Let me step away from fundraising and share a story from life outside of work. My family has recently moved to Halifax, Nova Scotia where we have easy access to the ocean and all it has to offer. My daughter always liked the beach and the ocean, so getting there on the weekend to enjoy the waves sits near the top of weekly goals. So each week, I sift through the data and visualizations to predict whether we’ll head to the beach and if so, what is the best day and time. Judgement definitely comes into the equation, along with experience and outside data such as weather:
WAVE FORECASTS
And logistics like how long it’ll take to get to the different spots, relative crowds etc.
SUMMARY
This collection of data visuals plus my daughter’s priorities and the experiences I have from prior weekends form the foundation for how I’ll “pitch” the weekend activities for the family. Mix all of that together and more, and I figure out whether to hype an afternoon at the beach or start looking for an alternative to distract from the fact we won’t go this weekend. In this particular case, I know she’ll takes waves over weather, so Saturday evening at the beach wins, even with the rain.
However, I don’t go to my daughter and say, “examining the wave forecast shows a 75% increase in wave size along with an additional star for quality. Combine that with a reasonable drive to an accessible beach and you should see 37% better July 13th year over year.” Instead, I start with the conclusion: on Saturday afternoon we are going to boogie board until the ocean spits us out.
That is it. That is all that matters and all she wants to hear. If she has questions, I’ve got the data ready, but most likely she’ll bring new data to the conversation. For example, she may mention plans to go to a movie with a friend that same afternoon. No problem, I know the second best day and time would be Sunday morning, so then we discuss if we can do both within the weekend. In other words, we start to solve problems, not a new cycle of analysis.
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