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Remember to be user-centric. Think about your user’s needs/problems you are trying to address.
Thanks to the revolution of digital fundraising, organizations today gather a very high amount of potential leads every year through their campaigns. Many fundraisers would love to convert existing leads into donors using direct mail or telemarketing.
However, since leads haven’t donated yet they can’t be segmented with models such as RFM. As a result, understanding the most potential leads to include in a new appeal becomes really difficult. Fundraisers that wish to convert their current leads, have to make guesses or extract random leads lists for their conversion appeals.
Because of how hard it is to understand which leads are ready to convert, issues arise hurting both the NPO and the potential donor. On the fundraiser side, redemption rates get really low (usually below 0.5% according to a series of interviews conducted with a panel of large NPOs based in Italy) and unsustainable. As for the donors, they might hear from the charity too early in the relationship. This could stress them and make them perceive the organization as too pushy.
COVID-19 has further exacerbated the issue of untapped lead pools. The drastic reduction in F2F due to COVID left many organizations deprived of a key source of new donors and revenue. In this scenario, understanding how to better convert online leads into donors, becomes an even more important task for NPOs.
Please describe your proposed solution and indicate how this is improving the alternatives, if there are. Who will be willing to pay for this solution?
The solution
To help organizations fully unleash the revenue potential of their online leads pool, we developed GiveRank. GiveRank is a cutting-edge artificial intelligence that allows NGOs to dramatically increase the redemption of lead conversion appeals by analyzing leads online behavior and predicting which leads have the highest chance of becoming donors.
How does it work
When leads interact with an organization they leave traces that often get recorded within the NGO’s CRM. This data includes email opens, topics of the email read, web pages visited, and much more. It also includes very important information: whether any of these leads eventually converted into donors at some point in time. We can call this ‘digital behavior data’.
GiveRank's powerful AI engine analyzes years of lead’s behavior data within the organization’s CRM. It learns the complex behavioral patterns that characterize leads that eventually turned into donors. For instance, visiting an annual report and opening the newsletter early in the morning might be a part of the pattern that predicts a future donation.
To make its predictions, GiveRank uses a combination of machine learning algorithms created by our data science team at W-Mind. Our engineers were able to develop such a system of algorithms thanks to more than three years of work on non- profits datasets.
What else can it do
What’s more, GiveRank can further increase the redemption rate by indicating the Donor Journey* that will have the highest conversion on new leads in terms of digital touchpoints, topics, content, and much more. Giverank can also indicate different optimal donor journeys for different kinds of leads*, according to their unique behavior. This provides potential donors with a more personalized and rewarding experience. It also further increases the redemption rate.
Lastly, GiveRank can be set to predict both potential one-off donors, regular or even future middle donors within a lead pool. When enough years of data are present within the NGO it is possible to predict which leads will become middle donors even years after donating for the first time.
How big will the revenue be if your idea were to be implemented? Is your idea scalable? It your idea replicable in most of the countries?
Revenue potential
GiveRank is able to increase* the redemption rate of all appeals sent to leads that haven’t donated yet by a factor ranging from x3 to x12 times (compared to manual segmentation based on trial and error or random extraction). By implementing GiveRank across organizations, we could unleash millions of euros of new revenues from untapped lead pools. Fundraisers would have a way to supplement the acquisition gap brought by COVID and the disruption of F2F. They would also create a new sustainable source of revenue for the future.
Relationship with the donor
By reaching out to leads with a high chance of conversion, we can also improve the quality of the relationship between the NGO and new potential donors. Leads would receive a donation request from the organization only when their behavior tells us they are ready and happy to hear from the NGO.
Scalability and replicability
GiveRank it’s fully scalable, replicable and can be easily implemented throughout different organizations sizes, languages, cultures, and geographies. Our algorithm dynamically adapts to understand the unique behavior of local users.
Feasibility
GiveRank has already been developed and tested extensively. Tracking and storing digital behavior data is a requirement to implement GiveRank and we know that not all NPOs are at that stage yet. If some leads behavior has already been tracked by the NPO, GiveRank can be made fully operational in 3-4 months. If the organization has to start tracking leads behavior from scratch, GiveRank would need 10-12 months to be successfully implemented.
