AI in Customer Experience

As one of the leading trends in technology, Artificial Intelligence (AI) continues to gain in popularity for marketers and sales professionals, and has grown to be an essential tool for brands that wish to provide a hyper-personalized, exceptional customer experience. The availability of AI-enhanced customer relationship management (CRM) and customer data platform (CDP) software has brought AI to the enterprise without the high costs that were previously associated with the technology.

The combination of AI and machine learning for gathering and analyzing social, historical and behavioral data enables brands to gain a much more accurate understanding of its customers. Unlike traditional data analytics software, AI is continuously learning and improving from the data it analyzes, and is able to anticipate customer behavior. This allows brands to provide highly relevant content, increase sales opportunities, and improve the customer journey. 

    To deliver truly excellent experiences, all customer-focused business units—like sales, customer service and marketing—must work together and efficiently leverage AI tools for common goals. By doing this, AI has the potential to help brands connect with customers on a more personal level, thus increasing loyalty and securing trust not just for now, but post-pandemic as well.

 Real-Time Decisioning and Predictive Behavior Analysis
 Real-time decisioning is defined as the ability to make a decision based on the most recent data that is available, such as data from the current interaction that a customer is having with a business — with near-zero latency. Precognitive’s Decision-AI, for instance, features a sub-200 millisecond response time to assess any event in real-time using a combination of AI and machine learning. Decision-AI is part of Precognitive’s fraud prevention platform, and can be integrated on a website using an API.

    Real-time decisioning can be used for more effective marketing to customers. One example of real-time decisioning is to identify customers that are using ad blockers, and provide them with alternative UI components that can continue to engage them. Another is personalized recommendations, which are used to present more relevant content to the customer. By using AI and real-time decisioning to recognize and understand a customer’s intent through the data that they produce, in real-time, brands are able to present hyper-personalized, relevant content and offers to customers.

    Predictive analytics refers to the process of working with statistics, data mining, and modelling to make predictions. Because AI is able to analyze large amounts of data in a very short amount of time, it uses predictive analytics to produce real-time, actionable insights that guide the next interactions between a customer and a brand. This is often referred to as predictive engagement, and it requires the knowledge of when and how to interact with each customer, something that AI is very good at.