AI Help is the use of artificial intelligence to help improve business processes and systems. It can help automate routine tasks and free up human workers for more creative and personal work.

Aside from increasing operational efficiency, AI can also boost customer support. It can automatically assess customer messages, categorize them, and send them to the right team for follow-up.

1. Automated Text Analysis

There are a multitude of applications for text analysis, which helps teams extract meaningful information from unstructured data. This can be from customer reviews, online forums, messages, phone transcripts, surveys and other forms of feedback.

AI-powered text analysis reduces manual work to help improve productivity. It also increases the accuracy of the results, and provides a better understanding of your customers’ preferences, trends, and needs.

Instead of searching through thousands of conversation logs to identify which keywords and phrases are used most effectively by top-performing representatives, text analysis automatically finds them for you. It then extracts and micro-categorises them in the right sequence or pattern to produce customer-satisfying results.

This can be used to identify customer sentiment, automate the classification of critical tickets, or determine which queries need to be sent to a specific specialist team. It can also be used to create more complex cross-analysis and patterns, which further empowers teams to find automation opportunities, revenue-generation ideas and customer-satisfying strategies.

2. Sentiment Analysis

Sentiment analysis is a form of text analysis that uses an AI engine to interpret text based on its sentiment. This analysis helps you understand how people are feeling about your brand or product, so you can make more informed marketing decisions.

It can also help you respond to negative PR crises before they escalate, limiting damage to your reputation and financial cost. For example, the viral tweet that wiped $14 billion off of Tesla’s valuation in a matter of hours could have been prevented with the help of sentiment analysis.

In addition to monitoring comments on social media and third-party websites, sentiment analysis can also be used to gather competitive intelligence about competitor brands. It can also tell you about what your customers want and need from your products or services.

It is important to note that while a rules-based approach works well, it can become naive in some contexts and is susceptible to error. This is why a hybrid algorithm that incorporates machine learning is often preferred.

3. Chatbots

Currently, chatbots are being used in the customer service industry to automate customer queries and improve their experience. They also help with sales and marketing by providing a personalized experience.

These chatbots can answer FAQs and offer dedicated support to customers in case of any problems. They also assist in site navigation and recommend relevant products to customers.

However, they need regular optimization to provide the best possible user experience. As new customer demands and business priorities change, they should be analyzed and updated accordingly to ensure that they’re giving the correct information.

Businesses also want to make sure that they have a way to escalate queries and concerns to a human agent. In our report, nearly half of consumers said they would prefer a bot to help with simple issues but still want to speak to a human for more sensitive and complex queries.

4. Machine Learning

Machine learning is a subfield of artificial intelligence that allows computers to learn from data without being explicitly programmed. It uses large volumes of structured and unstructured data to help predict outcomes based on past experience.

It’s behind chatbots, predictive text, language translation apps, and the shows you get recommended on Netflix. It’s also helping power autonomous vehicles and machines that diagnose medical conditions.

However, it’s important to note that AI models can also be influenced by human biases. For example, chatbots trained on how people speak on Twitter can pick up on racist or offensive language.

Machine learning has helped companies gain insights previously out of reach, including understanding consumer behaviour in a way that would have been impossible to do without it. The technology is also being used in predictive analytics, fraud detection, and business process automation.