Taking Royal Flying Doctor Service Victoria to new heights
Tim Paris – 28 September 2020
RFDS Victoria increased net revenue in their 2020 Tax Appeal by nearly $35,000 by using Dataro’s propensity scores. RFDS Victoria used the predictive scores to remove the least likely givers from their mailing list and to add an extra 1000 high probability donors. This saved over $9000 in mailing costs and netted an extra $26,834 in donations, including 14 gifts over $500 and 1 major gift.
- $34,491 increase in net revenue
- 14 extra gifts >$500
- 1 extra gift >$5000
- 13.8x ROI on investment with Dataro
Donors ranked by Dataro as more likely to give in fact gave the vast majority of campaign revenue.
How We Did It
Dataro used machine learning to generate ‘propensity scores’ for all RFDS Vic donors. This method is much more powerful than old approaches like RFM analysis and accurately models how likely each donor is to respond to the campaign. For instance, a donor with a score of 0.5 is roughly 50% likely to give. Using this information, RFDS Vic was able to easily remove the least likely givers from Wave 2 of the appeal, saving on mailing costs. This was a great strategy as they were still able to contact everyone in Wave 1, but saved costs in Wave 2. RFDS Victoria was also able to ‘add in’ higher probability donors that Dataro had found elsewhere in their database.
By combining these two uses for Dataro’s donor scores – reducing campaign size while adding more likely givers – RFDS Victoria was able to achieve an optimal campaign list and net a higher return.
Dataro’s propensity scores accurately predicted actual response rates in the appeal. This graph shows how Dataro’s predictive score closely matched the real world response rate.
What they said
“We tested Dataro's propensity scores in our 2020 Tax Appeal and the results speak for themselves. We were able to raise more funds and reduce our costs. We're now rolling out Dataro's propensity scores across all of our appeals and regular giving programs.”
Data & Insights Specialist
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