3 simple steps to conduct predictive analytics on a shoestring budget

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Greg Warner is CEO and Founder of MarketSmart, a revolutionary marketing software and services firm that helps nonprofits raise more for less. In 2012 Greg coined the phrase “Engagement Fundraising” to encapsulate his breakthrough fundraising formula for achieving extraordinary results. Using their own innovative strategies and technologies, MarketSmart helps fundraisers around the world zero in on the donors most ready to support their organizations and institutions with major and legacy gifts.

Predictive analytical modeling can help you understand and predict who might likely become a major or planned gift donor for your nonprofit fundraising efforts.
It’s powerful. In fact, if you are considering this avenue, I highly recommend my friends at Blackbaud Target Analytics (Katherine Swank and Lawrence Henze). They are both experts in the field.
But, most organizations can’t afford sophisticated predictive analytical modeling
So I thought I’d give you 3 simple steps to do it on a shoestring budget.

  1. Pull a list of all of your donors and donations
  2. Identify the top 20% of your donors (they probably provide about 80% of your organization’s revenue)
  3. Analyze those donors to uncover their similar characteristics

So there you have it! There’s your predictive analytical model. Now, as new donors come in and continue to give, use your model to try to determine who among them have the same characteristics as your current top 20%.
I didn’t say it would be easy. Just simple.
BONUS:
Usually, what you’ll want to identify will fall into three categories (behavior, demographics, and responses to marketing). Here are some things to look for as you analyze your donors:

  • BEHAVIOR- Do they participate in events or not? Do they “like” your Facebook page? Do they share your videos?
  • MARKETING – Transactional data, such as number of gifts per year, the size of gifts and frequency of giving, etc.
  • DEMOGRAPHICS – Average age, level of education, etc.

 

Recommendations

>> Are You Measuring the Right Performance Metrics?
>> Predictive Program Evaluation: Don’t React, Predict
>> How Big Data Is Changing Philanthropy
 

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