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Better Donor Engagement Through Cluster Analysis


... Cluster Analysis Agglomerative Hierarchical clustering algorithm starts with each point as a cluster and recursively joins together nearest clusters based on the ... – PowerPoint PPT presentation

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Title: Better Donor Engagement Through Cluster Analysis

  • Better Donor Engagement Through Cluster Analysis

  • Introduction/Challenges
  • What is the problem we are trying to solve?
  • Our Starting Point
  • Our Approach
  • Alliances
  • Data
  • Technique
  • Our Learnings
  • Next Steps
  • Recommendations

The United Way of Greater Cincinnati
  • Serve communities in 10 counties in Southwest
    Ohio, Northern Kentucky and Southeast Indiana
  • Over 100,000 donors, advocates and volunteers
  • Annually over 50.3M invested in the community

  • Flat Annual Workplace Campaign
  • Aging donor base

Opportunity Better Engagement With Our
Our Starting Point
  • Lots of data little actionable information
  • Minimal data scrubbing expertise
  • Minimal statistical expertise
  • Minimal tool set for analytics or visualization
  • Existing Workplace Analyses based on macro level
  • Raised over Goal
  • 3 5 year giving trends
  • /donor
  • Participation

What Is Driving These Results?
Desired Engagement Approach
  • Understand underlying constituent behavior
  • Provides enlightenment on why the macro level
    results occur
  • Interact with constituents by targeting unique
    behavioral characteristics
  • Improves
  • Donations
  • Advocacy
  • Volunteerism

Our Approach
  • Find a partner with expertise willing to teach us
  • University of Cincinnati Department of
    Operations, Business Analytics and Information
  • Sponsored 2 Graduate Level Students Master
  • Began Fall 2013

Problem statement To use Descriptive Analytics
for UWGCs Individual Constituents to describe
our current state numerically and visually,
further develop segmentation, correlate
variables, and build the basis for predictive
experiments with a focus on Long-term Retention
Our Data
  • Individual level data for 2006 2012
  • No Personally Identifiable Information (PII)
  • Demographic
  • Pledge
  • Volunteer Activity
  • Recognitions
  • Affinity Groups
  • Event Attendance
  • Frequent Meeting with students and professors
  • Lots of data scrubbing!

Donor Cluster Analysis
  • Used a Hierarchical Clustering Technique
  • Based on a set of independent variables
  • Does not force the user to specify the number of
    resulting clusters up front
  • Run the clustering model, look at the results,
    refine parameters, repeat until results make
    practical sense

Cluster Analysis
  • Agglomerative Hierarchical clustering algorithm
    starts with each point as a cluster and
    recursively joins together nearest clusters based
    on the least distance measure until there is only
    one cluster.
  • We divide the resultant tree formed by this
    recursive agglomeration based on statistical
    measures and look for homogenous clusters and
    their properties.
  • Dataset Clustering Output

Initial Variable Creation
  • Began with a data pool of 14 variables
  • Examples
  • Acquisition Rate
  • Volunteer Participation Rate
  • Average Contribution
  • Average Event Attendance

Initial Correlation Analysis
Final Correlation Analysis
After variable reduction to reduce
multi-collinearity 6 variables remain
Final Variables
  • Correlation analysis determined there were 4
    independent variables that could be used in the
    clustering model
  • Volunteer Participation
  • Churn Rate
  • Influencers (Active Contributors Who Registered
    for 10 or More Events)
  • Average Contribution

Refinement Of The Cluster Analysis
  • UC Students provided their R code to us used to
    perform variable correlation and cluster analysis
  • R is an open source statistical programming
  • Analogous to SAS or SPSS
  • We modified the data input to include only
    individuals from our top 200 accounts
  • Accounts that our Resource Development
    Professionals focus on
  • Reran the clustering analysis process

Initial Cluster Analysis Output
Number of Clusters - Visual Approach
Final Cluster Analysis Output
Cluster Analysis Numerical Output
Final Cluster Analysis Informing Strategy
  • Final Output Generated 7 Clusters 3 of Which
    Had Low Average Contribution Rates
  • 25 Companies With Very High Churn Rate, Minimal
    Influence And Lowest Average Contribution
  • 55 Companies Low Average Contribution, Average
    Churn, Minimal Influencers and Low Volunteerism
  • 63 Companies With Low Churn Rate, Minimal
    Influencers, Low Volunteerism and Low Average

Final Cluster Analysis Informing Strategy
  • Characteristics of the Other 4 Clusters
  • 10 Companies High Volunteer, Average Churn and
    Average Contribution
  • 12 Companies With Strong Mix of Influencers and
    Average Contribution (Largest Overall Workplace
  • 30 Companies With Low Volunteer Rate and Very Low
    Churn Rate and High Average Contribution
  • 4 Companies with Low Churn, Highest Volunteer
    Rate, Strong Influencers and Highest Average

How UWGC Is Using The Results
  • Now we have a characterization of individual
    behaviors at our Top 200 Accounts
  • Using that characterization to formulate account
    specific engagement plans for 2015 campaign
  • Capitalize on strengths
  • Address areas of opportunities
  • Assess results after the 2015 campaign

Concurrent Activities
  • Tool selection
  • R for analytics
  • Tableau for visualization
  • Training
  • Local Meetups
  • Business Intelligence
  • Data Analytics
  • R

Recommendations For You
  • Unless you have strong statistical modeling, data
    analytics or business intelligence capabilities
    in house
  • Corporate alliance
  • Academic alliance
  • Other United Ways (Contact Me)
  • Tools Choose Wisely
  • Strongly consider tools used by your alliance
  • What are local companies using?
  • Training
  • Meetups (
  • On-line courses (Coursera
  • Swirl (

Contact Information
  • 513-762-7102

(No Transcript)