Group :4SushantShaliniKunal

Outline: What is data mining?Why use data mining?How does data mining workThe process of data miningTools of data mining Diamonds in the Data MineCRM and BI at Harrahs

Generally, data mining (sometimes called dataor knowledge discovery) is the process ofanalyzing data from different perspectives and summarizing it into useful information. It allows users to analyze data from many different dimensions or angles, categorize it, and summarize the relationships identified. Technically, data mining is the process of finding correlations or patterns among dozens of fields in large relational databases. Data Mining, also known as Knowledge-Discovery in Databases (KDD), is the process of automatically searching large volumes of data for patterns. Data Mining is a fairly recent and contemporary topic in computing. However, Data Mining applies many older computational techniques from statistics, machine learning and pattern recognition.

A simple example of data mining is its use in a retail sales department. If a store tracks the purchases of a customer and notices that a customer buys a lot of silk shirts, the data mining system will make acorrelation between that customer and silk shirts.

The sales department will look at that information and may begin direct mail marketing of silk shirts tothat customer, or it may alternatively attempt to get the customer to buy a wider range of products..

Another widely used (though hypothetical) example is that of a very large North American chain of supermarkets. Through intensive analysis of the transactions and the goods bought over a period of time, analysts found that beers and diapers were often bought together. The grocery chain could use this newly discovered information in various ways to increase revenue. For example, they could move the beer display closer to the diaper display. And, they could place the high-profit diapers next to the high-profit beers.

Data is one of the most valuable assets for any corporation - but only if we know how to reveal valuable knowledge hidden in raw data. Data mining allows us to extract diamonds of knowledge from historical data and predict useful outcomes form that. Data mining can- * optimize business decisions, * increase the value of each customer and communication, and *improve satisfaction of customer with your services.

Data mining creates link between separate transactions and analytical systems in a large-scale information technology. It uses various software to analyze relationships and patterns.

Generally,the following four types of relationships are sought:

A task of finding a function that maps records into one of several discrete classes. For example, a restaurant chain could mine customer purchase data to determine when customers visit and what they typically order. This information could be used to increase traffic by having daily specials.

Clustering is a task of identifying groups of records that are similar between themselves but different from the rest of the data. For example, data can be mined to identify market segments or consumer affinities

Association : Data can be mined to identify association. The beer-diaper example is an example of associative mining.

Data is mined to anticipate behavior patterns and trends. For example, an outdoor equipment retailer could predict the likelihood of a backpack being purchased based on a consumer's purchase of sleeping bags and hiking shoes.

The process of data mining consistsof three stages: 1) The initial exploration, 2) model building or pattern identification with validation or verification, and 3) deployment (i.e., the application of the model to new data in order to generate predictions).

This stage usually starts with data preparation which may involve cleaning data, data transformations, selecting subsets of records and - in case of data sets with large numbers of variables ("fields") performing some preliminary feature selection operations to bring the number of variables to a manageable range).

This stage involves considering various models and choosing the best one based on their predictive performance (i.e., explaining the variability in question and producing stable results across samples).

That final stage involves using the model selected as best in the previous stage and applying it to new data in order to generate predictions or estimates of the expected outcome.

Artificial NeuralNetworks: Non-linearpredictive models that learn through training and resemble biological neural networks in structure.

Genetic algorithms: Optimization techniques that use processes such as genetic combination, mutation, and natural selection in a design based on the concepts of natural evolution.

Decision trees: Treeshaped structures that represent sets of decisions. These decisions generate rules for the classification of a dataset

Nearest neighbormethod: A technique thatclassifies each record in a dataset based on acombination of the classes of the k record(s) most similar to it in a historical dataset (where k 1). Sometimes called the k-nearest neighbortechnique

Rule induction: The extraction of useful if-then rules from data based on statistical significance.

Data visualization: The visual interpretation of complex relationships in multidimensional data. Graphics tools are used to illustrate data relationships.

In 2002 during a sluggish economy:Harrahs recorded 16 straight quarters of revenue growth in its 26 casinos

It posted $4 billion+ in revenues, while rivals wilted

What was Harrahs secret?1-*

CRM Strategy: Get to know customers better and betterAcquire a rich repository of customer information

Slice and dice data finely to develop marketing strategies

Identify core customers by predicting their lifetime value

Gather increasingly specific information about customers preferences then appeal to those interests

Reward employees for prioritizing customer service

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Sales TeamWhat are you trying to sell?Define your sales strategy.Brainstorm about the data you would like to collect (be precise) to execute your sales strategy. How will you obtain the data?How will you combine the data to create useful information and possibly organizational knowledge? What information will you create (be specific, providing examples)?What data/information will you share with the Marketing and/or Customer Service teams?Customer Relationship Management (CRM) systems contain a Sales component called Opportunity Management. What are some potential opportunities you might identify using this facility in conjunction with the data warehouse?

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Marketing TeamDefine your marketing strategy.Brainstorm about the data you would like to collect (be precise) to execute your marketing strategy. How will you obtain the data?How will you combine the data to create useful information and possibly organizational knowledge? What information will you create (be specific, providing examples)?What data/information will you share with the Sales and/or Customer Service teams?Customer Relationship Management (CRM) systems contain a Marketing component called Cross-selling and Up-selling. What are some potential cross-selling and up-selling opportunities you might identify and use in your Marketing campaign using this facility in conjunction with the data warehouse?

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Customer Service TeamWhat does customer service mean to Harrahs?Define your Customer Service strategy.Brainstorm about the data you would like to collect to execute your customer service strategy. How will you obtain the data?How will you combine the data to create useful information and possibly organizational knowledge? What information will you create?What data/information will you share with the Sales and/or Marketing teams?Customer Relationship Management (CRM) systems contain a Customer Service component called Contact Center where customer service representatives answer customer inquiries and respond to problems.What are some potential opportunities this facility provides that will support your Customer Service strategy. What data and/or information might you obtain and use from these contacts?

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The concept of Data Mining is becomingincreasingly popular as a business information management tool where it is expected to reveal knowledge structures that can guide decisions in conditions of limited certainty. Today increasingly more companies acknowledge the value of this new opportunity and use data mining tools and solutions that help optimizing their operations and increase customers bottom line.

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