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Case Exercise: YMCA “Hallowine” Silent Auction

The Windward YMCA in Kailua, HI holds an annual fundraising event every Halloween called Hallow-wine. Businesses and individuals donate items to offer at a silent auction. There is also a team that goes out to solicit donations(usually from businesses) for the auction or get cash donations. The objective is to raise as many funds from the event as possible. The silent auction raises money from the sale of donated items and increases ticket sales to attend Hallow-wine. Due to limited space and management overhead, they can only offer a limited number of items at the auction. Items that do not get bid on at the auction are given away, given back to the donors, or thrown out (i.e. no salvage value). However, generally, a donor is willing to give a cash donation of approximately 40% of the value of the item in lieu of donating the item. Executive Director Bill Hastings has a spreadsheet of items for the auction in the previous year but is unsure how to make use of this data to help improve the management of the auction and fundraising results this year. His goals are to make the acution more profitable, higher attendence of the event, and reduce overhead/waste of donated items and opportunities to obtain cash donations.

Learning Objectives

• Experience a full life cycle business analytics process and its application to addressing real-world business decision making.
• Review some basic descriptive and inferential statistics
• Experience using “good enough” models useful for practical business decision making
• Determine useful decision models and descriptive and prescriptive analytics to support decision making

Analytics

To descriptive models of categorical and numerical data to determine the products that are the most profitable and/or the most likely to be sold, and overall to improve the decision process of the auction.

Statistics Needed

• Frequency tables and charts
• Percentiles graph
• Pareto chart
• Expected value
• Measures of central tendency, variability
• Oulier identification
• Data tables
• Distribution summary tables and charts
• Scatter plots
• Correlation matrix
• Hypothesis testing T-test, Anova, ChiSquare, Binomial

Data Source

Hallo-Wine auction items 2009.xlsx

Tasks:

Business Analytics Problem-Solving Process

Answer the questions below. Use the 6 steps of the business analytics process as a framework for performing the analysis and then write a report to recommend using useful results for actionable decision making.

Step 1. Recognizing the problem

• What is the gap between what happened previously and what is wanted now?
• What background knowledge is needed?
• How can you describe past performance?
• What problems from the previous auction need to be addressed for the upcoming auction?

Step 2. Defining the problem

• What decisions need to be made?
• What are the factors involved in making the decisions?
• What questions do I need to ask to address to make the decisions?
• What would sufficient answers to these questions look like?
• Describe the data that is available (define the variables, kind of data, type of data). Do you have variables for each of the factors in the decisions to be made? What, if anything, is missing?

Step 3. Structuring the problem

• What is the decision criteria or strategy for the decisions to be made? (goals, constraints, etc.)
• How do answers to the questions relate to the decision?
• What data analysis need to be made to answer the questions?
• Is data is needed and is it practical to obtain?
• Is the data provided sufficient to answer the questions (kind of data, amount, quality, etc.)?

Step 4. Analyzing the problem

• What models and techniques are needed to address the questions?
• What is the best representation to plot a distribution?
• What are the assumptions we need to make?
• Are the assumptions reasonable? How will they affect our models?

Step 5. Interpreting Results and Making a Decision

• How do we incorporate how confident can we be in our results?
• What key assumptions were made and how does the degree we think they are valid affect these results?
• What cautionary considerations must be communicated?

Step 6. Implementing the solution

• How will the analysis results be used to make the decisions?
• What resources or limitations do we need to consider?

Questions to Answer:

1. What is the problem(s) Bill is trying to solve?

2. With the given data, what are key metrics to look for to solve this problem? Explain why those metrics are important.

3. How confident can you be that your chosen metrics are significant to addressing Bill’s problem?

4. Give a brief explanation of any exceptions or events that may cause your solution to be less effective.

5. State at least one action Bill can take for this year’s Hallo-wine auction, that is based on last year’s data, and will increase the amount of money raised.

Report

Write a professional report (as if you were a hired consultant or employee) for the director of YMCA. The report should be to the point and give specific, actionable advice or solutions based on the data and analytics. Avoid technical aspects and terms that are non-essential and any speculations not substantiated by the data. This report should be concise without lengthy explanations being necessary to understand it. Avoid “educating” and lengthy explanations of what or why. You do not need to detail how you performed any analytics, data manipulation or other subjects that Bill probably wouldn’t be interested in.

There is no min or max page limit as charts and tables can take up a highly variable amount of space. However, any charts or tables included need to be understandable to a layman at first glance (labeled and captioned if needed).

The particular models you use, interpretations, and advice given are your choice and you should be prepared to explain or defend this if needed!

Use this as an outline for the report:

A. Description of the business problem

• What is Bill Hastings trying to accomplish? e.g. Reduce left-over auction items.
• How will you decide what items to auction versus take cash value?
• Why are your findings important?
• What questions will be answered and how do these explicitly help address the decisions? e.g. Which products are the most profitable and/or the most likely to be sold.

B. Data, methods, and models and results

• Discuss the basic approach used to analyze the data and any concerns about the integrity and quality of the data used. e.g. “Some item categories do not have enough data in the last year to make conclusive decisions.”
• Briefly describe the models used (formulas, tables, graphs) indicating what they are used for (try to avoid technical details) e.g. “This model allows us to be x% confident in accepting items for auction of y category.”
• Provide detailed answers to the decision questions using the models
• Indicate all “important” assumptions made and why you think they are reasonable. An assumption is important if: you need it to get a result e.g. “Last years data was taken accurately with no fraudulent data.” or, if wrong or invalid, would significantly affect your results. e.g. the assumption “Last year’s data and items were representative of an average year and not outliers”. If wrong or invalid, would significantly affect what items would be acceptable for donations.
• Do not list technical assumptions used for statistical analysis e.g. “We assume the data is Normally distributed.”

C. Decision making

• Explain specifically how you used the models and results to make your decisions. This may be literal results such as “We will never accept items from x categories because they are better used for cash value.”
• Detail special considerations or issues to watch out for e.g. “If there is an influx of items from category x than we may need to accept cash value for y number of items.”
• Describe how the effects from using the results for making the decisions can be measured or observed. i.e. How can the executives know that accepting certain items for auction will be sold?