build predictive and voice models for chat analytics and messaging automation.
