Businesses spend a tremendous amount of time, energy, and money attracting customers. Customer retention becomes a critical issue for companies as the cost of obtaining a customer rises. In some industries, such as the telecommunications industry, customers have a habit of churning, or frequent moving from one carrier to another, signing up to receive bonuses or special offers and leaving as soon as possible for special deals at another carrier.<\/p>\n
By identifying customers at risk of churning, companies can better evaluate whether or not to accept those customers or design strategies to reduce churning and thus increase customer retention. Data mining can help identify customers likely to churn by examining customers who have churned and those who haven't, and identifying characteristics that help predict what a new customer might do.<\/p>\n
SQL Server Data Mining contains multiple algorithms that can perform churn analysis based on historical data. Each of these algorithms will provide a probability, or likelihood, that each of the customers will stay or go. SQL Server Data Mining provides simple tools that make it easy to determine which algorithms and settings produce the most accurate model for a particular situation, guaranteeing that organizations will get the best results possible. Once a company has chosen the best model, it can put the model to work by using the DMX language and SQL Server Reporting Services to deliver the list of most likely churners via Web reports or a SharePoint portal.<\/p>\n","protected":false},"excerpt":{"rendered":"
Businesses spend a tremendous amount of time, energy, and money attracting customers. Customer retention becomes a critical issue for companies as the cost of obtaining a customer rises. In some industries, such as the telecommunications industry, customers have a habit of churning, or frequent moving from one carrier to another, signing up to receive bonuses […]<\/p>\n","protected":false},"author":56,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_monsterinsights_skip_tracking":false,"_monsterinsights_sitenote_active":false,"_monsterinsights_sitenote_note":"","_monsterinsights_sitenote_category":0,"footnotes":""},"categories":[53],"tags":[],"class_list":["post-27","post","type-post","status-publish","format-standard","hentry","category-softwares-de-mineria-de-datos"],"_links":{"self":[{"href":"https:\/\/blog.espol.edu.ec\/pvicuna\/wp-json\/wp\/v2\/posts\/27","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/blog.espol.edu.ec\/pvicuna\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/blog.espol.edu.ec\/pvicuna\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/blog.espol.edu.ec\/pvicuna\/wp-json\/wp\/v2\/users\/56"}],"replies":[{"embeddable":true,"href":"https:\/\/blog.espol.edu.ec\/pvicuna\/wp-json\/wp\/v2\/comments?post=27"}],"version-history":[{"count":3,"href":"https:\/\/blog.espol.edu.ec\/pvicuna\/wp-json\/wp\/v2\/posts\/27\/revisions"}],"predecessor-version":[{"id":29,"href":"https:\/\/blog.espol.edu.ec\/pvicuna\/wp-json\/wp\/v2\/posts\/27\/revisions\/29"}],"wp:attachment":[{"href":"https:\/\/blog.espol.edu.ec\/pvicuna\/wp-json\/wp\/v2\/media?parent=27"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/blog.espol.edu.ec\/pvicuna\/wp-json\/wp\/v2\/categories?post=27"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/blog.espol.edu.ec\/pvicuna\/wp-json\/wp\/v2\/tags?post=27"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}