What is the heart of any business? We think the answer is customers. High client satisfaction, loyalty, and advocacy are the main goals of today’s market leaders. Every entrepreneur strives to make the business popular and achieve the best customer experience. But not all of them know how to understand consumer’s real needs and address them appropriately.

How to enhance user satisfaction? What technological solutions can help? We understand your perplexity and decided to address all your questions in this article. Here, we’ll share the most prominent use cases of next-generation client services with AI and ML and how you can benefit from them.

Improving personalization with AI customer experience

All customers are different, and each of them has their own preferences and needs. Knowing your target audience is vital to deliver a personalized service. And this poses a significant problem for entrepreneurs: how to analyze thousands or even millions of customer activities every day to address their particular needs?

If gathering and processing data manually, you can barely study a tremendous amount of information and juxtapose it with customers’ actual needs. Moreover, a human can’t predict the user’s further actions and provide customized suggestions. And that’s where artificial intelligence and machine learning come into play. One of the most widespread uses of AI and ML in customer experience is service personalization and below are the most common use cases:

  1. AI-powered chatbots. Modern chatbots are far from those annoying ones used in previous years. Now, chatbots are becoming more like helpful personal assistants that can collect insights during small talks with customers. Then, chatbots can send these datasets to your CRM for further personalization steps.
  2. Sentiment analysis. This approach uses the Natural Language Processing (NLP) technology to work with raw data. For instance, if your customer leaves a comment on your social media page or gives feedback in a particular form, sentiment analysis tools detect the connotation of these words and show how users feel about your product or service. These insights can help you improve areas that confuse your customers and provide solutions to retain them.
  3. Hybrid recommender systems. In recent years, hybrid recommender systems have become quite popular technology that generates several highly-personalized content delivery strategies. Advanced recommender systems combine user’s past behavior models and real-time models to provide even more accurate offerings. As an example, consider how Netflix uses this technology to personalize recommendations and predict customer preferences. It processes users’ demographics, personal preferences, and viewing history to delve into one customer’s tastes and suggest the next series to watch. Thanks to its recommendation engine and machine learning customer retention, the company saves $1 billion per year.

Enhancing customer support

The key to delivering the best customer experience lies in timely and quality support. Some companies choose to add new communication channels to keep pace with the high volume of live support calls. But this solution just adds complexity to service operations and raises expenses.

Alternatively, you can employ AI self-service technology that enables users to solve minor problems or easily find answers without calling a support agent.  Apart from the growing demand for this technology among users, business owners also see its advantages in numbers. As much as 40% of live support queries today can be resolved with self-service options.

Self-service digital assistants can help customers address most of the routine questions quickly and save time waiting for a customer support agent. Furthermore, self-service options are available 24/7, which you can’t expect from humans. Consider this solution for mundane tasks and frequently asked questions while your customer support will focus on high-value requests.

Advancing upselling and cross-selling

You might think upselling and cross-selling strategies work well only for a business, not for a customer. But that’s not entirely true. When searching for a particular product or service and getting relevant add-ons, customers are more likely to complete the whole order in one place. This means intelligent upselling and cross-selling encourages customer loyalty and provides clients with more flexible choices. But how to make these strategies work smartly and provide suitable suggestions? Converging AI algorithms, ML, and intelligent virtual assistants can process data from marketing and sales to automatically create and send advanced upselling and cross-selling offerings to target customers.

Summing up the best customer experience factors

Artificial intelligence and machine learning have already done much for businesses and particularly for the best customer experience. Smart digital assistants, streamlined support, personalization of offers and suggestions are just a few examples of how AI and ML improve customer satisfaction. These technologies open new opportunities for organizations to refine the understanding of customer needs and find approaches to cover them.

However, enterprises’ challenge lies in designing the right strategies for implementing AI and ML solutions and creating a great customer experience. We know that many companies aren’t sure where to start this journey and find it hard to implement AI services themselves. This is where choosing a reliable and reputed technology partner like TEAM International can save your time, avoid unnecessary costs, and gain a competitive advantage.