Data Mining Project: Need to determine whether to build skills in-house or outsource.

Not a great deal of detail about the warranty service project, so my guess should specify assumptions about the project.Next , as we consider the existing staff structure at Retro, 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.

Please see my notes below. I am struggling with this because the information is very generic. Any help would be appreciated.

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Organizing a Data Mining Project
To do:
? Retro currently doesn't have enough staff members with the right skills to support a data mining project.
? Need to determine whether to build skills in-house or outsource. Even if Retro should outsource (as it did for the direct mailing campaign using the expertise of Apex Decision Support, Inc.), trained staff will still be needed to manage and maintain the data mining project on an ongoing basis.
Several executive board members raised concerns that such issues should be addressed at the outset of the data mining project rather than after the project is complete (or nearly complete).
E-mail from Malachi Alaka, Retro's corporate attorney, that raises a variety of concerns about data mining. His note, in part, says:
? I know the more information we gather, the better the analysis, but when we put a lot of information in one place, I get worried. Not that we have anything to hide, mind you. So far, the information in the data warehouse has been customer- and not product-specific. But if we're putting information on the quality of our products in a data warehouse, I'm worried that if something goes wrong, that information will be used against us.
? If we mine data on vendors to determine ones to weed out, I'm afraid we may be accused of making decisions based on unethical factors, such as race, sex, or even location.
It seems that every day, increasing numbers of corporations are facing ethical and privacy issues regarding the use of data. You know that collecting and especially publicizing data that customers feel should be private can severely damage a company's public image.
Others at Retro have brought up national and international legal issues that affect the collection of data. You know the executive board will want to be sure that proper procedures are in place to protect the company from any problems associated with data mining.

Background:
Limited information
Company Overview:
Retro's product line consists of a mid-sized sedan (Impression), a minivan (Express), a small sport utility vehicle (Altitude), and?the most recent addition?a sporty coupe (Vortex) available with a hard or soft top. The company's first vehicle, the Impression, was extremely successful. Sales were high, and Retro developed valuable brand equity.
Over the past few years, however, a variety of trends and issues have arisen that Retro's executive board is particularly concerned about. Sales for Retro's Impression model have leveled off, and although sales for all Retro vehicles have increased over the past years, the sales numbers have not met the company's projections.
Project Scenario
Retro Motor Company blends the classic look of the past with the technology of today.
Established in 1991, the St. Louis-based manufacturer is well known in the automotive industry for the quality and ingenuity of its designs.
In 1993, Retro unveiled its Impression vehicle, and three other popular models soon followed: Express, Altitude, and Vortex.
You were recently hired as manager of analytical services for Retro.
Working with an outside data mining consulting firm, you have analyzed processes and problems in various departments. Your goal has been to ensure that Retro operates more efficiently, maintains quality, and increases sales.
Retro's executive board is pleased with your results and wants you to apply data mining techniques to three other challenges facing the company.
First, many customers are unhappy with Retro's platinum warranty service.
Second, Retro's advertising campaign has not reached the customer market and has not increased sales as expected.
Third, Retro wants to create predefined options packages for its car buyers.
You will evaluate how data mining could help Retro address the first problem. Then you'll determine how to manage this data mining project, including its staffing needs and ethical, privacy, and legal issues.
Using the findings of Apex consulting, you will also interpret the results and potential solutions of data mining techniques as applied to Retro's three problems: the warranty service problem, the customer market problem, and the options package problem.
In addition, you will assess the infrastructure changes needed to support these data mining projects.
Finally, you will present and justify your findings and a data mining implementation plan to Retro's executive board.
As manager of analytical services, you will demonstrate how data mining can help Retro stay competitive and do business more intelligently.
My role
In one of your recent data mining projects for Retro's marketing department, I worked with an outside data mining consulting firm (Apex Decision Support, Inc.) to help determine the most profitable group for a direct mailing campaign. The costs of the campaign, including the consulting fees, were lower than previous campaigns, but the response rate was higher. Pleased with the results, the marketing department and the company's executive board want me to use data mining to help analyze the other business problems across the company's departments.
Memo from Jack Holsey, a member of the executive board and director of business operations at Retro (and the person I directly report to), sends me an e-mail message about these problems and what he expects from you and your data mining capabilities.

To: Manager of analytical services
From: Jack Holsey
Subject: Your New Assignments

Retro faces a variety of challenges if it is going to compete successfully in the new millennium. We need to learn more about our customers and how to better satisfy them. After being in business for more than a decade, we have a great deal of customer and operations data, but we need to improve our efforts at turning it into useful information that can help us make better decisions. I know that you and others in your department are exploring the use of data mining to enhance analysis of our data.
Members of the executive board have identified problems that data mining might help solve but are largely unfamiliar with data mining techniques. We'd like you to
? evaluate how data mining could help Retro address these problems
? assess the staffing, management, and infrastructure needs of potential data mining projects
? help us interpret the results of various data mining techniques so we can find optimal solutions to our problems
? make recommendations to our executive board so that Retro improves its decision-making
Specifically, I want you to help with three key problems facing Retro Motors:
? The service division needs help in understanding why many customers are unhappy with our platinum warranty service program.
? The marketing department needs assistance in figuring out why customers in the market are not responding to a nationwide advertising piece.
? Retro wants to determine the best predefined options packages for customers purchasing new vehicles.
Problem: Look at the problem of dissatisfied platinum warranty customers.
Brian Nichols, Retro's service division manager, is very concerned about this. Customer response card and e-mail response data, as well as service attendant anecdotes, indicate that many people who have purchased the platinum warranty for their cars are unhappy with this service option.
You have customer data (including age, address, date of purchase, and model purchased) for the 540,000 purchasers of the platinum warranty. In addition, you have warranty satisfaction levels for the 295,000 people who sent back the customer response cards and responded to service e-mails. You also have Retro's automotive service data (type, cost, length of time for service or repair). You have lots of data about these platinum customers, but what really separates the satisfied ones from the unsatisfied ones?
Further discussion with Jack Holsey, Retro's director of business operations, reveals that Brian Nichols is not familiar with data mining. You decide that a well targeted memo to Brian will be a good starting point for this data mining project