Today’s customers can be hard to please. To stay competitive, businesses need to ship quickly, respond to customer inquiries in real time, and seemingly know what customers want before they even know.
Rather than guessing, many companies are turning to artificial intelligence (AI) to fill in the gaps. In fact, according to Gartner, enterprise use of AI has grown 270 percent over the past four years.
Let’s take a look at a few of the most common applications for artificial intelligence in the world of business.
Every business wants to understand what their customers are thinking and feeling. In fact, companies already spend huge amounts of time and money to try to get to know their customers better. Unfortunately, despite the wealth of tools at their disposal, most organizations are not very good at listening to customers.
Why? In many cases, this disconnect is related to the tools themselves – what companies are using and what they’re trying to measure may not work in harmony. In fact, according to the Harvard Business Review, customer satisfaction (CSAT) and Net Promoter Scores (NPS) (the two most widely used measures), fail to tell companies what customers really think and feel, and can even bury serious issues.
When executed properly, improving customer engagement can be a hugely competitive driver of business growth, leading to increased customer loyalty, satisfaction, and advocacy. To understand how to create the right CX, businesses are turning to data-generated insights. Yet, CX datasets can be messy due to the simple fact that customer behaviors are chaotic.
However, this complex data is the reason why AI is needed. With assistance from AI systems, businesses will have a more complete picture of the customers they are serving. After all, AI is much better than humans are at quickly and accurately finding patterns across the myriad of data points inherent in the customer journey. Using these insights, businesses can more accurately deliver what customers want in the way that customers want.
The role of AI has certainly become more prominent in the past few years, yet consumer sentiment is split, with 50 percent of customers believing that virtual assistants and AI chatbots make it harder to resolve an issue.
Therefore, companies need to be careful about implementing AI into the customer experience (CX). Fortunately, several tried-and-tested applications bolster CX:
1. Understand customers better – Using both AI and machine learning to collect and analyze social, historical, and behavioral data helps brands more accurately understand their customers. Unlike traditional data analytics software, AI is continuously learning and improving from the data it analyzes. As a result, it is able to anticipate customer behavior, giving brands more opportunities to offer highly relevant content, expand sales opportunities, and tailor the customer journey to buyers’ needs.
2. Predict customer behavior with real-time decisioning – Real-time decisioning refers to a business’ ability to use up-to-date data to make informed decisions (such as data generated during recent customer interaction). By using AI to improve the real-time decisioning process, businesses can more accurately recognize and understand what the customer wants through the data that they produce. At the same time, adding AI to predictive analytics, businesses will get actionable insights that guide them on when and how to interact with each customer. Further, AI can analyze historical data and provide deeper insights that make the customer experience more relevant and more likely to lead to a sale.
3. Resolve problems faster – When customers want customer support, they want it now. Here, chatbots are an optimal solution by providing 24/7 support, coherently answering frequently asked questions, initiating returns, and more. And when human interaction is needed, chatbots can elevate tickets to a customer service representative, provide them with the context of the call, and allow them to take it from there.
4. Analyze customer feedback – Sentiment analysis can help agents prioritize customer messages by tagging them as “frustrated,” “happy” or “angry.” How? Put simply, sentiment analysis uses natural language processing and machine learning to detect positive or negative sentiment in text. It is often used by businesses to monitor mentions of their brand online and quickly discern sentiment from customer reviews, making it the perfect tool for understanding customers better.
5. Enhance the online shopping experience – AI can easily provide tailored product recommendations based on the user’s past browsing history. At the same time, AI can also adjust product pricing so that it’s always competitive, further making customers feel catered to during their shopping experience. Further, retailers that use chatbots that can answer questions customers have while shopping provide an added, responsive touch that help shoppers feel closer to the brand.
Today's customers expect personalized experiences that span across digital channels.
With our SAI suite – which stands for Stefanini Artificial Intelligence – we can work with you to co-create the right AI-enabled CX solution for your business. SAI is comprised of different technological capabilities, which incorporates AI into data science that enables the development of AI-powered analytics and visualization for reactive, proactive and/or prescriptive systems.
Further, our virtual assistant Sophie is building better user experiences through hyper-personalization. With an omnichannel approach, she can interact with various personas and provide human-like interactions.
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