Bibliographic record
Abstract
This paper explores the evolving strategies behind fundraising and marketing campaigns in the context of higher education and non-profit organisations. It offers an in-depth look at how personalised approaches, relationship marketing and data-driven strategies contribute to long-term donor engagement and sustainable financial growth. The author’s experience ranges from working with an international cosmetic brand to leading annual giving campaigns at the University of Montreal (UdeM), where they achieved record-breaking results in donor mobilisation and revenue generation. The goal of this paper is to provide readers with practical advice on how to create and manage successful fundraising campaigns across multiple channels, focusing on the importance of segmentation, personalisation and strategic planning. It also addresses the challenges and opportunities of engaging donors in the digital age, with a focus on the role of social media engagement and return on investment (ROI) tracking. Readers will gain a better understanding of how to structure annual giving programmes, optimise donor retention and use data to refine campaign strategies. In addition, this paper offers an overview of how to lead teams, set objectives and drive organisational growth through effective philanthropy and fundraising initiatives. By the end, readers will have the tools and knowledge to apply multichannel strategies successfully and achieve measurable results in their own campaigns. This article is also included in The Business & Management Collection which can be accessed at https://hstalks.com/business/.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.011 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".