1. April 29 - May 1, 2015 Better Donor Engagement Through Cluster Analysis

2. 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

3. 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

4. Challenges Flat Annual Workplace Campaign Aging donor base Opportunity: Better Engagement With Our Constituents

5. 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?

6. 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

7. 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

8. 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!

9. 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

10. 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. DatasetClustering Output

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

12. Initial Correlation Analysis

13. Final Correlation Analysis After variable reduction to reduce multi-collinearity 6 variables remain

14. 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

15. 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

16. Initial Cluster Analysis Output

17. Number of Clusters - Visual Approach

18. Final Cluster Analysis Output

19. Cluster Analysis – Numerical Output

20. 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

21. 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

22. 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

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

24. 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)

25. Contact Information doug.brueckner@uwgc.org 513-762-7102