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Post on 01-May-2018
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What Is “Analytics?”
L ki B k L ki F dLooking Back• Constituents• Program
Looking Forward• Constituents• Program• Program • Program
Fundraising Has Three Primary Business Processes
Base DevelopmentOne to many strategies of engagementOne-to-many strategies of engagement
Major/Planned Gift DevelopmentOne to one high ROI strategiesOne-to-one high ROI strategies
Prospect DevelopmentC i f b jConversion from base to major
Prospect Development has Three Stages Feeding Major and Planned Gift Cultivation
Market ResearchIdentification with screening and modeling
Prospect ResearchQualification with data
Fi ld R hField ResearchDiscovery / qualification
through interaction
Plan Strategy
Stewardship CultivationMajor Gift
FundraisingCycle
5Solicitation
Effective Prospect Development for Planned Giving
Identifies prospects meeting the criteria planned gift donors.Traditional characteristics- Traditional characteristics
- Characteristics unique to your organizations
Works with fundraisers to develop strategies for aligning the p g g gprospects with the institution for a philanthropic partnership.
Characteristics
AssumptionsC i t t d
ObservationsA ti ll Consistent donors
Old donors
Donors with appreciated
Assumptions generally accurate for most institutions.
Other common characteristics Donors with appreciated assets from our research:
Legacy families
Multiple property owners
Employment in education d bli iand public service
Donor loyalty
Positive donor experience
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Positive donor experience
Drawing Planned Giving Donors Out of a Hat
Imagine a hat with 130 slips of paper.
About 31% of the slips have the words “planned giving donor” written on them.
If you draw a slip out of the hat If you draw a slip out of the hat, approximately 1 in 3 will be a PG donor.
For most organizations, planned giving donors represent a far lesser portion (<5%).
Can We Improve This Ratio?
We could survey our actual planned giving donors asking:donors asking:
How would you describe yourself?- A blue slip of paperp p p- A green slip of paper- A yellow slip of paper
The Answer: Unknown
There is not enough information.
Y d t k th di t ib ti f th d l tiYou do not know the distribution of the random population.
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Consider Your View
Now, which slip will you select?
Population Total Count % of Total PG Donors % of PG
Donors% of Color that are PG Donors
Blue 60 46% 20 50% 33%
Green 60 46% 12 30% 20%
Yellow 10 8% 8 20% 80%
Total 130 100% 40 100% 31%Total 130 100% 40 100% 31%
33%33%
67%
1 in 3 1 in 5 4 in 5
Principle
Common characteristics may not be distinguishingcharacteristicscharacteristics.
How populations are different (target vs. random) is more interesting statistically and predictive than common characteristics of a target group.
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Modeling Can Predict Many Things
Major, planned, and annual giving
Bequests, annuities, trusts
Program or department models. (giving to fine arts capital needs (giving to fine arts, capital needs, scholarships, patient care, etc.)
Membership likelihoodS i k b i iSeason ticket subscriptionsAlumni affinityChannel preferences (mail Channel preferences (mail, phone, email)Next gift amounts
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Loyalty scoring with precise weightings
Effective for Planned Giving:
Your constituents compared to
Your success stories using
Your data to identify
Your unique opportunity
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Method
Understand your goals before you begin.y g
Gather your data. Included demographics, giving, research, and screening dataand screening data.
Prepare the data for modeling.
Model.Model.
Evaluate the results against existing donors and prospects.
Score the file and implement the results.
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Common Score Format (Fractional ranking displayed)
All records have a ranking and a 0–1,000 score.
Planned Giving Rank Label
Planned Giving Score
Minimum Maximum
0 Lower 50% 4 500
1 Top 50% 500 750
2 Top 25% 750 900
3 Top 10% 900 9503 Top 10% 900 950
4 Top 5% 950 975
5 Top 2.5% 975 990
6 Top 1% 990 995
7 Top 0.5% 995 997
8 Top 0.25% 998 999
9 Top 0.1% 999 1,000
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Evaluate by Comparing Scores to Actual PG Donors
90100
5060708090
enta
ge
010203040
Perc
e
0
0 Lo
wer
50%
1 To
p 50
%
2 To
p 25
%
3 To
p 10
%
4 To
p 5%
5 To
p 2.
5%
6 To
p 1%
Top
0.5%
p 0.
25%
p 0.
1%
5
7 T
8 To
p
9 To
p
PG Donor Not PG Donor
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PG Donor Not PG Donor
Sample of Possible Variables in Your Model
Category Variable
Giving
Length of Giving Relationship
Frequency Index
Monthly Payment PreferenceMonthly Payment Preference
Capacity Multiple Property Ownership
>100 miles from campus
Geography Texas (-)
77251 (+)
Event Attendance (+)
Management Survey Response (+)
Alumni Volunteer(+)
Ed J b T l ( )Demographics
Education Job Title(+)
Single(+)
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Opportunity: Review Portfolio, Prioritize Direct Marketing Appeals, g pp
Planned Giving Not Assigned a Model Rank Prospect Manager Managed
0 Lower 50% 53,425 92
1 Top 50% 26,507 257
2 Top 25% 15,330 724
3 Top 10% 4,767 585
4 T 5% 2 201 4744 Top 5% 2,201 474
5 Top 2.5% 1,197 410
6 Top 1% 326 208
7 Top 0.5% 129 139
8 Top 0.25% 59 101
9 Top 0 1% 20 88
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9 Top 0.1% 20 88
Bringing Data Mining In-House
More and more organizations have in house data mining have in-house data mining capacity, from large shops to small shops.
Large shops generally have dedicated staff.
S ll h h d l d h Small shops have developed the skill sets in research, advancement services, or annual giving.
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Making the Case
Gather references of peers and aspirant peers.
B ild f ti l j t tBuild a cross-functional project team.
Start with short-term projects—specific appeals.- Communicate goals before the projectCommunicate goals before the project.- Communicate the success after the project.
Educational and research institutions:- Explore on-campus knowledge resources (economics, statistics,
business departments).Explore on campus software resources- Explore on-campus software resources.
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Statistics Software
SPSS- My personal preferencey p p- User friendly for expert and novice alike- Large network of other researchers using SPSS
SASSAS- Very powerful for large data sets- Needed for regulatory testing
(not necessary in fundraising)(not necessary in fundraising)- Good network of researchers using SAS
DataDeskObj t i t d f t t d t d- Object-oriented format easy to understand
- Excellent for exploratory analysis- Large network of other researchers using DataDesk
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Training
Software training courses
Conferences and users groups
Learning through outsourcing (you b i th d l ll are buying methodology as well
as analysis)
Onsite consultingOnsite consulting
Campus resources
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Learn Through Outsourcing
Many organizations outsource their analytics; benefits include:benefits include:
Expert analysis.
Opportunity to learn from their methodologyOpportunity to learn from their methodology.
High level of service over the short term.
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Developing In-House Capacities
It is not hard to learn.
A l ti i b i t f th tit t l ti d Analytics is becoming part of the constituent relations and admissions skill set.
Nobody knows your data like you do.y y y
Ability to create multiple models and analysis—not to be restricted by costs.
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When You Leave Today, Remember:
Build a prospecting plan around your unique characteristicsyour unique characteristics.
Consider predictive analytics to identify and prioritize your list.
Comparing PG donors to random donors is more valuable than summarizing common PG donor summarizing common PG donor characteristics.
Whether you outsource or build yanalytics in-house, analytics is within your reach.
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