AI-powered Customer Experience Services and Solutions

Role of AI in Customer Experience

With the increase in computation power and decreasing prices of storage devices leads to a digital transformation to apply AI in solving business problems. Some specialists are calling it the fourth industrial revolution. AI is all about making computers think like humans with customer interaction solutions. Using domain expertise of humans, we are feeding the features to the system to create AI to solve problems of domains like healthcare, stocks, Computer Vision (CV), Natural Language Processing (NLP), Retail, Entertainment, etc. Due to these applications, have better solutions for problems like cancer prediction which is a way better than trained medical experts, stock market prediction by analyzing the various traits such as sentiments of people, detecting unusual activities in video, etc. The entire credit goes to the active community of researchers that made solving such problems with greater accuracy.

AI can also be used for customer interaction, like humans are interactive and intuitive researchers are making systems that are as interactive and intelligent as humans but are resistant to tiring and boredom. The driving force behind it is electricity and a network connection. According to Gartner by 2020, 85% of customer interaction will be managed without a human.

Causes of the popularity of AI in customer experience and interaction

No human intervention in services
Time-efficient
Cost-efficient
Better data crunching
Hungry for data
Improves routing of tickets

No human intervention in services — A piece of software deals with the customer for handling its queries. Whatever you ask it can give you better answers or suggestions without saying pardon

Time-efficient — Can handle queries in no time. Without the need to think like a human before answering.

Cost-efficient — A single AI bot can handle communications of many channels at once. Hence, it leads to saving the cost of hiring.

Better data crunching — When tackling with the wide variety of data collected from different sources such as feedback, surveys, customer requirements, etc. humans might get into a state of confusion which to And what to deal first. Using the fusion of AI and machine learning it quantifies the insights collected from the data and ultimately leads to better strategic decisions.

Hungry for data — Results in better performance when we feed to more (variety) data. Let’s take an example of an AI chatbot deployed at some support site. If we keep on asking the short queries, it gives a good result but not as efficient as per the expectations. Great questions will improve its performance as the bot tries to find the intent from the query.

Improves routing of tickets — For a customer-centric organization, it is necessary to improve the ticket’s path. Consider the example of Uber there can be issues like refund status, driver not arrived, etc. So the system must route the tickets by understanding the intent of the problem to
the respective customer care executive so that customer doesn’t need to wait for more.

Some facts and figures related to Artificial Intelligence

Robots and AI will replace 7% of jobs in the US by 2025. But an equivalent of 9% of the posts will be created.
According to Forrester, 8.9 million new jobs to be created by 2025 which leads to an increase in demand for robot monitoring professionals, data scientists, automation specialists.
By 2020 52% of consumers will expect that the company should provide service via virtual reality.
73% of Companies will shift their AI product to the cloud.
58% of consumers want the product can self-diagnose issues and automatically troubleshoot itself.
According to Oracle, nearly 8 out of 10 businesses have adopted AI or are planning to take by 2020.

The secret sauce for a successful business

The organization must focus on customer involvement and engagement. With the introduction of AI in customer interaction, people started enjoying the services, increase in time of engaging, gains user trust and improves the brand value.

Building blocks for AI in Customer Experience and Interaction

Data Unification
Real-time insights delivery
Business Context

Data Unification — In an organization, data comes from many sources i.e. records, real-time data and curated data from the team. The problem is how to merge and match these sets of data to get the best out of it. Companies like General Electric (GE) have spent several years in data unification. The most important and tedious phase for data engineering teams to prepare ready for modeling.

Real-time insights delivery — It’s all about analyzing the interest of the customer. Organizations like Amazon, Flipkart has made their recommendation algorithms that recommend product promptly and moreover can increase the buying capacity of the customer (upsell). According to Harvard Business Review, 60% of business leaders, say customer analytics is critical by 2020, it will be increased by 79%.

Business Context — When applying AI in the organization, we first need to understand the perspective of the business. What is our target customer and how we handle the ambiguity in the conversation between customer and support?

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