We already know that AI has greatly influenced the way we conduct business, as well as how we approach customer experience. This article will not focus on the high-level impact, but rather on practical examples, best practices, and use cases for using generative AI in customer experience. Let's get started!
What is AI customer experience?
AI customer experience (CX) is an innovative approach that uses AI technology, including advanced machine learning algorithms, chatbots, and digital agents, to provide responsive, tailored, and predictive customer interactions at a large scale. Essentially, AI CX uses cutting-edge technology to enhance customer experiences, increase customer satisfaction through personalized interactions, increase CX team productivity, and streamline business operations to save costs. AI customer service tools can streamline operations and improve customer experiences by automating support processes and integrating with enterprise applications.
10 Real ways AI can improve your customer experience
- Streamline customer service operations to reduce workload and improve efficiency
- Onboard CX and CS Teams faster
- Enable great CX at scale
- Anticipate customer needs and enhance customer engagement
- Provide AI-Powered data analysis
- Analyze customer data to predict and prevent churn
- Reduce operational cost
- Create more consistent brand experience
- Boost knowledge management
- Provide faster customer support
How to use AI in your CX
To make your workflow more efficient, it’s best to use a single software for all your CX tools, such as ClientZen. This allows customer support teams to track interactions and progress, ensuring no relevant information or tasks are missed.
AI can be utilized in various ways, and while it’s impossible to cover all its capabilities in a single blog post, let’s focus on the most frequently used use cases that we’ve discovered from our users. AI tools can also provide fast, personalized service to customers, enhancing their overall experience.
Gen AI can be used for the followings:
- Automatizing customer feedback analytics process as a whole
- Clean data and unnecessary noise
- Structure and tag customer interaction for better data analysis
- Predict customer churn
- Dive deeper in finding the root cause of your customers pain points
- Sentiment analysis
Automatizing customer feedback analytics process as a whole
In 2024, Gen AI is the basis of automation. It speeds up your workflow like no other technology has before. Let's see how Mantra AI has transformed the entire customer experience (CX) process. Mantra AI is a custom artificial intelligence developed by ClientZen. It has been implemented to enhance the manual process of customer feedback analysis by analyzing customer behavior to provide valuable insights. But how does it work?
Similar to many other artificial intelligence systems on the market, Mantra AI learns from your input, particularly customer data. It then uses its capabilities to map out specific requirements, in this case, the needs of the user. In customer feedback analysis, this means that it automatically applies smart tags based on customer interactions.
Once you upload your customer feedback, the AI analyzes the data, comprehends the entire dataset, identifies patterns, and delivers the best possible output. For smart tagging, this means that Mantra AI extracts and defines tags for each piece of customer data. It organizes the entire dataset to help you make sense of a large amount of data, allowing for quicker, smoother, and faster analysis without human intervention. After applying the tags, any new input is treated similarly, with tags applied to group the new data and blend it with similar topics. If a new topic is detected, it is labeled with a new specific tag. Pretty amazing, right?
In addition to semantic tagging, it also performs sentiment analysis, detects negative sentiment drivers, which used to be a high-volume task, answers questions through its co-pilot assistant, estimates customer churn based on customer feedback and sentiment, conducts NPS and CSAT analyses, and handles essentially any customer feedback analytics task.
The AI can also identify recurring issues, feature requests, and questions, labeling each appropriately so you don’t have to worry about a thing. Moreover, AI-powered chatbots can streamline and personalize the customer service experience by providing efficient feedback analysis, automating routine tasks, and improving customer satisfaction.
Clean data and unnecessary noise
Why do we need to clean data? Well, in order to obtain accurate results in data analysis, it's crucial to clean the data. Data cleaning and noise reduction are important in data analysis, especially in customer feedback analysis and the overall customer experience. We should only focus on relevant data because including irrelevant customer insights could lead to misleading outcomes and wrong conclusions.
Wondering which data to keep and which customer insights to include in your data analysis process? Here's a good answer: you don't need to know everything because artificial intelligence does this for you.
Mantra AI detects the noise from your customer interactions. What kind of noises are we talking about? A good example is a simple customer support interaction. The conversation starts with a quick greeting, followed by a typical question like "how can I help you?". This initial information is considered unwanted data. We don't want to include intro and outro instant replies when we analyze customer feedback. Due to its recurring nature, this data will be identified as high volume, but it adds no value to the analysis and may even mislead you.
