An analysis of the four main tasks of data mining in business world

Two Styles of Data Mining 1. Top-down approach Used when we know approximately what we are looking for or what we want to predict Predictive model uses experience to rank possible outcomes in the future by calculating a score for each outcome Model is seen as a black box because we care only about the predictions and not how it actually works Goal of building a predictive model is to apply knowledge gained in the past to the future Example problem: Which customers are likely to buy a specific type of car?

An analysis of the four main tasks of data mining in business world

Share October 29,by Intetics Inc. The banking industry is highly competitive.

The Virtuous Cycle of Data Mining

It is sensitive to political and economic conditions in their domestic countries and all over the world. Because of a lot of risks, a key strategy for many banks is to improve their performance by reducing costs and increasing revenues. One of the best ways to realize both objectives is to use data mining to extract valuable information from customer data.

An analysis of the four main tasks of data mining in business world

The objectives of our present research is to define: What business strategies are best solved using Big Data analysis and Data Mining How banking executives can evaluate these strategies according to two criteria: In each case, collection of more data can lead to significant improvements in performance.

Banks already have a variety of data about customers. In addition to personal information and data about accounts and transactions, banks can collect data such as purchase histories, channel usage, and geo-locational preferences.

How Businesses Can Use Data Clustering

Market analysis and customers insight In context of our current article this wide group includes relatively new strategies for market analysis and customer insight based on gathering and processing data from the Internet. This information can be used to attract new customers, increase loyalty of current customers, and gain competitive advantage due to a deeper understanding of market tendencies and customer preferences.

Study of channel performance This group combines two approaches. The first one is using all data from banking channels in a more efficient way with the aim to increase their profitability. Risk management Credit scoring systems and fraud protection techniques are well-known applications of data mining analytics in banking industry.

A modern trend in this group is extending the volumes of information that is used as predictors in data dining models. Social media interactions, transactions, purchase patterns and so on could be used as additional sources of information in risk management.

We now know what business tasks can benefit from data mining and how. But where to start first? We next asked some banking experts their opinion on which tasks are most vital to achieving lower costs and higher revenues. The finance sector experts evaluated each group based on how significant each group was to the banking sector and how urgently changes were needed.

They weighted these two criteria and ranked the groups on a scale of 1 to 20, where 1 was the most urgent and significant, and 20 — least urgent and significant.

An analysis of the four main tasks of data mining in business world

The group with the lowest score is the most urgent and significant set of business tasks banking executives should focus on: According to our research, customer experience management is the number 1 most significant and urgent topic in modern banking.

Implementation of data mining in this set of business tasks is the best way to achieve customer centric banking and improve cross-selling and up-selling.From the First Step - Business Analysis as a task can be performed by the Data Analysis particularly during the initial stage when the Data model, Data analysis model and Presentation model are defined.

In the Initial stage, when models are defined, we always start with understanding the Business problem. 5 Image Mining There are two major issues that will affect the image data mining process.

One is the notion of similarity matching and the other is the generality of the application area, that is, the breadth of usefulness of data and! #"!$% & ('.). *",) = + &,%) /). Major Clustering Techniques in Data Mining and Customer Clustering The four major categories of clustering methods are partitioning, hierarchical, density-based and grid-based.

However, for customer relationship management (CRM) and marketing programs, customer clustering emerges as the most important strategy.

We report on the panel discussion held at the ICDM'10 conference on the top 10 data mining case studies in order to provide a snapshot of where and how data mining techniques have made signi¯cant real-world impact. ranging from scientific discovery to business intelligence and analytics.

What Is Clustering in Data Mining?

concepts from machine learning and statistics. The main parts of the book include exploratory data analysis, pattern mining, clustering, and classification. The book lays the basic foundations of these tasks and Data Mining and Analysis: Fundamental Concepts .

Major Clustering Techniques in Data Mining and Customer Clustering The four major categories of clustering methods are partitioning, hierarchical, density-based and grid-based.

However, for customer relationship management (CRM) and marketing programs, customer clustering emerges as the most important strategy.

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