Social profiling | Solutional Webdesign Antwerpen

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Social profiling is a technique that involves the collection and analysis of personal information from online and offline sources, especially redes sociais^[3] platforms. This process is used to build individual and group profiles based on user engagement, interests, locations, and social networks. Key methods for data analysis include machine learning^[2], ontology, and fuzzy logic. This practice also raises issues of privacidade^[4] e security^[5], as it involves access to personal information. Marketers use social profiling for customer relationship management^[1] and to tailor their strategies. Social profiling also intersects with data mining and social information processing, which are essential in the digital landscape. However, users need to exercise caution with the information they share online to prevent potential privacy leaks.

Definições de termos

1. ↑ customer relationship management.

1 Customer Relationship Management, often abbreviated as CRM, is a strategic framework that businesses use to manage and improve their interactions with customers. Originating from the concept of database marketing in the early 1970s, CRM has evolved to incorporate technological advancements like data warehousing and software as a service (SaaS). It's categorized into strategic, operational, analytical, and collaborative types, each serving different functions. The primary goal of CRM is to boost customer loyalty and satisfaction, reduce complaints, and enhance the value of customer relationships. A strong CRM strategy involves collecting customer data, training employees, and leveraging social and location-based services to improve customer engagement. It offers benefits like improved customer knowledge, customized interactions, and enhanced efficiency.

2 Customer Relationship Management, commonly referred to as CRM, is a strategy used by businesses to manage and improve their interactions with customers. Originating in the early 1970s, its development was marked by key milestones such as the introduction of database marketing in 1982 and the design of the first CRM product in 1993. CRM can be categorized into four types: Strategic, Operational, Analytical, and Collaborative, each with a unique focus area. The CRM system consists of various components such as marketing, data aggregation, and CRM-specific software, all aimed at building and managing customer relationships effectively. The benefits of employing CRM can be seen in improved customer satisfaction, efficient sales force, and personalized marketing approaches. The field of CRM is continuously evolving, responding to trends and developments such as customer-centric strategies and the impact of global events on customer behavior.

3 Customer Relationship Management, often abbreviated as CRM, is a strategic framework that businesses use to manage and improve their interactions with customers. Originating from the concept of database marketing in the early 1970s, CRM has evolved to incorporate technological advancements like data warehousing and software as a service (SaaS). It's categorized into strategic, operational, analytical, and collaborative types, each serving different functions. The primary goal of CRM is to boost customer loyalty and satisfaction, reduce complaints, and enhance the value of customer relationships. A strong CRM strategy involves collecting customer data, training employees, and leveraging social and location-based services to improve customer engagement. It offers benefits like improved customer knowledge, customized interactions, and enhanced efficiency.

2. ↑ machine learning. Machine learning, a term coined by Arthur Samuel in 1959, is a field of study that originated from the pursuit of artificial intelligence. It employs techniques that allow computers to improve their performance over time through experience. This learning process often mimics the human cognitive process. Machine learning applies to various areas such as natural language processing, computer vision, and speech recognition. It also finds use in practical sectors like agriculture, medicine, and business for predictive analytics. Theoretical frameworks such as the Probably Approximately Correct learning and concepts like data mining and mathematical optimization form the foundation of machine learning. Specialized techniques include supervised and unsupervised learning, reinforcement learning, and dimensionality reduction, among others.