Product School

Leverage AI for Customer Experience

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Author: Product School

February 9, 2024 - 6 min read

Updated: February 9, 2024 - 6 min read

In today's digital age, customer experience is undergoing a profound transformation thanks to the integration of Artificial Intelligence (AI) technologies. From streamlining repetitive tasks to providing personalized experiences, AI is reshaping how businesses engage with their customers.

In this article, we'll explore how to use AI to take your customer experience to the next level. Throw out the rule-based technology of the past and embrace customer experience solutions that adapt to each customer.

Improving Customer Service with AI

Virtual assistants and AI-powered chatbots are changing the way businesses interact with their customers. "Data-driven" can sound a bit cold, but that's only because we don't associate data with feelings. In reality, AI puts feelings first. With the help of sentiment analysis, AI chatbots can understand the nuances of human language. These emotionally intelligent systems understand and respond to customer queries in real time, delivering personalized support at scale.

How does generative AI enhance the chatbot experience?

In the (quite recent) past, all chatbots were rule-based. A rule-based chatbot follows a fixed sequence of steps to respond to user queries based on keywords or patterns. These chatbots can handle simple interactions and provide predefined responses to common queries. However, they cannot learn or adapt to new situations. 

These simplistic customer service chatbots employ rule-based systems to provide scripted responses to frequently asked questions. However, if a customer has a more nuanced query, a rule-based chatbot will struggle to respond effectively. This can cause the customer to feel frustrated and give up on whatever it was they were about to do. Many companies miss out on opportunities this way.

AI-powered chatbots utilize artificial intelligence and machine learning algorithms to understand and respond to user queries. They can analyze natural language, context, and user intent to provide more accurate and personalized responses.

Siri and Alexa are examples of AI-powered chatbots. These chatbots can engage in natural language conversations, understand complex queries, and provide relevant responses based on the context. They can do this because of advances in sentiment analysis and predictive analytics.

Companies like Apple and Amazon were pioneers in this space. They had access to huge amounts of user data, and the resources to invest in development. The exciting thing is that these technologies are now widely available.

Leveling up chatbots with predictive analytics and sentiment analysis

Predictive analytics analyzes historical user interactions and data to identify patterns and trends in user behavior. By understanding past interactions, chatbots can predict user intent more accurately and anticipate the user's next question or action. Predictive analytics can improve the performance of NLP algorithms used by chatbots to understand and process natural language. 

By analyzing large datasets of text data, predictive analytics can enhance language understanding, enabling chatbots to interpret complex queries and provide more accurate responses. This process begins with collecting relevant data from various sources. These might be:

  • Databases

  • CRM systems

  • Transaction records

  • Customer interactions

  • External sources like social media or market research

Based on all this data, AI systems become experts in the topics that your customers care about the most.

Of course, it’s one thing to understand something, and another to be able to communicate it effectively. AI chatbots analyze user sentiment and emotions expressed in text data. By understanding the tone and sentiment of user messages, chatbots can adapt their responses accordingly, showing empathy, providing reassurance, or offering support as needed.

Blog image: EQ vs IQ

Aligning AI Efforts with Customer Interests and Intent

To properly align AI efforts with customer needs, businesses must adopt a customer-centric approach. It's essential to leverage AI technologies not just for automation, but also to deliver personalized experiences that resonate with customers. By understanding the customer journey and leveraging AI insights, businesses can tailor their marketing strategies for more engaging campaigns.

Generative AI technologies further enhance customer engagement by creating dynamic and interactive experiences. By leveraging generative AI, businesses can deliver personalized content and recommendations that capture the attention of their audience, fostering deeper connections and loyalty.

Better content recommendations with LLMs

AI-powered content recommenders are an example of throwing out the “rule book” for seismically better results. In contrast to rule-based recommenders, AI recommenders can deliver personalized recommendations tailored to each user's interests and tastes. In contrast, rule-based recommenders rely on predefined rules or criteria, offering generic recommendations that may not resonate with individual users.

Large Language Models (LLMs) use vectors, which are numerical representations of words or phrases, to provide contextual content recommendations. Languages are incredibly complicated systems. Vectors use math to create relationships between words and allow LLMs to understand content in a way that humans do.

By analyzing these vectors, LLMs can identify similarities between user interests and available articles. AI recommenders come up with the most relevant content for users based on their preferences and browsing history. This approach ensures that users receive personalized recommendations that align with their interests and enhance their overall browsing experience.

These systems recommend content that users are likely to find useful. This increases levels of engagement and customer satisfaction.

Enhancing Customer Satisfaction with AI Tools

Implementing AI tools to analyze customer feedback is a great way to turn current complaints into future solutions. The first step is to clearly define the objectives and requirements for analyzing customer feedback. Determine the types of feedback you want to analyze, such as customer reviews, survey responses, or social media comments. Additionally, outline the key metrics and insights you want to extract from the analysis, such as customer satisfaction scores.

Blog image: Digital sentiment

AI solutions can take a vast database of customer surveys and group them into useful categories. They analyze not only the words that customers express in surveys but also the sentiment behind those words. Once your team learns that the mobile app is responsible for 80% of customer complaints, setting priorities is a breeze. AI tools can make a long manual sorting task quick and full of accurate insights. 

In Conclusion: Throw Out the Old Rules

Using AI to enhance customer experiences is like having a super-smart friend who always knows what you need. Unlike old-school methods, AI-powered chatbots truly understand you, adapting to your feelings and improving with every chat.

The old chatbots that just followed the rules couldn't handle anything out of the ordinary. With AI, they understand complex queries and respond in a way that's just right for you.

AI doesn't just understand words; it picks up on your feelings too. It knows when you're happy, sad, or frustrated, and responds accordingly. It's like chatting with a real person—one who is available 24/7.

When it comes to recommendations, AI is a game-changer. Instead of just following rules, AI looks at your past preferences to find things you'll love. It's like having a friend who knows your taste in movies, music, and books, and always has the perfect suggestion.

Finally, AI helps us improve by analyzing feedback. It's like having a team of researchers dig through all your feedback and come up with smart ideas for making things even better.

Further reading:

Updated: February 9, 2024

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