april 29 - may 1, 2015 better donor engagement through cluster analysis
TRANSCRIPT
April 29 - May 1, 2015
Better Donor Engagement Through Cluster Analysis
Agenda
• 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
Challenges
• Flat Annual Workplace Campaign
• Aging donor base
Opportunity: Better Engagement With Our Constituents
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 metrics
- % 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 Systems
• ‘Sponsored’ 2 Graduate Level Students – Master Thesis
• Began Fall 2013
Problem statement: To use Descriptive Analytics for UWGC’s 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
CORRELATION churn_rate acquisition_rate
vol_rate aff_rate reco_rate influencers avg_contrib avg_pledge avg_freq avg_evrgd avg_evses avg_evatt avg_evppl avg_evnumregd
churn_rate 1 0.668 -0.048 0.325 -0.155 -0.063 -0.08 -0.08 0.15 -0.131 -0.11 -0.195 -0.141 -0.107
acquisition_rate 0.668 1 -0.037 0.581 -0.149 -0.059 -0.11 -0.11 0.028 -0.127 -0.107 -0.184 -0.133 -0.095
vol_rate -0.048 -0.037 1 0.04 0.288 0.38 0.16 0.16 0.085 0.439 0.429 0.517 0.441 0.363
aff_rate 0.325 0.581 0.04 1 0.033 0.068 -0.04 -0.04 -0.082 0.072 0.07 0.064 0.073 0.036
reco_rate -0.155 -0.149 0.288 0.033 1 0.203 0.55 0.55 0.133 0.619 0.606 0.657 0.6 0.32
influencers -0.063 -0.059 0.38 0.068 0.203 1 0.09 0.09 0.077 0.271 0.271 0.296 0.282 0.182
avg_contrib -0.078 -0.111 0.157 -0.035 0.553 0.09 1 1 0.117 0.278 0.274 0.295 0.251 0.134
avg_pledge -0.078 -0.111 0.157 -0.035 0.553 0.09 1 1 0.117 0.278 0.274 0.295 0.251 0.134
avg_freq 0.15 0.028 0.085 -0.082 0.133 0.077 0.12 0.12 1 0.082 0.074 0.102 0.08 0.048
avg_evrgd -0.131 -0.127 0.439 0.072 0.619 0.271 0.28 0.28 0.082 1 0.995 0.883 0.988 0.564
avg_evses -0.11 -0.107 0.429 0.07 0.606 0.271 0.27 0.27 0.074 0.995 1 0.852 0.981 0.552
avg_evatt -0.195 -0.184 0.517 0.064 0.657 0.296 0.29 0.29 0.102 0.883 0.852 1 0.861 0.553
avg_evppl -0.141 -0.133 0.441 0.073 0.6 0.282 0.25 0.25 0.08 0.988 0.981 0.861 1 0.57
avg_evnumregd -0.107 -0.095 0.363 0.036 0.32 0.182 0.13 0.13 0.048 0.564 0.552 0.553 0.57 1
Final Correlation Analysis
CORRELATION churn_rate
vol_rate aff_rate influencers
avg_contrib
avg_freq
churn_rate 1 -0.048 0.325 -0.063 -0.08 0.15
vol_rate -0.048 1 0.04 0.38 0.16 0.085
aff_rate 0.325 0.04 1 0.068 -0.04 -0.082
influencers -0.063 0.38 0.068 1 0.09 0.077
avg_contrib -0.078 0.157 -0.035 0.09 1 0.117
avg_freq 0.15 0.085 -0.082 0.077 0.12 1
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 language
- 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
Cluster Avg Churn Rate Avg Volunteer Rate Average Influencers Avg Contribution # of Companies1 0.44 0.02 0.01 321.47$ 252 0.24 0.01 0.12 326.83$ 553 0.09 0.02 0.06 439.47$ 634 0.24 0.19 0.29 713.70$ 105 0.16 0.03 2.96 724.35$ 126 0.11 0.02 0.27 1,471.71$ 307 0.14 0.21 1.17 4,647.90$ 4
Average 0.20 0.07 0.70 1,235.06$ 199
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 Contribution
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 Campaigns)
•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 Contribution
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 (www.meetup.com)
- On-line courses (Coursera www.coursera.org)
- Swirl (swirlstats.com)
