Keep Or Clean Out?
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When your modeling data are refreshed, should you keep multiple years of scores or only work with what’s most current?
I’ve gotten the same question from a lot of repeat clients lately who use our modeling scores (like Major Giving Likelihood, Target Gift Range, Principal Giving Solution, etc.). It’s a good question on its own, and it’s been on my mind to ask how your offices have considered it.
They’re asking this…
If we have scores from our last round of modeling in our primary database, should we keep those? Or should we clean them out?
Most often clients tell me they hope to find meaningful patterns in the donors whose giving increased or to identify some other actionable insight to improve their strategies. I appreciate the thought and intention of this question because data itself doesn’t raise money. Doing things with it makes the difference, and this question points to an action-oriented idea.
However…
I almost always vote for “out with the old, in with the new.”
First, there’s a technical reason. Target Analytics scores are rooted in statistical analysis, and year-to-year comparison can be tricky. Variables and data used in the modeling process likely shift over time—either because the client supplies, redefines, or changes the data they share with us or because we add and update data from the public space to make the process more robust. Without controlling the variables involved, determining why the scores changed—cause and effect—can be murky.
Second, reality sets in. To do lists are long in development offices, and I haven’t met a client that actually has time to tackle a project like this. They are excited about it, they want to do it, but there’s always a proposal to write, an introduction to find, a list to get to the mail house—and there should be. If it’s a question of forward progress or looking back at old scores, I don’t think there’s a question of where time is better spent.
So I typically recommend clients do the following:
They’re asking this…
If we have scores from our last round of modeling in our primary database, should we keep those? Or should we clean them out?
Most often clients tell me they hope to find meaningful patterns in the donors whose giving increased or to identify some other actionable insight to improve their strategies. I appreciate the thought and intention of this question because data itself doesn’t raise money. Doing things with it makes the difference, and this question points to an action-oriented idea.
However…
I almost always vote for “out with the old, in with the new.”
First, there’s a technical reason. Target Analytics scores are rooted in statistical analysis, and year-to-year comparison can be tricky. Variables and data used in the modeling process likely shift over time—either because the client supplies, redefines, or changes the data they share with us or because we add and update data from the public space to make the process more robust. Without controlling the variables involved, determining why the scores changed—cause and effect—can be murky.
Second, reality sets in. To do lists are long in development offices, and I haven’t met a client that actually has time to tackle a project like this. They are excited about it, they want to do it, but there’s always a proposal to write, an introduction to find, a list to get to the mail house—and there should be. If it’s a question of forward progress or looking back at old scores, I don’t think there’s a question of where time is better spent.
So I typically recommend clients do the following:
- Export the details they want archived from their primary database and securely save the file so the older information isn’t lost entirely
- Determine if they want all new modeling scores in their primary database, if it makes sense to include a segment of high priority scores, or if they prefer modeling data exist in ResearchPoint only (assuming RP is active in the office)
- Scrub older data before importing new scores, or use “link and sync” between Raiser’s Edge and ResearchPoint to update details
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ARCHIVED | Blackbaud Target Analytics® Tips and Tricks
07/18/2017 11:35am EDT
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