Question

The Stern School has a huge alumni base, but only recently has been working to engage them in a lifelong relationship with the school. It is not a good idea to bombard alumni with every possible fundraising opportunity. There are various different sorts of opportunities and engagements, and Stern wants to match them with the alumni for whom they seem to be best aligned. There are many positive advantages to such relationships; right now the School is interested specifically in increasing alumni giving. The Stern School administration has learned that you studied data mining for business analytics, and has asked you to help them assess a proposal from Blue Moon Consulting, to help increase alumni giving.

As a trial, Blue Moon has been asked to consider one fundraising engagement: the Undergraduate Scholarship Drive (the USD). Critique their proposal, below. Find the four most serious flaws in the proposal, and suggest how to rectify them. Your answer should comprise two sentences for each flaw: one stating the flaw, and one stating your suggestion for fixing it. You should accept as true any factual statement Blue Moon makes about what has happened in the past.

"We will mine the data from the prior USD campaign that was delivered to a random sample of 10,000 alumni. We propose to build a model to predict how much each alumnus will give, and then target those who will give the most. Stern has collected various data points on each alum, including demographic, geographic, major, year, interest, and first-job data, and stored it in the Alumni Database. We will use the amount donated as the target variable, and the data from the Alumni Database as the features. We will build a classification tree and a logistic regression from the data from the random campaign to estimate the amount donated. We will compare the models built based on the area under the ROC curve. The Stern administration has told us that they would like to target another 5000 alumni in the next test. The 5000 alumni with the highest area under the ROC curve will be targeted."

Answer 1

Blue Moon Consulting aims to help Stern School increase alumni giving by using data mining techniques to predict who might donate more and target them effectively.

Step-by-step explanation:

The Stern School is considering a proposal from Blue Moon Consulting aimed at boosting alumni giving by leveraging data mining techniques. The proposal involves the implementation of two predictive models: a classification tree and a logistic regression model. These models are designed to estimate the potential donation amounts based on historical data related to alumni.

The classification tree and logistic regression models are common tools in data mining for predicting outcomes based on various input variables. In this context, they would be utilized to analyze and understand the factors that contribute to higher alumni donations. The models work by identifying patterns and relationships within the existing alumni data, enabling the prediction of donation amounts for individual alumni.

The evaluation of the models' performance is gauged using the area under the Receiver Operating Characteristic (ROC) curve. The ROC curve is a graphical representation of the trade-off between sensitivity and specificity, providing insights into the models' ability to discriminate between those likely to donate and those less likely to do so.

The ultimate objective of the models is to target the top 5000 alumni who are predicted to contribute the most in future fundraising campaigns. By employing data mining techniques, the Stern School aims to enhance the effectiveness of its fundraising efforts by strategically identifying and prioritizing potential high-value donors. This approach aligns with the broader trend in leveraging data analytics to inform decision-making and optimize resource allocation in fundraising and development activities.