"This program was an awesome, concise, introduction into the world of data analytics for non-profit fundraising. It definitely will cause your perspective to change for the better." Colin Allum, Manager, Data & Analytics, National Urban League.
"Fundraising programs at the Baruch College School of Public Affairs have increased my technical competencies in fundraising, producing greater results for the clients I serve." Michael Taylor, Managing Director, LAPA Fundraising.
Two days. 16 hours. This is an on-campus program.
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2. Register here online
Analytics and its Use in Fundraising
Students explore the overall analytics umbrella, its uses, and how it fits into a data driven fundraising organization. Exploration includes discerning which analytics projects serve as the next best step. Exercises include developing a request for bids from vendors and developing new strategy from sample nonprofit dashboards.
Discussion and exercises review how data is cleansed, examined, and massaged for good analysis. This section both prepares for the following sections and helps students understand the statistical methods of working with data. Exercises include basic data examination, followed by work on exploratory statistics to understand the range of information available for study. A plan will be written for an annual giving leadership campaign.
Topics include several methods for testing whether certain behaviors and attributes are related to giving, including a demonstration on social media sentiment. Topics such as correlation and chi square testing will be demonstrated, with time for students to measure which indicators are related to constituent engagement. Outcomes from exercises include plans for better constituent engagement.
Discussion and demonstration will show how to use the linear regression method to score prospects. This module will use the prepared and tested data from the exploration and significance testing modules. Students will conduct a modeling study, resulting in a scoring equation for their prospect pool (if they use their own data). Interpreting and using results will also be covered.
Again using prepared and tested data, students will walk through a sophisticated probability model, called logistic regression, to understand the characteristics of donors vs. non donors. Exercises will include conducting a logistic regression study and interpreting results. Outcome will include a plan for acquiring new donors.
Demonstration and exercises will be offered on showing the results from exploratory statistics, significance testing, and modeling. Best charting techniques are shown, along with using graphics to tell the story.
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