Human Rights Campaign
“At ROI Solutions, we see our clients as partners. When a client faces a challenge, we are always ready to step in and are thrilled to partner with the Human Rights Campaign in this pivotal moment. We collaborated on implementing a custom MiLo machine learning model, and we’ve been very excited about its success.”
MiLo Team Lead
The Big Pivot:
Machine Learning Leads to Better Targeting
Human Rights Campaign (HRC) has worked for 40 years as the leading voice in the LGBTQ+ rights and equality movement. They spread their equality message through advocacy, advertising, and large-scale events, including Pride marches. Pride not only serves to galvanize the community but also helps drive supporters and donors to the organization.
When the pandemic struck in 2020, the organization was faced with a budgeting challenge knowing that Pride events would have to be postponed or canceled due to the societal impacts of the virus. They knew they had to pivot quickly and look for innovative approaches to better target and engage their constituents to help make up for an anticipated budget shortfall.
Building on a 20+ year partnership with ROI Solutions, the organization adopted MiLo Intelligence to help find the right constituents to help maximize their marketing and fundraising efforts.
About Human Rights Campaign:
The Human Rights Campaign envisions a world where every member of the LGBTQ+ family has the freedom to live their truth without fear and with equality under the law. Their mission empowers 3 million+ members and supporters to mobilize against attacks on the most marginalized people in our community.
For more about the Human Rights Campaign, visit hrc.org.
With ROI Solutions, the Human Rights Campaign went beyond traditional Cooperative Database models that focus on traditional Recency, Frequency, and Monetary Value (RFM) metrics to fuel model builds. These models are effective but limited because they do not analyze other important variables such as volunteerism, event attendance, and other unique organizational interactions. The result is that Coop models sometimes do not select and prioritize important audiences because they are not using those engagement variables during the build process.
Scored & Returned
The Lapsed and Deep Lapsed Model was tested against several internal and Coop models and performed exceptionally well. The response rate increased by 41%, while mail volume increased by over 38% compared to prior efforts. HRC’s net result was a 95% revenue lift on their annual lapsed recapture program.
In addition, the model identified responsive audiences in very deep lapsed audiences. While not the intent of the model, when someone (or a machine learning model like the one MiLo built) tells you where to look, you sometimes find the needle in the haystack.
We were thrilled to hear that MiLo found and selected a lapsed donor who last made a $100 donation over 13 years ago. She quickly responded with a $100 gift and a very generous $50,000 gift to the organization. Through the combination of sound operations and personalized communication and cultivation by HRC staff, the donor recently renewed with a $10,000 gift.