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Dating Mining and Implementation

Next, as we consider the existing staff structure at Retro, we will need to identify what areas could potentially support data mining. Might also consider what services are available from consultants, and not to get stuck in the details (number of personnel, total costs, etc). Jack Holsey (and ultimately the executive board) is looking for a general overview of staffing options, not a detailed cost breakdown.

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Please see response attached for better formatting and hyperlinks (also presented below), including one related article. It is interesting that no staff details were given, so I guess we need to work with what was provided. I hope this helps and take care.

1. Do you think data mining should be implemented using Retro employees and/or consultants?

Using data mining to analyze the staff structure -

It seems, that data mining would be a viable option for Retro to seriously consider. However, since Jack Holsey does not know much about data mining, it would be important to explain the rationale behind this decision, such as...
Data mining, also known as knowledge-discovery in databases (KDD), is the practice of automatically searching large stores of data for patterns. To do this, data mining uses computational techniques from Statistics and Pattern recognition. Specifically, (KDD) process consists of data selection, data cleaning, data transformation and reduction, mining, interpretation and evaluation, and finally incorporation of the mined "knowledge" with the larger decision making process. http://research.microsoft.com/dmx/DataMining/default.aspx This could be part of the discovery process for the potential staffing options.

In fact, you could inform Jack Holsey that data mining has already been demonstrated to be a useful tool in the decision making process (http://research.microsoft.com/~surajitc/), for many organizations.

For illustrative purposes, 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 a correlation between that customer and silk shirts. The sales department will look at that information and begin direct mail marketing of silk shirts to that customer. In this case, the data mining system used by the retail store discovered new information about the customer that was previously unknown to the company.

Likewise, Retro could use data mining to determine its staffing needs, as well as to address the three customer's issues (i.e., dissatisfied customers, etc.). In addition it could track the purchases and responses of a customer (both positives and negative responses - such as the problem of dissatisfied platinum warranty customers). For example, if Retro wanted to predict potential sales (i.e., predictability) some data-mining vendors use predictability of associations or sequences to mean the same as confidence. Prevalence can also be very useful as a measure of how often the collection of items in an association occur together as a percentage of all the transactions. For example, "In 2% of the purchases at the Retro, both a vehicle and warranty were bought."

Services available from data mining software:

a. Data Mining Software and Staffing

Dating Mining: A hot buzzword for a class of database applications that look for hidden patterns in a group of data. For example, data mining software can help retail companies find staff and customers with common interests, find commonalities and differences across staff and customers, and determine what staff and customer's needs are. The term dating mining is commonly misused to describe software that presents data in new ways.

Staffing. Data-mining software is not a replacement for skilled analysts. The algorithms used in data mining are quite powerful. If the users do not understand these issues or have access to knowledgeable staff, the results can be disastrous. The staff also needs to be committed to the effort. While the level of labor required to run a data-mining capability will vary with a number of factors, data mining is not a do-once-and-then-forget activity. Learning is incremental and environments (internal and external) change; therefore learning is a constant and ongoing process and so is data mining. The ongoing use of a data-mining capability requires business analysis and data analysis skills. Your analysis of the staff will determine whether you need to outsource of not (i.e., if none of the staff have these essential skills, outsourcing my be the best option).

Data mining is a capability consisting of the hardware, software, "warmware" (skilled labor) and data to support the recognition of previously unknown but potentially useful relationships. It supports the transformation of data to information, knowledge and wisdom, a cycle that should take place in every organization. Companies are now using this capability to understand more about their customers, to design targeted sales and marketing campaigns, to predict what and how frequently customers will buy products, and to spot trends in customer preferences that lead to new product development. It is important to note that data mining is not software alone, as some vendors would have clients believe. While software may play an important role, it is only in the context of clear objectives and careful thinking from business management along with the skill of the analyst that data mining ultimately leads to a success story rather than an embarrassing and costly failure.

True data mining software doesn't just change the presentation, but actually discovers previously unknown relationships among the data. Thus, data mining would be extremely helpful for Retro - in finding unknown relationships for the following:

a. Why the customers are unhappy with the platinum service program
b. Why customers in the market are not responding to a nationwide advertising piece
c. What the customer thinks the best predefined options packages for customers purchasing new vehicles

Armed with this information, Retro can make informed decisions for staff needs based on customer needs.

The following article may be helpful in the final decision of the best software package for Retro:

What Is Data Mining and What Are Its Uses?

If you're looking for great relationships within your customer base, this is the place to begin.

BY MARK L. LABOVITZ

Data mining is a capability consisting of the hardware, software, "warmware" (skilled labor) and data to support the recognition of previously unknown but potentially useful relationships. It supports the transformation of data to information, knowledge and wisdom, a cycle that should take place in every organization. Companies are now using this capability to understand more about their customers, to design targeted sales and marketing campaigns, to predict what and how frequently customers will buy products, and to spot trends in customer preferences that lead to new product development. It is important to note that data mining is not software alone, as some vendors would have clients believe. While software may play an important role, it is only in the context of clear objectives and careful thinking from business management along with the skill of the analyst that data mining ultimately leads to a success story rather than an embarrassing and costly failure.

Important Data-Mining Concepts

Any data-mining capability starts and ends with the business objectives and a design of the learning environment. A learning environment is a specific plan of discovery, which includes an operational or analysis-action plan to answer questions raised by pursuit of the business objectives. A partial list of questions commonly found in learning plans includes:

· Who are my customers and what do they look like? (profiling)
· Who are my most valuable customers? (determining customers' valuations, relationship discovery)
· What products or services are my ...

Solution Summary

This solution explains data mining techniques and implementation as applied to the case scenario of Retro employees and/or consultants. Supplemented with one highly informative article.

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