Structure and tag customer interaction for better data analysis
We’ve previously discussed the significance of tagging customer interactions and the reasoning behind it. Structuring data can help customer service agents provide better support by making relevant information easily accessible. In addition to implementing smart tags, what other techniques do you think of for structuring data?
In order to address this question, let’s first establish the type of analysis we regularly conduct. To enhance our customer experience, we must comprehend specific issues and overcome them. We achieve this through various analyses such as NPS, CSAT, sentiment analysis, topic analysis, RCA (root cause analysis), VOC analysis, gap analysis, and more. To carry out these analyses, we require a structured approach. We define categories, topics, subtopics, and custom segments to ultimately uncover the essence of our customers’ needs. This categorization approach allows us to follow the entire customer journey, from high-level analysis down to identifying the root cause of issues such as customer churn.
This is where AI comes into play, as it assists in categorizing and labeling by simply uploading your customer data. For instance, Mantra AI not only applies smart tags but also identifies topics, subtopics, and categories, streamlining the labeling process with minimal human involvement. Additionally, AI can facilitate self-service solutions for customers by providing AI-powered knowledge bases or FAQs, balancing self-service with human interaction to deliver a convenient customer experience.
Predict customer churn
AI can help you make correlations between customer sentiment and customer churn and anticipate churn before it happens. This can give you a powerful advantage if done properly. While many AI systems or LLMs struggle to predict churn accurately, Mantra AI is designed to do this job without any human adjustments.
The AI analyzes all customer interactions, learns from them, and, based on specific rules, provides you with the customer churn rate associated with customer sentiment. Once you have anticipated customer churn, your next step is to understand the drivers of negative sentiment in order to prevent it from happening. This is where we move to the next step.
Dive deeper in finding the root cause of your customers pain points
When you conduct a root cause analysis, you are able to identify the underlying factors or triggers that lead to negative sentiments. This process involves a thorough examination of the various elements that contribute to these negative emotions or attitudes, allowing for a comprehensive understanding of the issues at hand and providing valuable insights for potential solutions.
As a user of ClientZen, you may already be aware that negative sentiment drivers are readily accessible on your main dashboard. These drivers are automatically generated as soon as you connect your data sources, eliminating the need for manual input or excessive clicking within the platform. This means that upon logging into the dashboard, you can instantly access this valuable information without any additional effort.
Sentiment Analysis
Sentiment analysis software with AI (artificial intelligence) capabilities can do more than just identify positive, negative, or neutral sentiments with human-level accuracy. AI customer support tools can identify sentiments, understand the intricacies of human language, detect subtle tones, and even interpret sarcasm, resulting in a more comprehensive and accurate analysis of customer feelings.
AI-powered software utilizes machine learning algorithms and Natural Language Processing (NLP) techniques to analyze text data. It continuously learns from each piece of data it processes, thereby improving its understanding and interpretation of human sentiment. As it analyses more data, it becomes better at identifying and understanding sentiments.
One of the major advantages of AI-powered sentiment analysis software is its capability to process large volumes of data rapidly. This makes it an invaluable tool for businesses receiving vast amounts of customer data through various channels. With AI, businesses can efficiently analyze this input, identify trends, and promptly respond to customer concerns.
Furthermore, AI-powered sentiment analysis software offers more in-depth insights than most sentiment analysis tools. It can identify the intensity of the sentiment expressed in the text, such as determining whether a customer is slightly annoyed or extremely frustrated. AI can improve customer service through efficient sentiment analysis, helping businesses to increase productivity, efficiency, and customer satisfaction.
Want to learn more about AI for Customer Experience?
If you are interested in learning more about how generative AI can be used in customer experience (CX) or how to integrate artificial intelligence into your customer support and overall customer experience processes, you can find more information about ClientZen and Mantra AI in our blog section. Alternatively, feel free to reach out to us directly so that we can assist you in a more personalized way on your journey of adopting AI in your CX flow.
Customer feedback made easy
Customer feedback tagged automatically
Real-time customer sentiment scores
Pain-points evolution over time
