Customer Data Mining

A survey conducted by Digital Universe Corporation in 2011 showed that in 2011 alone, all companies have generated 8 zettabytes of data. This is equivalent to 8 trillion gigabytes. This is the same as all citizens in the United States will write 3 tweets per minute in the next 26, 976 years. This research also forecasts that by 2020 the total amount of data generated each year will increase to 5,000%. But what if they do not even get useful inferences, what is the focus of massive data collection? .

We adopt 1: 1 direct marketing strategy. One-on-one marketing is to understand each customer and build a sustainable relationship with customers. Due to data fusion and data mining methods, Amway sorts many customers into different categories, each with similar attributes. The attributes of different types of customers are different. Through data fusion and data mining to understand various customers' preferences, adjust products and services, greatly improve customer's business type and product satisfaction

The advantages of concrete data mining differ depending on goals and industry. Sales and marketing departments can minify customer data to increase lead conversion rates or create one-on-one marketing campaigns. You can use the data mining information on past sales models and customer behavior to build predictive models for future sales, new products and services.

CRM is technology that relies heavily on data mining. CRM applications include services that support sales, marketing, and data mining technologies to support that function. Combining these two technical subdivisions is sometimes called "customer data mining". Proponents suggest that customer data mining plus results include improved exploration and market segmentation, improved customer loyalty, cross selling and upselling, reduced risk management needs, and optimization of media advertising expenditure We insist.

Summary - Data warehouse and data mining for CRM (Customer Relationship Management) is a relatively new field for organizations given its strengths. Through data mining, organizations can identify key customers, predict future actions, and make aggressive and knowledge driven decisions. This article describes some data mining techniques for various CRM activities. An important factor for a successful business is to create value for customers. CRM basically attracts, maintains, and manages customers. Business intelligence analyzes and interprets large amounts of customer data and provides practical information that can be used to develop the organization and improve its growth strategy. Due to the ease of data collection in today's world and the low cost of maintaining the data warehouse, access to large amounts of customer data has become easier. Data mining has become the backbone of CRM activities