What You Need to Know about Predictive Analytics for Fund Raising
If you’re in the fund raising business you know that every day is a new day to connect with potential donors, partners and others that can donate money to your non-profit. Whether you’re dialing for dollars, tapping into previous donors, attending networking functions, implementing email campaigns or any number of other fund raising strategies, you know that it’s a massive undertaking. Not to mention, that you are most likely competing with other non-profits for those charitable dollars.
Now imagine that you can take your existing database, match them to people who have never donated to achieve increased donations by identifying those most likely to donate.
Well you can, by using predictive analytics!
Predictive Analytics (which includes Data Mining, Statistics and Predictive Modeling) has now been established as the most important analytical tool non-profits can use to increase their fund raising base and annual fund raising dollars. It is in fact one of the top 10 applications of data science in the world.
Using predictive analytics, a non-profit’s historical campaign database can be analyzed, refined and prepared for predictive modeling algorithms that can identify the significant characteristics that successfully differentiate non-donors from donors and also past non-donors who will become future donors. Armed with this information, your campaigns and your time can be focused on those audiences that are more likely to donate.
If fact, one of the remarkable outcomes reported from the application of predictive analytics is the fact that people who have never donated to a non-profit charity before donate significant amounts of money - $10,000 or more - when targeted in a marketing campaign driven by data science.
Uncovering hidden value (in this case, non-donors who have a previously unknown or unrecognized propensity to be donors) is a hallmark of predictive analytics.
Using automated modeling methods such as SAS Predictive Modeler and IBM’s SPSS Modeler, these studies can be undertaken for a reasonable expenditure of marketing dollars and will pay back the implementation costs by a factor of 5 or 10 to 1 or higher.
Much of traditional research looks in the rear view mirror or at what were the outcomes of a campaign in order to strategize an approach to drive a different outcome. With predictive analytics, it’s a move away from looking in the rearview mirror to looking forward with the ability to use historical data, to “predict” a future outcome based on certain behaviors.
In essence, predictive analytics turn uncertainty about the future into usable probability.
Therefore, by applying predictive analytics to a fund raising campaign helps focus efforts on those most likely to become donors, as well as by identifying those that most likely will not, regardless of how much time you spend knocking on their door.
So as you begin planning your fund raising campaigns, consider the implementation of predictive analytics and focus your efforts on generating the biggest ROI for your campaign investment.
Don’t ask yourself “if a non-profit should use predictive analytics. Ask yourself, why you haven’t done so yet!”
Also, if you’re curious as to how predictive analytics and data mining can be used in other situations, we’ve provided an example below:
Increase sales by identifying cross-sell opportunities
Improve customer retention by predicting likelihood of cancellation
Personalize marketing messages by predicting likely interests
Focus user interfaces on high value activities by predicting what a customer might want to do next
Improve customer acquisition plans by predicting potential lifetime value of prospective customers
Reduce fraud by predicting the likelihood that someone is not who they say they are
Reduce campaign costs by predicting non-responders and eliminating them from the campaign
Improve risk management by predicting the riskiness of a customer or of a product offer
Identify the right candidate for the right position for high-value C-Suite positions