Area of telecommunication sector is predetermined to take advantage of data analysis methods, because it continuously operates with huge streams of data changing dynamically every second when customers are using the services. Competition for every customer is very crucial here, independently if the company is GSM service operator or stationary phone connection provider. Due to wide public access to telecommunication services the number of potential customers is very large, it corresponds with the number of citizens in active age. Furthermore, acquiring and keeping the customers directly translates to company's profit. Therefore the proper understanding and care of customers is essential and this can not be done without intelligent exploitation of the available data.

This analysis is based on grouping the customers into the segments with similar profile and behavior (e.g. occasional high price purchases, regular low price purchases, teenage single product purchasers, long-term local customers). This will allow you to create segment-specific approach to your customers supported by dynamic update of segmentation and migration analysis between the segments. Customer's profiling offers useful information for designing new products or for proper addressing of marketing campaigns. Data mining enables people to create fast new segmentations and effectively adjust clustering to particular data and specific task requirements.

KCredit scoring is regarded as one of the most successful data modeling applications in business area. It involves an evaluation of your customers based on their application and behavioral data. This analysis can be used in various situations concerning any kind of credit offering to a customer, for example renting a valuable products or devices, mobile phones exchange, deciding on new contracts length with the customer, evaluation and tolerance of billing delays, credit scoring for leasing purposes etc. Identification of customer's risk level (e.g. high/medium/low risk customers, reliability level) enables the company to minimize the risk when providing credit services to their clients.

The goal of this analysis is to identify customers that are likely to leave company and join the competition, what is especially critical in highly competitive market of telecommunication sector, where profit is directly related to number of customers and loosing a customer means he/she will most probably use the competitor's offer. Churn modeling helps to increase the loyalty of customers towards your company in several ways. Discovering the factors causing a churn enables a company to address them properly. Additionally, separating the particular group with high churn likeliness allows you to focus more on your loyal customers.

Survival analysis estimates life time value of a customer and his/her churn hazard over a time (a churn means a customer is turning to different product provider). The analysis describes distribution of the survival time for individuals in a given population, investigates the strength of parameter influence on expected survival time and allows to compare survival time distributions among different subpopulations. By using this method a company can get valuable insight into customer behavior and find ways to increase his/her survival time.

Especially within telecommunication companies, the survival time analysis finds a wide set of applications e.g. deciding when is the best time to update a contract with customer, designing new contract duration and other conditions customized to specific client.

Fraud detection has proved to be powerful method capable of saving significant amount of money to a company as well as maintaining good relations with their customers. Detecting the frauds means identifying suspicious fraudulent transfers, orders and other illegal activities against your company. Models of fraud scoring can be divided into application and behavioral scoring.

Application fraud scoring detects suspicious clients at early stage of signing a contract with the company based on data from the client's application form. Another model - behavioral fraud scoring, is built on data collected during the client's (life time) activities e.g. billing data, usage of services or history of actions. Fraud detection is often applied to avoid telecommunications fraud (various misuse of communication services), computer systems intrusion, Internet transaction fraud, telemarketing fraud, identity theft etc.