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7 customer experience implementations of artificial intelligence

I know you're probably already tired of everybody talking about AI and how useful it can be. I must honestly warn you that this article is not an exception.

There is a strong bond between customer experience and the usage of AI, and I cannot be silent about that; businesses should know and, if they have resources, preferably implement it to beat the competition by enhancing their CX.

Here we go, 7 CX implementations of AI:

1. Chatbots

Chatbots are computer programs that simulate human conversation and have become increasingly sophisticated. They are often used in customer service to provide a better customer experience.

Most chatbots help customers with simple tasks, such as booking a hotel room or ordering a pizza. But as chatbots become more sophisticated, they are being used for more complex tasks, such as providing customer support or giving financial advice.

The main advantages of using chatbots for customer service are:

  • 24/7 availability
  • handling multiple conversations at once
  • can be customized to each customer's needs, which helps to create a more personal experience
  • cost-effective

2. Virtual customer assistants

A virtual customer assistant (VCA) is a computer software program that interacts with customers on behalf of a company. VCAs understand natural human language and provide answers to common questions. They can also offer guidance and support during the purchasing process.

Businesses use VCAs to improve customer service and reduce the need for human customer service representatives. VCAs take care of simple questions like store hours, directions, or even more complex questions about products and services. VCAs can also be used to upsell products and services or to provide cross-selling recommendations.

3. Intelligent call routing

Intelligent call routing is a call center technology that automatically directs calls to the next available agent. This can be based on skills-based routing, which leads calls to agents based on their skills or expertise, or on time-based routing, e.g., the time of day or day of the week.

In a skills-based routing system, each agent is assigned a set of skills, and the system looks at the available agent and compares them to the skills required for the call. If an agent with the required skills is available, the call is transferred to that agent.

As for a time-based routing system, calls are distributed at the time of day or day of the week. For example, calls during business hours may be routed to the sales team, while inquiries after these hours may be directed only to the support team. Time-based call distribution can also be used to route calls to different call center locations based on time zone.

This implementation can enhance customer satisfaction by ensuring that the most appropriate agent answers the call.

4. Sentiment analysis

Sentiment analysis is the process of automatically identifying and extracting opinions from the text. SA tracks customer sentiment over time, identifies areas of improvement for customer service, and even helps businesses to monitor their brand reputation.

There are several ways to perform sentiment analysis, but the most common approach is using natural language processing (NLP) algorithms to analyze text data. NLP algorithms can be used to identify the overall sentiment of a text, as well as the emotions that are expressed in it (e.g., joy, anger, sadness, etc.).

Sentiment analysis can also be used to monitor social media for mentions of a company or product and to identify potential issues that need to be addressed.

Sentiment analysis of all customer calls is possible only through speech analytics. Preferably to choose a software that identifies the keywords for a specific sentiment but still shows the whole context of the keyword and sentiment. In such a way, if you hesitate about any part of the analysis, you can double-check it through context and make sure that the KWs are appropriately set and correlate with sentiments.

The benefits of sentiment analysis are clear. It can provide businesses with valuable insights into customer sentiment and help them to improve customer experience. However, applying SA in conjunction with other data sources is essential to get the most accurate and complete picture of customer sentiment.

5. Data mining

Data mining is a process of extracting patterns from large data sets. The combination of data mining and AI can be assigned to develop smart systems that can automatically mine data and extract useful information. These systems can also be applied to develop new applications and services to make life easier for people.

DM aims to discover customer trends and preferences to target marketing campaigns and improve sales and customer experience.

6. Predictive analytics

Predictive analytics is a type of analytics that uses historical data to predict future events. But what for you may ask: to identify customer trends and patterns and forecast future customer behavior.

Consequently, it can boost CX in a number of ways; you can:

  • detect customer segments that are most likely to churn and then target these customers with retention campaigns
  • determine cross-selling and upselling opportunities and personalize customer experiences
  • improve customer satisfaction and loyalty
  • save money by reducing customer churn
  • increasing customer lifetime value

7. Prescriptive analytics

Prescriptive analytics is the branch of predictive analytics that deals with finding optimal solutions to problems. It can be thought of as a combination of predictive analytics and optimization and is often used to make decisions about pricing, resource allocation, and other strategic decisions.

Prescriptive analytics is sometimes also referred to as decision analysis or decision science. It is closely related to operations research and mathematical optimization and employs techniques from various fields, including mathematics, statistics, computer science, and economics.

One of the key benefits of prescriptive analytics is that it can help organizations make better decisions by considering a wide range of factors and constraints. It might consider the impact of a price increase on customer demand, the availability of resources, and the competition. And, of course, as a result, this will lead to an improved CX.

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