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VeraData Reduces Donor Acquisition Costs by 24% at National Children's Cancer Society

Alteryx Alumni (Retired)
Created

VeraData Reduces Donor Acquisition Costs by 24% at National Children's Cancer Society 

Author: Michael Peterman, CEO In-2CRev-28px-R.png

Company: VeraData

 

Use Case Overview: 

 

We provide deep analytics services for hundreds of clients. Of particular interest is the NCCS (National Childrens Cancer Society). This prestigious and highly respected organization has been doing more for the families of children with cancer since 1987 - yep, for almost 30 years. We are honored to be serving them as a client.

 

Describe the problem you needed to solve:

NCCS, like every other large charity in America, sends out direct mail fundraising solicitations to support these families. Like any other business has to spend money to acquire new customers, non-profit organizations spend money to acquire donors. They were faced with a year over year trend of increasing donor acquisition costs and increasing costs to reactivate lapsed donors. This was coupled with a concern was that there was a shrinking universe of potential donors who were willing to support their efforts.

 

Describe the working solution:

Enter VeraData. Our initial engagement with NCCS was to build a donor acquisition model to reduce their costs to acquire donors, which subsequently reduces the cycle time to break-even on the investment in new donors. Concurrently, we developed a lapsed reactivation model that used tons of external, outside information to select from their audience of former donors the individuals most likely to donate again, therefore increasing the universe of marketable names while maintaining the cost to reactivate. Lastly, our third component was to uncover an expanded universe of individuals who had the propensity to support the NCCS. This meant identifying new data sets and determining which individuals would be profitable to pursue.

 

There were several methodologies deployed to achieve these goals. Our analytic team settled on a series of support vector machine models solving for response rate, donation amount, package and channel preferences, etc. All of the information in our arsenal was called upon to contribute to the final suite of algorithms used to identify the best audience. Using Alteryx, R, Tableau and our internal machine learning infrastructure, we were able to combine decades worth of client side data with decades worth of external data and output a blended master analytic database that accounted for full promotional and transactional history with all corresponding composite data on the individuals. This symphony achieved all of the goals, and then some.

 

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Describe the benefits you have achieved:

The client experienced a 24% reduction in their cost to acquire a donor, they were able to reactivate a much larger than anticipated volume of lapsed donors (some were inactive for over 15 years) and they discovered an entirely new set of list sources that are delivering a cost to acquire in line with their budget requirements. Mission accomplished.

 

Since that point, we have broadened the scope of our engagement and are solving for other things such as digital fundraising, mid-level and major donors. Wouldn't have been possible to do with the same speed and precision had we not been using Alteryx.

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