What is Your Nonprofit's Strawberry Pop-Tart?

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Nearly a decade ago, Walmart wanted to understand what happened in their stores when a major storm came through town. The question they wanted to answer was which items would customers want in a time of need.

They analyzed petabytes of data and something popped in results: Pop-Tarts. Specifically, strawberry Pop-Tarts. As it turned out, sales of strawberry Pop-Tarts increased seven times their normal sales rate ahead of a hurricane.

You could have asked store managers or associates the same question and it is highly unlikely that any of them would have said strawberry Pop-Tarts. Their answers were likely to be anecdotal in nature and we should know by now that the plural of anecdote is not data.

Data Science Non-Fiction

Fast forward to today and the ability to answer valuable questions from the analysis of data has become the new normal. This is no longer science fiction. Continued advances in artificial intelligence, machine learning, and prescriptive analytics allow organizations to make meaning out of mountains of data.

Knowing what is possible leads to a simple question that every nonprofit should be asking: What is the strawberry Pop-Tart hiding in our data?

Perhaps it’s what makes some donors more generous than others. It might be what loyal members have in common. There could be indicators that help prioritize prospects for a capital campaign gift. It may illuminate which attributes lead to better email response rates. What do first-time $1,000 donors have in common in our data?

Data, analytics, and artificial intelligence can help nonprofits of all sizes and missions find their own strawberry Pop-Tarts. They can identify patterns and signals from all the noise to create value for their organizations.

Start with the Right Questions

But how do nonprofits make meaning out of all their data? How can they understand what works and what still needs improvement? How do they find their very own strawberry Pop-Tart hidden amongst all their data? There is a temptation to start using all the available technology, but this is likely to result in some frustration.

The best place to start any data exploration is to focus on asking the right questions — not choosing the technology to answer them. What problem are you trying to solve? What value are you trying to create? How will you measure success? Who benefits get getting the answer right — or wrong?

Starting with the right questions often leads to more questions, but that’s OK. This is a journey and we are just at the beginning.

Turning Insights into Action

The next step is to take the right questions and using them to turn insights into action. Yes, this will require some analysis, elbow grease, and data know-how. Thankfully, there is some helpful research to help point nonprofits in the right direction.

Target Analytics, a division of Blackbaud, looked at the giving patterns of more than 5 million donors and identified insights into what influenced them making their first $1,000 gift to a nonprofit. These were existing donors to more than 100 nonprofit organizations. As it turns out, how donors made their first gift and the number of years they have been giving has a significant influence on when they make their first $1,000 donation.

First, the research looked into the source of the first gift these future $1,000 donors made to the organization. 48% of these donors made their first gift through direct mail, 29% through telemarketing, and 10% through special events. Only 5% of first gifts were attributed to social media. Your own nonprofit’s first gift sources may be different, but at least know you know where to start looking. Knowing this can help nonprofits focus their efforts and prioritize certain engagement channels.

Second, the other thing that popped in the data was how many years of prior giving happened before the first $1,000 gift was made. 14% of $1,000 donors made that gift after seven years of prior giving. That jumped to 21% in year eight for the donors in the research study. It does not take a lot of data science wizardry to run your own analysis on current $1,000 donors and the year they made those gifts. Finding a pattern can help you to take action on the information.

Finally, even with this research we need to be careful not to confuse correlation with causation. Will nearly half of your donors be acquired through direct mail and almost a quarter of them make a $1,000 gift in their eight year of giving? Not necessarily. The findings are more descriptive than predictive. But now you’re thinking like a data driven nonprofit — which is a good sign.

The nonprofit sector has a tremendous opportunity to use data and analytics to help accelerate the change we all want to see in the world. Technology is no longer a limiting factor and it can allow social good organizations to drive improved performance and results. Start by asking the right questions and then focus on turning those answers into actions.

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