STAUPELL ANALYTICS GROUP - ANALYTICS EXPERTS FOR NONPROFITS, IMPROVING FUNDRAISING
  • Home
  • About
    • Staupell Team
    • Testimonials
    • Partnerships >
      • Prospect Research Institute
      • Lityx
      • TouchPoints
      • Gravyty
  • Services
    • Fundraising Analytics
    • Prospect Development
    • Business Intelligence
    • Database Administration
    • Fundraising Optimization Solution
  • Training
    • Analytics Machine Learning Artificial Intelligence
    • Business Intelligence Visualization Reporting
    • Prospect Research and Management
    • Webinars
    • Classes >
      • Beginner Analytics Using R
      • Analytics Classes
      • Skill Builder Series
    • Workbooks
  • Blog
  • Events
    • Water Cooler Chats
    • Video Replays
  • Contact
  • Product

Driven by Data Blog

Using Giving Variables in Data Mining

2/15/2016

1 Comment

 
Picture
In fundraising, we seek charitable gifts. When we model prospects, we hope to find those who will give at a certain level. Because of that, we often debate whether it is valid to use a prospect’s giving history in a major gifts model, since doing so might be what we call, “co-linear”, or comparing oranges to oranges, so to speak.
There are two reasons to use giving variables in a prospect model. First, unlike any other part of a fundraising shop, gift records are audited. That means that they are the cleanest and most consistent of all the data you would use. Second, past behavior often indicates future behavior – past giving can show the pattern of future giving. Using giving history judiciously can improve your major gifts model well.
Let’s take a look at a few variables related to giving that many modelers use to distinguish donors from invested donors.

  • Loyalty variables include the number of years given in the past 5 or 10, the number of years giving consecutive up to this year (consecutive years giving), and the number of years giving since the prospect became involved with your organization.
  • Recency variables include last gift date and average giving over the last 5 years.
  • Frequency variables include number of gifts and pledge payments over a period of time, percentage mix of new pledges vs. outright gifts, and whether a prospect has her gift deducted from a credit card on a monthly basis.
  • Method variables include the campaign code, tender type, honorarium/memorial flag, or whether the prospect gave through a corporation rather than from personal funds.
  • Movement variables include an up or down score (compare the number of years the prospect has increased giving vs. the number of years that he decreased or maintained giving) and a velocity measurement (giving this year divided by average giving over the previous 3 years).
  • Time lapse variables include distance from record creation date to first gift date, distance from first membership date to first charitable gift date, distance from first gift date to highest gift date, and distance from first contact to first major gift date.
  • Giving totals variables include dividing the donor’s life giving by the number of years in the database, average giving over the last 3 or 5 years, and percentage of highest gift to total life giving.
 
These are some of the ways that one can look at giving in order to find major gifts prospects. Note that any model that tries to predict giving this year still can not include independent variables that have giving this year in them. Be careful to distinguish the models. Feel free to try out some of these variables as a way of looking at current major gift donors as well.
 
Be sure to tweet us what you find @Staupell.
This blog post appeared in the APRA Upstate New York newsletter in the winter of 2010.
1 Comment
Mia Evans link
11/17/2021 09:09:27 pm

Thanks for helping me understand that giving variables in data mining will help get the cleanest and most consistent data you need, and understand the pattern for future use. With that in mind, companies would benefit from investing in this kind of service for their progress. I can imagine how understanding every bit of detail will help the company cater and provide more products or services that their clients would want for better impressions and experiences.

Reply



Leave a Reply.

    Keep Informed
    Sign up for
    notifications when a
    new post comes out

    Sign Up Now


    Authors

    Marianne Pelletier has more than 30 years of experience in fundraising, with the majority in prospect research and prospecting.

    Greg Duke helps Raiser’s Edge clients to optimize their database by implementing data clean-up techniques and creating reporting structures, including dashboards and SQL queries.  He also facilitates data imports into Raiser’s Edge and database administration.

    Categories

    All
    Advancement Svcs
    Annual Giving
    Artificial Intelligence
    Assessment
    Big Data
    Blackbaud
    Branding
    Dashboards
    Databases
    Data Management
    Data Mining
    Data Prep
    Dependent Variables
    Donor Modeling
    Efficiency
    Engagement
    GDPR
    Giving Variables
    Linear Regression
    Machine Learning
    Major Gifts
    NFT
    Participation
    Productivity
    Project Planning
    Prospecting
    Prospect Research
    Push Technology
    Raiser's Edge
    RE NXT
    Reporting
    Research Pride
    RFM
    Statistics

    Archives

    March 2023
    February 2023
    January 2023
    December 2022
    October 2022
    September 2022
    August 2022
    July 2022
    June 2022
    May 2022
    April 2022
    March 2022
    February 2022
    January 2022
    March 2021
    September 2020
    June 2020
    May 2020
    March 2020
    February 2020
    July 2019
    May 2019
    March 2019
    December 2018
    September 2018
    May 2018
    March 2018
    September 2017
    June 2017
    March 2017
    January 2017
    December 2016
    September 2016
    June 2016
    April 2016
    March 2016
    February 2016
    January 2016
    December 2015

    View my profile on LinkedIn
Picture
© COPYRIGHT 2023 Staupell Analytics Group. ALL RIGHTS RESERVED.
  • Home
  • About
    • Staupell Team
    • Testimonials
    • Partnerships >
      • Prospect Research Institute
      • Lityx
      • TouchPoints
      • Gravyty
  • Services
    • Fundraising Analytics
    • Prospect Development
    • Business Intelligence
    • Database Administration
    • Fundraising Optimization Solution
  • Training
    • Analytics Machine Learning Artificial Intelligence
    • Business Intelligence Visualization Reporting
    • Prospect Research and Management
    • Webinars
    • Classes >
      • Beginner Analytics Using R
      • Analytics Classes
      • Skill Builder Series
    • Workbooks
  • Blog
  • Events
    • Water Cooler Chats
    • Video Replays
  • Contact
  • Product