As one of the foremost trends in technology, Artificial Intelligence (AI) remains to grow in demand for marketers and sales specialists and has developed to be an indispensable mechanism for brands that want to give a hyper-personalized, outstanding customer expertise. The availability of AI-enhanced customer relationship software has made AI to the business without the high prices that earlier joined with the technology. A report showed that 39% of IT leaders are currently using AI or machine learning. AI has numerous applications for industry businesses, and in this blog, we will discuss ways that it can be practiced to enhance the customer experience. Now that we know what it takes to favorably implement artificial intelligence in customer experience.

About the conference The WCAI annual conference, Successful Applications of Customer Analytics is dedicated to real-world applications that exemplify a balance of high-level rigor and business know-how, as well as elevating the role of analytics in an organization's strategic decision-making. WCAI will host not only the full day event on May 10th, but also technical workshops the day before, on May 9th. This year, there are two workshops from 2:00 p.m. – 5;00 p.m. for attendees: Workshop Overview: Deep learning plays a significant role in sentiment analysis, where algorithms can be trained to quickly learn and detect patterns in large volumes of data. In this workshop, we will start by providing an overview on deep learning and on the Apache MXNet deep learning framework. We will next discuss how to address sentiment analysis use cases with deep learning.

"There is only one boss. And he can fire everybody in the company from the chairman on down, simply by spending his money somewhere else." -Sam Walton Companies typically spend most of their effort and resources on customer acquisition, even though the cost of retaining an existing customer is 5 times lower than acquiring a new one. Customer retention is a measure of how many of your customers continue to buy from you over time and are therefore loyal to your brand. Churn, sometimes known as customer attrition, is at the opposite end of the spectrum, i.e. how many customers stop buying from your company. Industries that use a subscription-based business model have traditionally focused more on churn than others. Banks, telecom companies, insurance firms, energy services companies, are among the many types of businesses that often use customer attrition analysis and customer churn rates as one of their key business metrics.

When customers interact with a brand they leave clues about their levels of satisfaction and engagement that can be acted upon by marketers. If you think about the number of touchpoints, from loyalty programme information and purchase behaviour through to online reviews, social media references and conversations with customer service representatives in contact centres, these interactions deliver data that helps marketers to visualise the customer's journey, assess their responses and uncover sentiment. The smallest detail can reveal the most interesting finding, and as the data accumulates across all of these areas, it provides an accurate, and often unexpected perspective. Data relating to customer interactions is both quantitative and qualitative. Structured quantitative data, which might include when the customer last purchased from a brand, how old they are, where they live and the products they most frequently buy, together with qualitative feedback, such as the unstructured voice of the customer needs to be married together.