During 2021, 2.791 of our total donor list got inactivated. With the Predictive Data Model for Loyalty Actions, we aim to establish a data projection model that will help us identify the behavior trends of our donors. This will allow us to track and retain donors that are likely to reactivate or even increase donor reactivation of those already inactive. The data prediction model is based on historical data and statistical algorithms to predict future scenarios with the smallest possible margin of error.
To achieve our goal, we rely on two tools that provide us with quantitative and qualitative information. On one hand we have Salesforce, the platform we currently use to manage the data from our donors. On the other hand, we conducted a study to generate archetypes and a donor's journey in order to have more qualitative information to feed our predictive model. The results of the archetype study showed us six type of donors and their motivations.
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SOS Children´s Villages Colombia
Remember to be user-centric. Think about your user’s needs/problems you are trying to address.
With the Predictive Data Model for Loyalty Actions, we would analyze quantitative data, identify patterns and with such, identify correlations with qualitative data. This way, we may identify motives for donors to reactivate their donations. This will allow us to focus our actions to retain donors who might become inactive or even increase reactivation of those who became inactive for the same reasons.
By analyzing such data, our target audience will give recognition to the use of artificial intelligence in our organization, which is not often applied in the NGO sector. This will encourage donor participation in our community, meaning optimization of resources and increasing donor’s lifetime value in our organization. This way, SOS Children´s Villages Colombia will be able to keep individual donors as our main and most valuable income.
What type of data and insight did you use for this analysis?
The target audience for the Predictive Data Model for Loyalty Actions are the inactive donors and recently reactivated donors. since it looks to identify their behavior through the analysis of quantitative and qualitative data, in order to establish different correlation variables that lead us to identify the behavioral trends of each donor.During 2021, various compliance indicators showed us that we need to identify the behavior trends of our donors in order to take quick action against some situations:
Identify donors who are susceptible to reactivation because during the 2021 we presented almost 3.000 /three thousand/ (2.791) donors that got inactivated.
The donors who got inactivated represent 14% of the active donors during 2021. Taking this into account, we want to prevent and reduce that, understanding their behavior through the Big Data.
Reactivation / Saving
My organization (NPO)
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Challenge C: New channels, new tools. Increasing relevance and long-term value when engaging with our supporters
My solution will match the following innovation challenge(s) in Cluster 3
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C1: How can we use new technologies to improve donor engagement by delivering more effective segmentation and personalised journeys? How might we gather, store and interpret supporter data to create a better experience for a higher-value segment?