Banks, telephone service companies, Internet service providers, pay TV companies, insurance firms, and alarm monitoring services, often use customer attrition analysis and customer attrition rates as one of their key business metrics (along with cash flow, EBITDA, etc.) because the cost of retaining an existing customer is far less than acquiring a new one (Wikipedia). According to this article, the probability of selling to a new customer is 60-70%, while the probability of selling to a new prospect is 5-20%. So knowing if a customer is at risk of leaving is one of the most important tasks a company has to perform in order to keep growing its business. The data can be found here on kaggle public datasets. We will predict if a customer will churn based on his informations. There are 7043 customers and 20 features.

Companies with the highest loyalty ratings and retention rates grew revenues 250% faster than their industry peers and delivered two to five times the shareholder returns over a 10 year period. Earning loyalty and getting the largest number of customers to stick around is something that is in the best interest of both a company and its customer base. So why do companies struggle with retention? Other than some subscription-based businesses such as telecom that report Average Revenue Per User (ARPU), most companies aren't required or compelled to disclose this in public filings. Many companies focus on functional priorities instead of the customer, believing customer loyalty will naturally emerge through these efforts.

A top priority in any business is a constant need to increase revenue and profitability. Within the retail industry, the main source of revenue is based on the purchases of customers. For this reason, companies need to focus on customer retention. When a customer leaves or churns from a business, the opportunity for potential sales or cross selling is lost. When a customer leaves the business without any form of explanation or notice, the company may find it hard to respond and take corrective action.

Dynamic pricing, a practice started by American Airlines, a FICO customer, in the 1980s, has now become a common marketing discipline for many corporations across industry sectors. From airlines, hotels and entertainment events to perhaps the most well-known e-retailer, Amazon, these companies have been using dynamic pricing to improve profitability relative to rapid changes in supply and demand. As technologies for machine learning (ML) and artificial intelligence (AI) become more advanced and the dimensions of available data expand, dynamic pricing is going beyond its traditional inventory management function, enabling companies to deliver optimal customer experiences in real-time. In essence, pricing is becoming dependent on the ability to make offers that continuously adjust to changing consumer behavior and preferences while also responding to organizational inventory and profit requirements as well as other external pricing influences. Today, enterprises are able to marry rich data sets with sophisticated pricing models and apply advanced analytics and machine learning techniques to produce pricing alternatives across thousands of product stock keeping units (SKUs).

Sicap is launching the artificial intelligence enabler Sicap AI Engine. When combined with TargetMe, Sicap's customer engagement automation platform, the new product helps mobile operators to predict and reduce customer churn. Before the AI Engine is deployed, its neuronal network system is trained by using an operator's historic data. To increase the prediction accuracy over time, the training is continued using the operator's actual data. The product provides a churn prediction list including potential causes for churn and subscriber segments, based on their likelihood to churn within certain confidence intervals.