## 2 Literature Review
### Customer Relationship Management and Predictive Marketing  
Customer Relationship Management (CRM) started gaining popularity in the late 1990s through work conducted by American tech companies such as IBM and Gartner. There are four main types of CRM--strategic, operational, analytical, and collaborative--which all aim to manage the company's relationship with both potential and current customers:  
* Strategic CRM, which is the use of customer data through systematic analysis as a means of marketing management [13];
* Operational CRM, which supports company operations whereby information on employees, customers, and leads is stored;
* Analytical CRM, which is a strategy whereby customer and market information is analyzed to aid the decision-making process for business management [14];
* Collaborative CRM, which allows for multi-way communications between companies and their customers, with the goal of facilitating more profitable retention of customers [15]. The primary goal of CRM is to maximize customer retention while attracting a steady stream of new customers to maximize the company's revenue streams [16]. By adopting certain CRM methodologies, companies can improve their understanding of how to engage and interact with their customers to form mutually beneficial relationships through collaboration [17]. Utilizing information technology and information systems in CRM is becoming increasingly important. More specifically, modern data analysis techniques combined with the increased amount of available data have made this approach particularly attractive to businesses. It is critical, therefore, for companies to adopt such practices to achieve innovative CRM capabilities. Machine learning has emerged as one of the most influential and popular tools to increase the effectiveness of any CRM strategy. The main four dimensions [18] across which ML is commonly applied in the context of CRM are customer identification (target customer analysis and customer segmentation), customer attraction (marketing), customer retention (loyalty program analysis, one-to-one marketing, complaint, and conflict management), and customer development (customer lifetime cycle analysis, upselling and cross-selling, the market basket analysis). Big data analytics is transforming businesses by transferring the focus from products and channels to the customer, with the aim of maintaining personal relationships and moving from mass marketing to highly personalized marketing. Predictive analytics, used to transform raw data into useful information, has great relevance for marketing purposes, allowing the prediction of customer behavior and allocation to specific groups of customers. By using innovative instruments, companies can adopt big-data-driven, micro-targeting marketing practices, which permit the improved precision of segmentation and targeting. Predictive marketing is a powerful tool that can greatly enhance a company's CRM capabilities. One of the predictive marketing methods is cluster analysis. Cluster analysis algorithms can manage vast amounts of data to identify elements that characterize certain consumers and find correlations that are difficult to identify manually. Conjoint analysis is also a method adopted in predictive marketing for CRM. It is a research technique used to identify customer preferences by identifying relevant attributes for consumers in the selection and purchase processes. The outputs of conjoint analysis often guide business decisions on new products and promotions. Moreover, online reviews have a strong impact on consumer choices, and sentiment analysis or opinion mining can potentially predict future sales and assist marketing strategy. Sentiment analysis is important for a better understanding of consumer preferences, which leads to the identification of precise targets and better advertising strategies. [PERSON] et al. [19] conducted sentiment analysis to analyze the effect of the emotions expressed on social media platforms by Indian customers regarding automobile companies' stock prices.