Sometimes we need to reflect and re-evaluate why we do what we do. For me, as a fundraising data analyst, I find it extremely useful to regularly go back to basics when prioritising my work. A well structured report can be a powerful weapon to prove a point or hammer home an idea. The trick is to keep things relevant and useful.
My dabblings in the database are not simply to feed my curiosity. I don’t produce reports to act, as I have heard others suggest, as a sort of carrot and stick to coerce those fundraisers into doing what they are told. I ain’t the data entry police. So why is it important that I do what I do? Why is data analysis so important in a fundraising environment? To answer this we have to remember the key factor that data analysis is a means to an end. Sounds a bit negative but I don’t see it that way. We have to remember it is all about the business of raising money for good causes. If the analysis is not firmly connected with that drive then it is not relevant.
For me, data analysis has its biggest business impact in the 3 main areas of planning, measuring performance and targeting activity. I realise of course that this is not an exhaustive list. For now I’ll say something about Planning.
Seems obvious but much of the process of deciding what to fundraise for and how much we can expect to bring in is done on operational necessity and instinct alone. If you mention feasibility studies or project management, unless you are lucky, you are in danger of being treated with a level of contempt usually reserved for criminals and traffic wardens, or so it seems.
I’m not suggesting that working on experience is a bad thing but somewhere along the line there will be someone relying on that money arriving in the quantities you have promised and on time. HE institutions for example need to consider what it is appropriate to fund out of budget and what it is appropriate to raise new money for.
How analysis of data can help this process at the basic level is to:
- describe effectively what has gone before.
- describe what can realistically be achieved under similar conditions.
- describe the level of investment needed to achieve this.
Remove the blindfold and get a true picture of what you have!
This involves looking at the capacity and level of engagement of the constituent database as a whole. It also involves looking at the donor journey from initial cultivation to gift. How long does that take and who needs to be involved? Knowing how often that process can be repeated and what quantities of gift can be expected can lead you to an achievable and realistic target.
The overarching theory is that we all want to make good decisions. We want to foster an environment where sound judgement is applied through all aspects of our work. We can do this if we are adequately informed.
I remind myself that I do what I do because we need an analytics programme to help us make decisions that change the way we work for the better.