Hitting the Bullseye on Cause Marketing with Predictive Analytics
Customer Company Size
SME
Region
- America
Country
- United States
Product
- DataRobot Managed AI Cloud
Tech Stack
- Amazon Web Services (AWS)
- Predictive Analytics
Implementation Scale
- Enterprise-wide Deployment
Impact Metrics
- Revenue Growth
- Productivity Improvements
Technology Category
- Analytics & Modeling - Predictive Analytics
Applicable Functions
- Sales & Marketing
Use Cases
- Predictive Replenishment
Services
- Data Science Services
About The Customer
DonorBureau is a small company that has been helping hundreds of nonprofits, fundraising organizations, and agencies maximize their return on investment from their fundraising campaigns since its inception in 2011. Using predictive analytics, DonorBureau provides modeling and segmentation services aimed at improving the efficiency of fundraising appeals. Their proven models help predict how receptive a prospect will be to an organization, with a specific appeal, at a specific time of the year and frequency of contact.
The Challenge
DonorBureau, a small company that provides modeling and segmentation services to nonprofits, was facing the challenge of providing more effective and accurate predictive models to differentiate itself in a competitive market. The company was dealing with over 900 million mail transactions, 140 million donations, and over 40 million individuals, and the predictive modeling demands were mounting. Ideally, they would like to have a large team of data scientists on staff, but those are coveted positions that come at a premium. Building and deploying predictive analytics is time-consuming, budget-breaking, and for the layperson, challenging to implement and maintain.
The Solution
To overcome their challenge, DonorBureau partnered with DataRobot who provided an automated, highly accurate, fast, and cost-effective Enterprise AI solution, powered by Amazon Web Services (AWS). The powerful algorithms in the DataRobot Managed AI Cloud offering allowed DonorBureau to automatically generate far more accurate models in a fraction of the time. The benefits were immediate and continuous. The team quickly experienced a 10% improvement in accuracy right out of the box, with no fine-tuning and a total cost of ownership amounting to just 25% of their previous expense.
Operational Impact
Quantitative Benefit
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