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5/23/2019

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Copyright 2019, Staupell, LLC.

WHAT IS THIS ANALYTICS

NONSENSE, ANYWAY?

Marianne M. Pelletier, Staupell Analytics Group

APRA Indiana

June 5, 2019


Analytics in Your Daily Life


Everybody’s Doing It

Image analytics of different scanned parts of a passenger’s body

Analytics to make Zillow estimates closer to accurate.

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There Are Even Contests

Kaggle.com contest listing

Copyright 2019, Staupell, LLC. From http://nirvacana.com/thoughts/wp-content/uploads/2013/07/RoadToDataScientist1.png

Tons of Techniques and Articles


What Does It All Mean?

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What Modeling/Mining Won’t Do

DANA KATHERINE SCULLY

Born: February 23, 1964

Raised: 3170 W. 53 Road, Annapolis, Md.; San Diego, Calif.

Mother: Margaret (Maggie) Scully

Father: Capt. William Scully, USN (died December 1993)

Siblings: Older brother William, Jr.; older sister Melissa (died April 1995); younger brother Charles

WILL GIVE $1 MILLION AS SOON AS WE CALL.


What Modeling/Mining Will Do


Yeah, But How Does It Work?

Borrowed from Wikipedia, equations for logistic regression. Honest!

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Analytics Is a Combination of Disciplines

• Programming

• Visualization (We used to call that reporting)

• Statistics

• Machine Learning

• And, for us, Fundraising

ANALYTICS AND ITS ROLE IN

FUNDRAISING


Analytics in Fundraising

EngagementEvent Analysis/Social Media Mining/Sentiment

Analysis/Membership Modeling

Annual Giving

Timing/Segmentation/Behavior Chain/Renewal & Upgrade

Major/Principal Gifts

Modeling/Timeline/Portfolio Management/Assignment/

Behavior Chain

Planned GiftsModeling/Marketing

Segmentation

Volunteers

Modeling/Tracking/ Assignment

Board MembershipModeling/Advanced Modeling

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Engagement

My

Experience


Annual Giving

9/9/2009Mailing date

Bulk of mail returns

30

8/29/2009 phonathon

starts

60 90 120 150 180 210 240

Bulk of phonathon returns

Last phonathongift

1st phonathon gift

Last mail gift

1st mail gift

What could we have been doing here?


Measuring Volunteers and Gift Officers

Prospect Capacity Primary Giving %

Stage Probabilityof Giving

Donna Madonna $1 million Athletics Cultivation .48

Jo Joe $100,000 Online Media In Ask .75

Dean McLean $500,000 Children Qualification .06

Bonnie Bonanza $5 million Endowments Cultivation .65

Portfolio: Johnny Seacrest

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Segmenting Between Annual and Major Gifts

Tool: WEKA

Capacity $100,000+

Lifetime giving

$1,100+

Major Gifts

Average yearly

giving $52 or more

Attended 2 or more events

Leadership AG

3 pieces of personal

information of record

Annual Giving


Scoring Major Gifts Prospects

Largest donors =

(Life giving * 0.0005467) + (age * 0.000765)

– (class year * 0.1252)

+ (children * .000003456)

• Equations are sometimes translated to scores ranging from 1 to 99

• Used for selecting best prospects

• Created correctly, raw score is used for forecasting giving


Behavior Chain

Year 1: Prospect attends

event

If prospect joins Facebook page in Year 1, then 70%

likely: Annual Giving donor in Year 1

Year 2 to 4: If prospect brings guest to 2nd event, then 89% likely: Annual Giving donor in

Year 2.

Year 2 to 4: If prospect gives to annual giving,

then 56% likely: Leadership

Annual Giving by Year 5.

Year 2: If prospect responds positively to survey & attends 2nd

event, 93% likely: Annual giving donor in Year 2

Year 3 to 5: If prospect goes to 3rd

event, then 55%

likely: Annual Giving Donor in following

year

Year 1 to 4: If prospect volunteers, then

67% likely: MG donor by Year 6

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Finding Good Language

TYPES OF ANALYTICS PROJECTS


Cluster Analysis: The Soccer Mom Thing

Tool: WEKA

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Giving Group Characteristics

Donor •Record of an e-mail address•Attended certain events•Live in specific states

Leadership Annual Giving Donor

•Cultivated by phone more than twice•Cash total is $1,100 or more•Belong to a committee

Major/Lead Gifts •64 years old or older•Cash total is $1,100 or more•Has made stock gifts

Cluster Analysis Translated to Action


Model

Unstandardized

Coefficients

Standardized

Coefficients t Sig.

