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Data Mining Applications

The amount of information is increasing exponentially. However, companies need to learn how to interact with today's volumes of data in order to increase their efficiency and be competitive in the market.

Data Miner
Data Mining Applications

Data Mining for Telecom

Telecommunications companies actively and effectively use Data Mining algorithms to explore their own data. And not surprisingly, providers in the IT industry are realizing the competitive advantages of data analytics.

Predicting traffic volume and minutes of conversation next month for a client.

Regression problem. Having information about the client's activities (communication / internet), it is possible to predict the volume of traffic and minutes of conversation, as well as the number of SMS messages in the next month. For example, using the Data Mining algorithm, it was revealed that the client will exceed the norm for his tariff next month. This information will allow the customer to propose rate adjustments in advance.

Analysis of the 'cold' call base to identify potential customers.

Classification problem. Depending on the number of calls, their duration and other known customer data, it is possible to classify customers into categories according to the effectiveness of cold calls. The information received will allow you to better search for customers without wasting time on deliberately unsuccessful options.

Revealing customer loyalty.

Data Mining can be used to determine the characteristics of clients who, once using the services of a given company, are most likely to remain faithful to it. As a result, the funds allocated for marketing can be spent where the return is most.

Data Mining for Banks

One of the most extensive areas of business where the application of Data Mining algorithms is relevant is the banking sector. Here are some practical scenarios:

Customer retention / predicting customer churn.

The classical problem of binary classification, which has the following formulation: 'Having enriched historical data on client transactions, it is necessary to predict whether the client will leave the company in the next month or not.' You can project the task into some sections: for example, the bank decided to hold a campaign to attract customers - a 3-month grace period. In this situation, it is required to predict whether the client will continue to use the services on general terms or will leave the bank.

Identifying credit card fraud.

By analyzing past transactions that were subsequently found to be fraudulent, the bank's security service identifies some stereotypes of such fraud and takes preventive measures.

Predicting the volume of POS transactions in the next month for each customer.

A regression problem in which you want to predict the volume of POS transactions for each customer. Solving this problem allows segmenting customers and planning in advance and adjusting personal offers and new promotions for customers.

As well as many other examples.

Data Mining for Banks
Data Mining Applications

Data Mining for Retail

Shopping basket analysis.

It is required to identify products that buyers seek to purchase together. Knowing the shopping basket is essential for improving advertising, developing inventory strategies and how to arrange them in sales areas.

Exploring Temporary Patterns.

With the help of historical data, retailers can make decisions about the creation of inventories, focused on the dynamics of customer demand over a time interval. As a result, the business gets answers to questions like ''If a customer bought a video camera today, then after what time will he most likely buy new batteries and what percentage of customers will immediately purchase an additional memory card''.

Data Mining in Automotive

Satisfaction forecast.

When assembling cars, manufacturers must consider the requirements of each individual customer, so they need the ability to predict the popularity of certain characteristics and the knowledge of which characteristics are usually ordered together.

Guarantee policy.

Manufacturers need to predict the number of customers that will submit warranty claims and the average cost of claims.

Create predictive models.

Allows merchants to recognize the nature of the needs of various categories of customers with certain behaviors, for example, buying products from famous designers or attending sales. This knowledge is needed to develop highly targeted, cost-effective promotional activities.

As well as many other examples.

Data Mining for Insurance Companies

Predicting problems in certain insurance segments.

Classification problem. Based on historical data on the client's activities, such as car / life / house insurance, the availability of insurance payments, it is possible to predict and segment the audience. As a result, with the help of the obtained profiles, it will be possible to adjust the insurance conditions and give an assessment of 'trust' to each client.

Analysis of the effectiveness of informing clients.

Classification problem. Using historical data on phone usage for technical support, SMS and online applications, it is necessary to classify customers into categories that tend to receive information through their preferred communication channel. The results obtained will make it possible to adapt the information and technical support of customers, taking into account their preferences in the method of obtaining information, which will qualitatively improve the service.

Data Mining for Medical Companies

Formulation of medical diagnoses.

There are many expert systems for solving this problem, which are built mainly on the basis of rules describing the combination of various symptoms of various diseases. With the help of such rules, they will find out not only what the patient is sick with, but also how to treat him. The rules help to choose means of medication, determine indications and contraindications, navigate medical procedures, create conditions for the most effective treatment, predict the outcomes of the prescribed course of treatment, etc. Data Mining technologies make it possible to detect patterns in medical data that form the basis of these rules.

Forecasting the demand for medications.

Data Mining allows you to predict the demand (secondary sales) for medications for more accurate planning of shipments (primary sales), inventory and production.

As well as many other examples.


Data Mining in Industrial production

Determination of the components

affecting the characteristics of alloy steel, and obtained the values of their influence effects. You can build various regression models, solve many important practical problems, for example, show how the strength of bearings increases with an increase in the proportion of molybdenum, what is the effect of sealing rings made of a new material, etc.

In metallurgy and mechanical engineering, problems related to equipment failure,

– roller wear, bearing failure, bearing seizure, etc.

As well as many other examples.