💡 This article is part of our Guide to Donation Forms (Giving Experiences)
Smarter Suggestions, Backed by Real Data 📊
Givecloud’s Automatic setting for suggested amounts is designed to present the ideal giving options to your supporters—maximizing average donation size without sacrificing conversion rates. Unlike competitor systems that rely solely on predictive tools, Givecloud’s algorithm is continuously refined by our product team through rigorous A/B testing and ongoing data analysis, including market segmentation, device type, geography, and real donor feedback.
The result? A finely tuned system proven to increase average donation amounts by up to 60% compared to standard form layouts. 🚀
At its core, Givecloud’s donation suggestion algorithm has two powerful layers working together to meet donors exactly where they are:
For returning donors, if their most recent gift was over $75, Givecloud uses it as a baseline to generate a set of suggested amounts based on fixed multipliers:
➤ 0.5× (half the last gift rounded up to the nearest $5)
➤ 0.75× (three-quarters of the last gift rounded up to the nearest $5)
➤ 1.0× (equal to their last gift)
➤ 2.0× (double rounded up to the nearest $100 )
➤ 4.0× (quadruple rounded up to the nearest $100)
These tailored options help donors give generously—grounded in their own past behavior, but without added pressure. 💸For new donors, Givecloud draws on subtle cues from their device—like type, brand, model, and operating system—to generate a smart, context-aware suggestion. Even without giving history, the experience still feels personal and intuitive. 💡
When enabled, this feature transforms a static donation form into an intelligent, donor-aware experience—inviting supporters into a moment that feels thoughtful, empowering, and emotionally resonant. Because giving isn’t just about money—it’s about meaning. ❤️
Data that is not used includes: IP address, geography and browser type. These datapoints were studied but determined to be poor indicators of donation amount optimization due to both the inaccuracy of the data and the existence of far stronger correlations.