1. To Data Discovery & Predictive Analytics
Beyond Reporting …
To Data Discovery & Predictive Analytics
... benefits and best practices.
Copyright 2019

2. Agenda Reporting vs. Data Discovery Technology Landscape
Annual Giving: Slicing and Dicing Lists
Major Giving: Attachment Scores
Predictive Modeling Use Cases

3. Data Discovery: Complements Reporting
Reports & Dashboards:
Pre-structured
Primarily Static
Quick
Essential Facts
Well Formatted
Standard Outputs
Data Discovery:
Ad Hoc
Highly Interactive
Don’t Know the Questions
Want to See Stories
Need to Slice and Dice Lists
Ad Hoc Analytical “Sandbox”
Prospect Identification
Campaign Results
Financials
Annual Giving
Campaign Feasibility
Funds Management
Ad Hoc Reports
Major Giving Metrics
Activity Reports
Field Officer Dashboard
Predictive Modeling

4. Data Discovery: “Brings Data to Life”
Access
Multiple
Sources
Blend
“Bridge” data systems!
Model
Synthesize
Empower
Your Team
In-Memory
Data Mart

5. Technology Landscape

6. Technology Landscape

7. Technology Landscape

8. Technology Landscape

9. Technology Landscape ve

10. ve

11. Prospect Pool Management / Metrics Prospect Identification
Financial Reports
Campaign Dashboard
Field Officers
Prospect Pool Management / Metrics ve
Annual Giving
Prospect Identification

12. Kill the “Cycle of Pain”
CUSTOM REPORT REQUESTS:
Custom query submitted to IT
Backlog – 5 days to get answer
Begs another question, back through the cycle
Frustration on both sides
EXCEL:
Download “extracts”
Slice and dice in Excel
Time consuming and challenging
Don’t have all the data
Hard to show to management
“Shadow data systems”

13. Create: Collaboration
Answers in seconds vs. weeks
More collaboration across the team
“Stories” vs. “Stats”
Improved metrics and performance
More analytical and also more strategic thinking
90% reduction in custom reports
Facilitate immediate visual data mining for novice users.

14. Annual Giving: Cut Lists

15. 24,469 LYBUNTs (current donors)
All gave last year; 1/3 have yet to give this year..
Click here to select.

16. 3,892 of LYBUNTs Give in the Dec. Quarter Each Year
It’s now March and 8.5% of them have not yet given!!
Click here to select.

17. 325 of the Dec. Quarter Donors Missed this Year!!
Click here to select.
60 have given in Dec. for the past 5 years, 98 the past 4 years!!

18. Here’s the list of the 325; Export to Excel
…. with contact info.

19. Predictive Modeling
“Uses mathematical tools and statistical algorithms to examine and determine patterns in one set of data . . .
. . . in order to predict behavior in another set of data
Integrates well with in-memory-data and data visualization”

20. Iterative Process

21. Top 10 Use Cases Major Giving Annual Giving Other Attachment Scores
Expected Ask Values
Ranked List for a Program
Planned Giving
Annual Giving
Segment Non-Donors (most attractive to solicit)
Ask Amounts
Best Appeal Messages
Other
Patient Potential (from patient encounters)
Event Attendance (who is likely to attend)
Ad Hoc Hypothesis Testing
Source: ADVIZOR Fundraising Clients

22. What Matters in Attachment
Example:
Higher Education Attachment
Example:
Healthcare Patient Potential to Give
Volunteer positions
Bin by type
Reunions attended
Giving history
Last 5 years
6 to 10 years
Events attended
Last 2 years
Last 3 to 10 years
Legacy admitted/denied
Newsletter clickthroughs
Last 6 months
Last 7 to 12 months
Student sports
Student activities
Distance from institution
Current on file
Most Recent Area Visited (+/-); e.g.:
Urology (+)
Dermatology (+)
ENT Surgery (+)
Age (+ from 40 to 90)
# Visits Overall (+ up to 49)
Being Male (+)
Most Recent Facility Visited (+/-)
First Area Visited (+/-)
Time Between 1st & Last Visit (+)
Most Recent CPT Group (+/-)
# Other Patients Related To (+/Cube Root)
Distance from Hospitals (+/log)
# Distinct Areas Visited (+)
Last mo.
Last 3 mo.
Last Visit was in Last 6 Mo. (+)

23. Attachment Scores: Find the Best Prospects

24. Scenario: Quadrant A, not staffed
Example: Unassigned Quadrant A Prospects?
Scenario: Quadrant A, not staffed
Click here to select Quadrant A.

