In our recent Water Cooler Chat, Greg Duke, Partner for Database Services, shared the Monte Carlo technique – a way of understanding what the likely range of results would be for a given scenario. In our blog post today, we expand on the concept of using this forecasting technique to take care of three different fundraising needs.
Will We Meet the Campaign Goal?
We worked with our first client who, rather than asking us how to meet a campaign goal set from a needs assessment, asked us to set a campaign goal based on what could be raised. We used Monte Carlo as part of our work to determine the best possible campaign goal based on past giving results. To do the work, we not only conducted an overall Monte Carlo analysis, but we also found ways to boost giving by examining the client’s separate solicitation activities.
We were able to demonstrate an achievable goal for the organization, in addition to providing extra tips on boosting current giving and campaign giving through our several studies.
What Can Annual Giving Raise in this Economy?
The recent earthquake wreaked on our world economy by the pandemic threw our fundraising efforts into turmoil. Not only did we as fundraising staff have to start doing our jobs very differently, but the job loss/Great Resignation compounded with the new stock market crash has us scrambling to identify prospects and ask amounts. Is there a way to promise our unsettled leadership an annual giving goal that can be reached? Yes.
Using economic data, combined with our giving data, can be added to the regression equation that we would build for our Monte Carlo analysis. Then we can adjust as needed to manage expectations (I used to call that the MGN or “Marianne Gets Nervous” measure to avoid being expected to raise just above the highest possible result), and present our findings.
Can We Reduce Gift Officer Portfolio Size Without Missing Anyone?
Clients often have a small group of gift officers whose portfolios are overloaded, all over the (literal) map, and heavy on qualification prospects. Research works diligently to qualify wealth and giving, but whether a prospect would take a visit is a question belonging to statistical modeling. Meanwhile, travelling to Alaska is a fun venture, but not necessarily feasible if there is only one $100,000 prospect living there.
Instead, Monte Carlo can use the prospect’s geographic location (even if you use latitude and longitude to accommodate for prospects outside of your country) to determine likely returns for gift officer travel. But understanding what each gift officer can adjust his or her work and travel plans to maximize results.
How does it work? Using the same modeling technique to derive the initial equation, Monte Carlo can be used to move the “what-if” needle back and forth on locations to see what the best possible Major Gifts solicitation efforts can yield.
So, how does one get started doing these three exercises? First, watch Greg’s video here. Second, try the techniques using a variety of different data models. What do you find? Try modeling last year’s results based on data from before the year to see if you could have forecasted your results well. Then let us know about your project. We love talking about projects like this.
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