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Artificial Intelligence/Machine Learning for fundraising

  • 1.  Artificial Intelligence/Machine Learning for fundraising

    Posted 6 days ago
    Has anyone tried to investigate and/or implement the use of Artificial Intelligence or Machine Learning in the fundraising space?
    Do you use any software to analyze your contact reports and proposals to predict donor behavior?
     

    We are trying to gather information on what data would be required for a successful AI implementation.

    Thank you!


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    Jasmine Dsouza
    Advancement Services
    Temple University
    jasmine.dsouza@temple.edu
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  • 2.  RE: Artificial Intelligence/Machine Learning for fundraising

    Posted 6 days ago
    There are a few ways to use AI in fundraising, I recently spoke about it at a conference. They include:
    1. to write marketing copy
    2. to dedupe and otherwise maintain data integrity in your database
    3. to identify prospects and do modeling
    4. to automate gift processing and other types of document capture and automation
    5. To write code in languages like SQL, R and Python that empower non-coders to create analysis and reports that they understand conceptually, but cannot program themselves
    One of the most compelling use-cases for AI is as an interface, I believe. Nevertheless, there are quite a few firms offering AI and ML for fundraising in the manner that you're suggesting too. 



    Thank you,
    Isaac Shalev
    Data Strategy Expert
    Sage70, Inc.
    (917) 859-0151
    isaac@sage70.com

    Schedule a 30-minute consultation now:






  • 3.  RE: Artificial Intelligence/Machine Learning for fundraising

    Posted 5 days ago
    Hi Jasmine,

    The short answer is, AI and ML have piqued the curiosity of the education space but much of the work the institutions are doing is in areas such as predicting admissions etc. In the fundraising space, most major prospect research tools claim to use AI + ML to predict/score prospects, and some CRM platforms have canned algorithms for predictions but in this case, the end-user has little control over the algorithms and techniques used by the vendors. Besides, the accuracy of the predictions suffers because the approach here is to apply a one-size-fits-all type solution to everyone's data set.

    I am working with some organizations where they feel limited by the AI offered by the tools and are venturing into performing their own analysis and machine modeling. Each has been a custom analysis project with its own dataset and involves applying suitable models to determine the prospect strength, understanding the donor base better, and predict donor behavior (to guide solicitation, donation amount etc.)

    Creating a software tool to analyze contact reports and proposals to predict donor behavior is like creating one drug to cure all illnesses: essentially impossible. A skilled data analyst will take a look at the data and apply suitable cleaning and modeling techniques based on the data. Therefore this becomes a data-centric activity.

    Regarding your question on what data will be required for successful AI implementation: in general, the more information relative to your prospects/donors and their activities you have, the better the accuracy of your predictions. There is definitely an optimal ratio of the number of records versus the variety of information on the donors, but a skilled machine learning analyst will be able to massage the available data to increase the accuracy of the predictions. 

    If you can elaborate on what your AI goals are and where you are in your journey, then I would be happy to further clarify. 

    P.S. Another way fundraisers use AI is to use natural language processing to interface with donors and prospects during events or on an ongoing basis. This is a different game from machine learning and predictions, but it's an area that is rapidly developing. Hope this helps.

    Thank you,

    Medha

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    Medha Nanal
    CEO & Principal, Top Cloud Consulting
    (Data, Systems, Analytics)
    medhananal@topcloudconsult.com
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