Opposite to rule-based chatbots, AI ones have natural language processing and machine learning tech. This helps them understand the intent of a customer request and formulate relevant answers. Moreover, imitating a human-like conversation is perfect for saving agents time and letting them focus on more complicated tasks. 

Similar to live chats, businesses place AI chatbots on high-traffic website pages to proactively engage visitors in a conversation and:

• answer the most simple questions, 
• advice on products,
• provide pricing/shipping info,
• book tickets, apartments, appointments,
• capture users' feedback,
• guide customers to order placement, etc.

Thus, instead of filling out a boring contact form or browsing the entire website on their own, your visitors are involved in a lovely conversation and enjoy an immediate response.  

Here is a great example of how AI chatbot Artie provides simple answers to the visitor’s questions and routes the conversation to a human agent. AI chatbot is the best solution to turn a classic process of answering FAQs into a thoughtful conversation. 

There are a lot of repetitive tasks that your agents can automate with AI. Some customer support automation opportunities are connected with support team workflow, others — with understanding customers and their requests. In both cases, AI saves time and helps to prevent your team from burning out by focusing on exciting tasks only.

Here are a few examples of how AI natural language processing and machine learning can ease your support agents' lives:

• AI can interpret customers' requests and suggest a relevant solution, article, or product in a chat or display it on the agent's screen while they're on the call.
• For better prioritization, AI can qualify customer requests, add ticket tags, or route conversations to the relevant agent. 
• Opposite to agents, different language is not a barrier for AI to process a customer query. Thanks to the auto-translation feature many solutions offer these days, AI can communicate hundreds of languages fluently.
• AI can remind your agents about task deadlines. For example, a day and time when they promised to call a client back.
• It is not a problem for AI to analyze previous call recordings or chats to define customers' interests, the most helpful agent responses, etc. All that data goes to a customer profile, saved replies, or any format you need.
• During a call, AI can sense the moment the conversation escalates and suggest your agents change the approach.

There are the most compelling cases of AI implementation in customer support today. But it doesn't mean that's the end of its potential.

The higher level of email personalization is when subscribers receive educational content that meets their interests at the moment when they require it 100%. To go beyond addressing them by name, you require raw data from various sources. 

And in terms of refusing cookies, AI becomes an excellent solution to this task. Businesses use AI to analyze customers' behavior and implement that info to optimize email campaign content and sending time.

For instance:

• If AI defines your subscriber checks the inbox in the morning, it will send your digest at that exact time. Thus, it reduces the number of unwanted emails and your email has more chances to be read. 
• If AI sees that your subscriber has never purchased an item over $1K, it will automatically recommend similar products in the email message. 
• If AI notices that some of your subscribers passed the "loyal customers" benchmark (by time or amount of purchases), it can reward them with bonuses.  
• If AI sees the repetitive pattern in categories/web pages/content subscribers engages with, it personalizes the offers list in an email.

Let's be fair. Businesses aren't good at listening to customers. They spend a vast amount of resources to get to know them better. But still, it isn't enough. And that's not an issue of a try. It is more about the tools businesses use. 

For example, imagine the case of an insurance company with a problem of an increasing number of customers asking for a higher-level manager to talk to. First, they used CSAT and NPS surveys. But the results the got weren't enough to find the reason for the issue. So the QA team decided to analyze the recordings of each incoming call during the last month. Sure thing, they'd spent weeks doing that… if not an AI.

With the results of calls' sentiment and keyword spotting analysis, they defined the root cause of the customer escalations and stalled it.

The same story with customers' behavior and preferences. One thing is to collect and manage data, but quite another is to analyze it. Good to know that AI can bring you valuable insights about customers in the blink of an eye based on their:

• geolocation events,
• chat history with your brand, 
• previous purchases,
• website behavior,
• socials, email account,
• psychographic info.

With user behavior recording, you can build rich portraits of your buyer personas with info about products they prefer, the content they want to read, etc. So marketers can target customers most likely to buy your products and provide them with a highly-personalized experience.

Sure thing, studying customer feedback is not a problem if you have a dozen of them per month. But what if thousands of customers leave feedback in a written, video, or even audio format? 

There are tons of content you can't ignore because it influences the quality of your customer service directly. Thus, there are two options: it can be either your agents who spend days reviewing all that data or AI doing that in a matter of minutes. 

The choice is obvious.

For example, you have the following comment: "I was disappointed with product updates. It looks like every time your team announces them, it is over $1K in service. Developers seem to have a hard time diagnosing the problem, and it seems to be getting more and more expensive." 

When AI analyzes its copy with an NLP approach, it defines keyword groups. Thus, the words like "over $1K," "have a hard time," and "more expensive" are categorized under complaints and go into the specialized vocabulary of your customers. Eventually, AI combines it with traditional rating scales for better processing by agents. 

As a result, you have a great source of insights directly shaping your customer support, service, marketing, and product efficiency.