Bibliographic record
Abstract
At the University of Waterloo (UW), the Annual Giving programmes aim to scale fundraising efforts while providing a personalised experience for donors. By analysing collected data, institutions can gain insights into donors’ interests, enhancing engagement and philanthropic contributions. Limited resources, however, often hinder the effective implementation of mass personalisation. This paper explores the challenges and opportunities faced by the UW in improving response rates for bulk appeals, such as Giving Tuesday, Renewal and Short Lapsed campaigns. The traditional ‘Last Gift’ segmentation method, while straightforward, is cumbersome and relies heavily on manual data preparation. To address these issues, we developed a prototype donor affinity model (DAM) that leverages donor data to predict and align fundraising efforts with donor interests. This model aims to enhance the effectiveness of our annual giving programmes by moving beyond last-gift analysis to a more comprehensive understanding of donor behaviour. This paper focuses on improving response rates for bulk appeals, starting with Giving Tuesday, using a donor affinity approach. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".