DonorIQ offers an all-in-one solution, handling everything from hosting your data to developing custom models and providing automated reports. This means you get actionable insights quickly and seamlessly.

Our models dive deep into individual donor behaviors—looking at how each person interacts with your campaigns—to predict their likelihood to contribute. This personalized approach is far more accurate than relying on broad demographic profiles, which often assume "lookalikes" will behave the same way.

We analyze hundreds of variables, including every solicitation and donation record, combined with external demographic data, to create complex profiles of your potential donors. This allows for a nuanced understanding of each individual.

Our models are built by a team of PhD data scientists who bring advanced techniques from a variety of industries to maximize the value of your data.

We use advanced fuzzy-matching algorithms to uniquely identify individuals across multiple data sources, ensuring a complete and accurate donor history for each person.

DonorIQ can process and score massive datasets rapidly, allowing you near real-time insights into your campaigns.

We price our services based on the measurable value we provide, using clearly defined testing methods developed in collaboration with our clients. This ensures you only pay for results that positively impact your fundraising efforts.

Our solution is designed for easy integration, with minimal disruption to your existing processes. Since we handle the data and systems, you can focus on what you do best—fundraising.

We’re confident in our solution and offer head-to-head testing against any competitors you’re currently using, so you can see the difference DonorIQ makes.

Most traditional solutions only address parts of the problem, requiring you to stitch together various services, which can slow down your process and leave gaps in your data.

Traditional methods often use canned models or co-op data, assuming similar donors will act the same based on surface-level demographics, which can lead to missed opportunities and inaccurate predictions.

Traditional methods tend to rely heavily on Recency, Frequency, and Monetary (RFM) metrics, focusing only on past donation history while ignoring critical behavioral data. This limited scope often results in less effective targeting.

Traditional approaches often involve lower-level analysts performing basic segmentations, leaving much of the potential insight in your data untapped.

Traditional methods often struggle to match individuals across different files due to reliance on standard address verification, which can miss crucial connections and result in incomplete data.

Scoring with traditional methods is often slow and cumbersome, making it difficult to integrate into your regular production process, causing delays and inefficiencies.

Traditional services often come with a fixed price, regardless of the actual value they deliver, which can make it hard to justify the cost when results are uncertain.

Traditional methods can be difficult to implement and often don’t integrate smoothly with your existing systems, leading to frustrating delays and a disjointed workflow.

Most traditional providers don’t offer head-to-head testing, making it hard to compare their effectiveness with other solutions.