1. Session Code: 314 Competing in an Analytical Environment
Phillip Manning
Associate Director for Analytics
The Pennsylvania State University
Kelly Snyder
Executive Director Research and Analytics

2. Penn State Fundraising Analytics
More than just data mining…
The use of statistical analysis and predictive models to support and optimize fundraising decisions

3. Analytics at Penn State…the beginning
History/background
Data Warehouse access for Research Staff
Data mining using Microsoft Access® as the interface
Subjective scoring
Prioritizing lists

4. Preparing for Analytics
Data Integrity Projects
Developed high job title script and identified/linked large public and private employers
Instituted event attendance tracking
Created corporate hierarchy process for big companies
Cleaned up abnormal birthdates
Automated additions of wealthy zip codes to prospects
Campus/College development and advisory board data clean-up, coding of past commencement speakers

5. Data Integrity Projects
Continual review and clean-up related to deceased alumni as well as clean-up of duplicate records
Collaboration with the Alumni Association relative to data consistency
Recoding of Honorary Alumni vs. Honorary Degrees
Collaboration with special mission units to collect data for prospect identification
Historical Library work-study and wage employees
Historical Research Assistants in our Applied Physics Lab

6. Takeaway... Know your data Partner with your IT staff
Prepare your data in anticipation of starting a “formal” analytics program
These were critical in our ability to scale the analytics program and become productive quickly.

7. Making the Case/Finding the Resources
Tested outside vendor products
Looked at University-wide resources
Benchmarked with a number of other Universities
All this and still, due to budget constraints, we could not get a new position funded
How did it ultimately happen???

8. Hiring Process Know the skill set that you need Technical Development
--When unable to find both sets of skills, we chose hiring a candidate with the technical skills that we believed we could “coach up” the development skills and our specific data.
--We felt this was reasonable since we had a number of power users who were very comfortable with the data and our development culture and processes.

9. Penn State’s Analytical Curve
Data
Modified from Davenport, T.H. & J.G. Harris Competing on Analytics: The New Science of Winning

10. Steps to Productivity Data Management Analytical Tools Collaboration
Quality
Access
Transformation
Analytical Tools
Collaboration

11. Data Management 60-80% of the Process Quality Data
Complete, consistent and valid information that represents its intended use in decision making

12. Data Management Data Access
The ability to retrieve and manipulate information

13. Data Management Transforming Data
Converting information from one form to another, a more meaningful form

14. Your Analytics Toolbox
Statistical analysis and predictive modeling requires more than just Microsoft Office®
*Penn State University does not endorse or necessarily have an affiliation with any of the software providers presented

15. Collaboration Leads to Success
"To apply an analytical discipline to our fundraising work by refining the prospect and donor populations, re-evaluating the methodology used to reach those populations and understanding the motivations of those populations in order to improve the solicitation and stewardship process" 

16. Collaboration Leads to Success
Work with all fundraising staff throughout developing projects to get “buy-in” early

17. Start with a Win Define a single project with clear objectives
Incorporate all relevant staff in the project from day one
Educate and continue to work with users to use the product and provide feedback for future model improvement

18. Penn State’s Planned Giving Model
17,792
Potential Planned Gift Prospects
What’s Important?
Affinity
Volunteer Engagement
Student Activities
Current Activities
Consistent support
Years of Giving
Multiple $1,000 gifts
Alumni Membership
Performance
(Dues Paying)

19. Continue to Win Further wins at Penn State
Major gift likelihood model
Annual leadership gift likelihood models
Provide value to other departments by taking on analytical projects outside of the research and prospect management office
Talent Management
Central Development

20. Learn and Improve Continuously
Spend 10% of your work time learning
Take time each week to think, come up with a new project on your own, investigate a problem or learn a new skill
Value feedback (positive or negative)
Feedback means people are using your products
Allows you to improve your processes

21. Advanced Analytics

22. Advanced Analytics

23. Final Thoughts Analytics buy-in from organization
Resources/implementation
Prepare before you initiate your analytics program
Data management/current processes
Staff the program with the right balance of technical and soft skills to make the sell
Collaborate across departments and within the organization Drives success
Win first and continue to win

24. Thank you for attending
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