25. Scenario: Quadrant A, not staffed
Example: Unassigned Quadrant A Prospects?
Scenario: Quadrant A, not staffed
3,277 Prospects in Quadrant A.

26. Scenario: Quadrant A, not staffed
Example: Unassigned Quadrant A Prospects?
Scenario: Quadrant A, not staffed
631 aren’t staffed. Click to select.
These are staffed. They are our top prospects. These portfolios are too big
Ideal portfolio
Size:

27. Example: Unassigned Quadrant A Prospects?
The 631 are all over the country.
The 631 are all over the country.
The 631 are all over the country.

28. Scenario: Quadrant A, not staffed
Example: Unassigned Quadrant A Prospects?
Scenario: Quadrant A, not staffed
Click to select Florida.

29. Scenario: Quadrant A, not staffed
Example: Unassigned Quadrant A Prospects?
Scenario: Quadrant A, not staffed
Sweep and click to select Miami area.

30. Scenario: Quadrant A, not staffed
Example: Unassigned Quadrant A Prospects?
List of the 24 in Miami Area.
Scenario: Quadrant A, not staffed
Key Attachment Factors, in priority order.
What they’ve given.
How much we think they could give.
Capacity Rating.
Attachment.

31. Scenario: Quadrant A, not staffed
Example: Unassigned Quadrant A Prospects?
Scenario: Quadrant A, not staffed
Committee Participation is the most influential Attachment Factor. Only 3 have been on a committee. Click to see which committees.
Committee Participation is the most influential Attachment Factor. Only 3 have been on a committee. Click to see which committees.
Committee Participation is the most influential Attachment Factor. Only 3 have been on a committee. Click to see which committees.
Action Item: Set up a dinner in Miami and get the rest of these people on key committees that fit their interest areas.

32. Scenario: Quadrant A, not staffed
Example: Unassigned Quadrant A Prospects?
Scenario: Quadrant A, not staffed
Here’s which committees those 3 have been on, and when.

33. Scenario: Quadrant A, not staffed
Example: Unassigned Quadrant A Prospects?
Scenario: Quadrant A, not staffed
List of the 24 for export to Excel. Can have a bunch of other columns off to the right.

34. Segment Non-Donors
Study non-donors who have been recently acquired. Who are they? What are their characteristics? Who else has similar characteristics??
For Graduates L5Y: Women, live in New York City, have degrees of MACC, JD, PHARMD, CERT, BBA, or BSHP*
For Graduates older than L5Y: Married, live in Virginia, Texas, or New York City, have Professional Degrees such as JDs, MBAs, BBAs, event attendees*
*Score based on weighted combination of all of these factors.

35. Focus on Successful Appeals …
Typically 10 to 15% of the appeals work
But teams often send everybody everything
Should measure and then focus on what works
And send primarily to “Look Like our Donors” segment
Successful appeals are generally:
Higher “touch”:
For example, from a class agent who knows the prospect
Personalized letter or call
Theme based to prospects’ interest areas
For example, healthcare message to medical professionals …
… sport message to alum who always clicks on sports article in newsletter
Etc.

36. … with 6 to12 Touches per Year
Goal: send each entity in target segment 6 to 12 effective appeals per year
Problem: often “over touch” with unsuccessful appeals …
We have seen 50+ to over half the non-donor base
Heavy
… and “under touch” with successful appeals
At same client only 1 to 2 successful appeal touches per year to people in target segment because sent to too many other people
Why: higher yield appeals generally cost more …
… so segmenting the non-donor base to focus on “look like my donors” is important
Substantial improvement potential

37. Predictive Modeling – Summary
Focus on Answering Questions
Do it in house; your team knows the data best
Start simple; don’t over complicate
“A simple model completed now is better than a complex model that takes forever”
Iterate and evolve, experiment, use common sense
Use the data that you have; don’t go crazy with social media and other “extras”
Embed it in your Data Discovery / Reporting system so that it updates and you can keep using it

38. Discussion, Q&A Other Tutorials: www.AdvizorInsights.com/resources
Follow-up: