AI for Customer Service: How to Improve the Customer Experience with Artificial Intelligence

Introduction

AI for customer service is already a reality. The adoption of artificial intelligence in this industry has the potential to save businesses time, money, and resources, as well as improve the experience of their customers. In this article, we’ll discuss how AI can be used to improve customer service through chatbots and virtual assistants, natural language processing (NLP), predictive analytics, sentiment analysis, case studies, and best practices for implementing AI in customer service.

Chatbots and Virtual Assistants for Customer Service

Chatbots and virtual assistants are two of the most popular uses of artificial intelligence in customer service. A chatbot is a computer program that can simulate human conversation, often via text or voice. Virtual assistants are similar to chatbots, but they tend to be more sophisticated and complex in their interactions with users. They’re also typically integrated with other services like Alexa, Siri, and Cortana—the digital assistants built into many smartphones.

The most common way businesses implement these technologies is by creating automated customer support systems that let customers interact with them through text or voice messages rather than phone calls or emails (which can take up valuable time).
For instance:

• An airline might provide its passengers with an option on its website where they can book flights directly from their browser instead of calling in;
• Or an insurance company could offer self-service options for checking policy details;
• Or a bank could allow customers who’ve misplaced their debit cards access only through its mobile app instead of allowing them access over the phone or at an ATM machine

Natural Language Processing (NLP) for Customer Service

Natural Language Processing (NLP) is the science of understanding human language. It’s used in many different ways, including customer service interactions and chatbots. NLP can help you improve the customer experience by enabling your chatbot to understand what customers are saying and respond appropriately.

Here are some tips for implementing NLP in your customer service:

• Use intent-based keywords so that your bot can better understand what a user wants from them. For example, if someone asks “how much do you charge?” then they’re probably looking for pricing information, so it would be helpful if they were directed there instead of being given an answer about how long it takes to ship an order out of stock items.* Don’t try too hard when writing scripts; let them flow naturally with conversational language rather than forcing yourself into awkward phrasing.* Use machine learning techniques such as natural language generation (NLG) or statistical machine translation techniques like transfer learning

Predictive Analytics for Customer Service

Predictive analytics is the use of historical data to predict future events. For example, if you have a database containing information about how customers behave and what they buy, you can use predictive analytics to generate predictions about what they’ll do next.

In customer service, predictive analytics can be used for everything from identifying potential problems with your product or service (e.g., if a customer has been calling frequently) to predicting which customers are likely to churn (i.e., stop using your service).

Sentiment Analysis for Customer Service

Sentiment analysis is the process of determining whether a piece of text is positive, negative or neutral. It’s used in customer service because it helps companies understand their customers’ feelings about their products and services.

For example, if you run a business that sells clothing online, sentiment analysis could tell you how satisfied customers are with their purchases and whether they’d recommend your site to friends. Sentiment analysis can also be used for more basic tasks like identifying the most common reasons why people leave reviews on Amazon or Yelp (so you can address them).

You can implement this technology by using an online tool like MonkeyLearn or IBM QUIZNATION (which was developed by IBM Research) to analyze user-generated content from platforms like Facebook Messenger or Twitter DMs in real time as soon as it comes through your system; this allows you to respond appropriately based on the moods expressed by each individual message before moving onto the next one!

Case Studies

• A major U.S.-based airline implemented an AI-enabled chatbot to help customers with their trip planning queries. The bot was able to answer questions about flight status, check-in procedures, baggage restrictions and more.
• A global insurance provider created a chatbot assistant for its customers who needed help with claims filing or policy renewals. The chatbot was able to streamline the customer service experience by automating several repetitive processes like claim submissions, policy renewals and online payments via credit card or debit card payment gateways (including PayPal).

Best Practices for Implementing AI in Customer Service

There are a few best practices to keep in mind when implementing AI. First, make sure you have a clear understanding of the problem you are trying to solve. You may think that an automated chatbot would be able to answer all your customer questions, but it’s important to consider how long it takes for people who use this type of technology on a regular basis (e.g., millennials) and then compare that with how long non-millennials take before they give up on using it at all.

Second, understand the strengths and limitations of AI and know where those strengths lie so that you can tailor them towards specific types of interactions with customers over others! For example: if someone is having trouble finding something in your store or app and asks for help finding it via live chat support then it might make sense for this person’s query instead be routed directly back into their phone via text message rather than through voice calls since these two mediums allow for greater speed than either would provide alone.”

Future of AI for Customer Service

AI for customer service is still in its infancy. It’s not yet capable of fully understanding the needs and desires of customers, but as AI advances and becomes more sophisticated, it will become better at anticipating them. The future of customer service is one that involves personalized conversations with customers based on their individual preferences and interests.

AI has already made great strides toward mimicking human behavior; it can now identify emotions from facial expressions or tone of voice (and even generate fake videos using those same techniques). This means that when you speak with an AI-powered chatbot or virtual assistant like Alexa or Siri, they’ll be able to understand how you’re feeling and respond accordingly! For example:

• If you say “I’m bored,” your virtual assistant might suggest some activities nearby that would keep you entertained until dinner time rolls around later today like going for a hike near the beach or visiting an art gallery downtown, etc.

Best AI Tools for Customer Service

Here are some of the most popular AI tools that can be used to improve customer service:

1. Chatbots – Chatbots are AI-powered tools that can help businesses automate customer service interactions. Some popular chatbot platforms include:

• Dialogflow: Build AI-powered chatbots for websites, mobile apps, and messaging platforms.
• IBM Watson Assistant: Create virtual assistants that can interact with customers in natural language.
• LivePerson: AI-powered messaging platform that offers chatbots, live chat, and messaging.
• ManyChat: Messenger marketing platform that can automate customer interactions with chatbots.

2. Natural Language Processing (NLP) – NLP tools can help businesses understand and respond to customer inquiries more accurately and efficiently. Some popular NLP platforms include:

• Google Cloud Natural Language API: Analyze text to understand its sentiment, entities, and syntax.
• Amazon Comprehend: Natural language processing service for extracting insights from text.
• Microsoft Azure Cognitive Services: Pre-built APIs for building intelligent applications using AI.

3. Sentiment analysis – Sentiment analysis tools can help businesses understand the emotions behind customer feedback and respond appropriately. Some popular sentiment analysis platforms include:

• Aylien Text Analysis API: This is a cloud-based API that provides sentiment analysis
• Hugging Face Transformers: This is an open-source machine learning library.
• MonkeyLearn: Machine learning platform that can analyze text and extract data.

4. Voice assistants – Voice assistants like Alexa and Google Assistant can be integrated with customer service platforms to provide a more natural and hands-free experience for customers. Some popular voice assistant platforms include:

• Amazon Alexa Skills Kit: Build skills and voice interactions for Amazon Alexa devices.
• Google Actions: Build actions and voice interactions for Google Assistant.
• Microsoft Cortana Skills Kit: Build skills and voice interactions for Microsoft Cortana.

Conclusion

In today’s world, customer service is a critical component of any company’s success. By using AI to improve your customer service, you can increase efficiency and reduce costs while also providing a better experience for your customers. The technology behind these systems is still new, but it has already proven its value in countless ways by improving communication between businesses and their customers.

FAQs