1

QBUS6600 Project 1 Outline: UNICEF Australia –

Predicting Response to Direct Mail Appeals

Background

UNICEF Australia is a dedicated children's charity committed to delivering lasting impact for

every child. It works in over 190 countries and territories to save children’s lives, to defend their

rights, and to help them fulfil their potential, from early childhood through adolescence.

To strengthen its vital programs, UNICEF Australia is continuously improving its fundraising

strategies through innovative campaigns, community engagement, and partnerships. By offering

various fundraising initiatives—such as charity events and digital marketing campaigns—it

enables individuals and organizations to contribute in meaningful ways. UNICEF Australia is

leveraging the use of data analytics to enhance propensity modelling, particularly by exploring

how external data sources can improve the predictive performance. This data-driven approach

enables more targeted and timely engagement with the appropriate audience, ultimately enhancing

supporter experience and optimising long-term support. The potential benefits include greater

marketing efficiency, leading to a huge impact on resources and aid delivered to children in need.

Problem Description

Use the available data (see ‘Data Description’ below) to build a propensity model for direct mail

(DM) appeals. The objective is to develop a model for predicting the likelihood of individuals or

organisations making a donation within the next three months in response to a direct mail appeal.

You can frame this task as a classification problem, where the goal is to predict whether an

individual/organisation will make an action within the next three months. The project presents a

unique opportunity to apply your data analytics skills to a real-world business challenge and

contribute to the ongoing success of UNICEF Australia. Your work will play a crucial role in

helping UNICEF Australia improve audience selection of their direct mail appeals and make

outreach more efficiently, making a positive impact on the lives of children globally.

In this project, you should:

• Conduct Exploratory Data Analysis (EDA) to identify the top features and attributes that

are likely to predict the future donation behaviour.

You should aim to find or reveal all relevant properties, characteristics, patterns, and

statistics hidden in the datasets.

• Develop a predictive model to forecast the likelihood of a donor making a donation over

the next three months in response to a direct mail appeal.

2

You can implement any statistical or machine learning approaches that you feel are

appropriate. Ensure that you justify the selection of your model and interpret the model in

terms of the key attributes for predicting the future donor behaviour. Use the F1-score to

evaluate the performance of your final model.

• Based on your analysis, outline a strategy to help UNICEF Australia improve audience

selection of their direct mail appeals, increase the response rate, and improve fundraising

efforts.

You should recommend a strategy for the UNICEF Australia team to execute, to take

advantage of the key insights that you have identified, and the models you have built and

validated. The strategy could include any enhancements and/or other interventions or

changes to direct marketing campaigns, backed by high-level cost estimates and

fundraising avenues accompanied by assumptions and/or supporting data.

Data Description

UNICEF Australia has provided you with their CRM data in multiple CSV files, including the

information on donation transactions, campaign details, and descriptive features of the donors,

such as address postdoc, donation type, and other relevant attributes.

You are required to utilize the existing CRM data and augment it with at least one third-party open- source data of your choice (e.g., Mosaic or ABS) to improve the accuracy of predictions.

UNICEF Australia has made efforts to ensure the data is relatively clean, however, we encourage

you to perform checks and conduct the necessary data processing and feature engineering. You are

also welcome to explore external datasets to enrich your analysis and feature engineering.

Useful Tips

Data Processing: Select and process the necessary CRM data files required for your analysis. Use

match keys to merge relevant datasets.

Train-Test Split: Implement a train-test split to validate your model's performance and prevent

overfitting.

Feature Engineering: Perform feature engineering to enhance model performance. Creating and

transforming features can uncover hidden patterns.

Experiment with Models: Test various machine learning models to find the most suitable one.

This experimentation is key to achieving high model performance.

51作业君版权所有