Learn, Explaining the Different types of Data Mining Model!
Data Mining Models: Basically The data mining model are of two types. First Predictive and, Descriptive. Also learn, How to explain Organizational Culture? Meaning and Definition!
Descriptive Models: The descriptive model identifies the patterns or relationships in data and explores the properties of the data examined. Ex. Clustering, Summarization, Association rule, Sequence discovery etc. Clustering is similar to classification except that the groups are not predefined, but are defined by the data alone. It is also referred to as unsupervised learning or segmentation. It is the partitioning or segmentation of the data in to groups or clusters. The clusters are defined by studying the behavior of the data by the domain experts.
The term segmentation is used in very specific context; it is a process of partitioning of database into disjoint grouping of similar tuples. Summarization is the technique of presenting the summarize information from the data. The association rule finds the association between the different attributes. Association rule mining is a two step process: Finding all frequent item sets, Generating strong association rules from the frequent item sets. Sequence discovery is a process of finding the sequence patterns in data. This sequence can be used to understand the trend.
Predictive Models: The predictive model makes prediction about unknown data values by using the known values. Ex. Classification, Regression, Time series analysis, Prediction etc. Many of the data mining applications are aimed to predict the future state of the data. Prediction is the process of analyzing the current and past states of the attribute and prediction of its future state. Classification is a technique of mapping the target data to the predefined groups or classes, this is a supervise learning because the classes are predefined before the examination of the target data.
The regression involves the learning of function that map data item to real valued prediction variable. In the time series analysis the value of an attribute is examined as it varies over time. In time series analysis the distance measures are used to determine the similarity between different time series, the structure of the line is examined to determine its behavior and the historical time series plot is used to predict future values of the variable.
Model Types Used by Data Mining Technologies
The following represents a sampling of the types of modeling efforts possible using Nuggets the Data Mining Toolkit offered by Data Mining Technologies for the banking and Insurance Industries. Many other model types are used and we would be happy to discuss them in more detail if you contact us. Don’t forget to read it, The Importance Benefits of Corporate Retreats in Business!
Claims Fraud Models
The number of challenges facing the Property and Casualty insurance industry seems to have grown geometrically during the past decade. In the past, poor underwriting results and high loss ratio were compensated by excellent returns on investments. However, the performance of financial markets today is not sufficient to deliver the level of profitability that is necessary to support the traditional insurance business model. In order to survive in the bleak economic conditions that dictate the terms of today’s merciless and competitive market, insurers must change the way they operate to improve their underwriting results and profitability.
An important element in the process of defining the strategies that are essential to ensure the success and profitable results of insurers is the ability to forecast the new directions in which claims management should be developed. This endeavor has become a crucial and challenging undertaking for the insurance industry, given the dramatic events of the past years in the insurance industry worldwide. We can check claims as they arrive and score them as to the likelihood of they are fraudulent. This can results in large savings to the insurance companies that use these technologies.
Customer Clone Models
The process for selectively targeting prospects for your acquisition efforts often utilizes a sophisticated analytical technique called “best customer cloning.” These models estimate which prospects are most likely to respond based on characteristics of the company’s “best customers”. To this end, we build the models or demographic profiles that allow you to select only the best prospects or “clones” for your acquisition programs. In a retail environment, we can even identify the best prospects that are close in proximity to your stores or distribution channels. Customer clone models are appropriate when insufficient response data is available, providing an effective prospect ranking mechanism when response models cannot be built.
The best method for identifying the customers or prospects to target for a specific product offering is through the use of a model developed specifically to predict response. These models are used to identify the customers most likely to exhibit the behavior being targeted. Predictive response models allow organizations to find the patterns that separate their customer base so the organization can contact those customers or prospects most likely to take the desired action. These models contribute to more effective marketing by ranking the best candidates for a specific product offering thus identifying the low hanging fruit.
Revenue and Profit Predictive Models
Revenue and Profit Prediction models combine response/non-response likelihood with a revenue estimate, especially if order sizes, monthly billings, or margins differ widely. Not all responses have equal value, and a model that maximizes responses doesn’t necessarily maximize revenue or profit. Revenue and profit predictive models indicate those respondents who are most likely to add a higher revenue or profit margin with their response than other responders.
These models use a scoring algorithm specifically calibrated to select revenue-producing customers and help identify the key characteristics that best identify better customers. They can be used to fine-tune standard response models or used in acquisition strategies.
Cross-Sell and Up-Sell Models
Cross-sell/up-sell models identify customers who are the best prospects for the purchase of additional products and services and for upgrading their existing products and services. The goal is to increase share of wallet. Revenue can increase immediately, but loyalty is enhanced as well due to increased customer involvement.
Efficient, effective retention programs are critical in today’s competitive environment. While it is true that it is less costly to retain an existing customer than to acquire a new one, the fact is that all customers are not created equal. Attrition models enable you to identify customers who are likely to churn or switch to other providers thus allowing you to take appropriate preemptive action. When planning retention programs, it is essential to be able to identify best customers, how to optimize existing customers and how to build loyalty through “entanglement”. Attrition models are best employed when there are specific actions that the client can take to retard cancellation or cause the customer to become substantially more committed. The modeling technique provides an effective method for companies to identify characteristics of chumers for acquisition efforts and also to prevent or forestall cancellation of customers.
Marketing Effectiveness Creative Models
Often the message that is passed on to the customer is the one of the most important factors in the success of a campaign. Models can be developed to target each customer or prospect with the most effective message. In direct mail campaigns, this approach can be combined with response modeling to score each prospect with the likelihood they will respond given that they are given the most effective creative message (i.e. the one that is recommended by the model). In email campaigns this approach can be used to specify a customized creative message for each recipient.