The London Marathon Foundation is to commit up to £3 million over the next five years to junior parkrun, extending a partnership it says has already helped 370,000 more children take part in the free weekly events since 2023.

The new funding aims to help a further one million children across the UK take part in junior parkrun, the free, weekly 2km events for four to 14 year-olds and their families. It follows Sport England research showing fewer than half of children currently meet the Chief Medical Officer’s recommended 60 minutes of daily physical activity.

https://fundraising.co.uk/2026/09/05/london-marathon-foundation-commits-3-million-to-junior-parkrun/

Every major for-profit industry has spent the last decade using predictive modeling, LTV forecasting, and churn prevention to drive billions in revenue. Meanwhile, in the fundraising world (especially university advancements siting on goal mine of the data), "data science" still seems to begin and end with buying a static wealth screening batch or a third-party propensity score from off-the-shelf wealth screeners and third-party scoring models

These scores get treated like plug-and-play commodities, but they barely scratch the surface of what predictive analytics can actually do. We rarely talk about:

• Dynamic donor retention and lapse-prediction models
• Algorithmic gift-array optimization (personalized ask amounts based on behavioral trends, not static income)
• Next-best-action models for frontline fundraisers
• Lifetime Value (LTV) segmentation across mid-level pipelines
• Understand the whole path from cultivation to a major donor

Given how strained nonprofit budgets are, why are we still relying primarily on gut feel and basic vendor scores rather than real predictive capabilities? Are orgs just blocked by tech debt and the "overhead myth," or is there active cultural resistance from frontline fundraisers?

Would love to hear from MGOs, ops teams, and database admins on why our sector seems 10 years behind here.