B Std. Error Beta B Std. Error

(Constant) -7436.395281 9033.303883 -0.823219874 0.410403264

Largest_gift 1.468713355 0.00116977 0.996917844 1255.557292 0

YOB 1.430976742 1.834900256 0.00065247 0.779866229 0.435488699

Zip_code_av_income 0.141377631 0.051514867 0.003651421 2.744404432 0.006073262

Zip_code_median_home -0.036998192 0.019684815 -0.002555849 -1.8795296 0.060202251

Wealthy_zip_index 5719.359642 11268.79536 0.000474527 0.507539578 0.611787845

JobFlag 1353.137091 2769.948321 0.000409818 0.488506258 0.625202368

a

b

x

Lifetime giving = (1.468713355 * Largest_gift) + (1.430976742*YOB) +

(0.121377631*Zip_Code_av_income) –

(0.036998192*Zip_code_median_home) + (5718.359642 *Wealthy_zip_index)

+ (1353.137091*JobFlag) -7436.395281

Used to put prospects in order. Can suggest ask amount.

Tool: SPSS

Linear Regression


ProspectID Predicted Gift462102 $11,737

571578 $3,058

502158 $6,529

112526 $5,704

571946 $3,175

489334 $6,005

448609 $12,230

452657 $6,080

475416 $4,764

448306 $1,434

461872 $5,988

282332 $2,973

0

50

100

150

200

250

300

350

400

-$1

49

$59

2

$1,3

33

$2,0

75

$2,8

16

$3,5

57

$4,2

99

$5,0

40

$5,7

81

$6,5

23

$7,2

64

$8,0

05

$8,7

46

$9,4

88

$10

,22

9

$10

,97

0

$11

,71

2

$12

,45

3

$13

,19

4

$13

,93

5

$14

,67

7

$15

,41

8

$16

,15

9

$16

,90

1

$17

,64

2

$18

,38

3

$19

,12

4

$19

,86

6

$20

,60

7

$21

,34

8

Fre

qu

en

cy

Predicted Gift Amounts

Scored Data

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Variables in the Equation

B S.E. Wald df Sig. Exp(B)Step 1

aWorkerFlag(1) -2.926 .123 565.050 1 .000 .054

BirthdateFlag(1) -.440 .027 264.201 1 .000 .644

Gender 26.288 2 .000

Gender(1) -.522 .107 23.936 1 .000 .594

Gender(2) -.140 .079 3.164 1 .075 .869

AddressType 2182.891 3 .000

AddressType(1) 1.289 .030 1796.932 1 .000 3.629

AddressType(2) .398 .049 66.390 1 .000 1.488

AddressType(3) 1.979 .098 405.699 1 .000 7.235

Constant 1.783 .127 195.944 1 .000 5.945

a. Variable(s) entered on step 1: WorkerFlag, BirthdateFlag, HOHGender, AddressType.

Estimates the probability of belonging to one of two groups

Tool: SPSS

Logistic Regression

Copyright 2019, Staupell, LLC. Tool: SPSS

Points out natural segments

Trees


• If (LEN_JOB_TITLE = 3) and (GENERATION = Boom) and (EMAIL_IND = Y) and (NUM_ADDR = 5) then DONOR = Y (1159.0/422.0)

• If (MARITAL_STATUS = Married) and (CONSTIT_TYPE = FRND)

then DONOR = Y (3501.0/814.0)

• If (HAS_JOB_TITLE = Y) and (STATE_NY_IND = N) and

(GENERATION = Greatest) then DONOR = Y (208.0/34.0)

• If (CULTIVATED_BY_VOL = Y) and (MGO_SOLICITED = Y and (VISITED_WITHIN_30 = Y) then DONOR = Y

Color Key:

Information Given

Demographic

Engagement/Affinity

Indicator

Wealth Screening

Organization

Activities

Tool: WEKA

Labels interactions among characteristics

Rules

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Text Mining

Tool: SPSS Text Analytics

For sentiment analysis

HOW CAN RESEARCHERS USE IT?


Major Gift Donors

Prospects Accept Visits

Donors Give

Constituents Attend Events

Use big data techniques to identify future

donors

Use modeling and forecasting techniques to identify leadership

giving and MG prospects

Your domain: Use modeling, dashboards, flow charting to move

prospect to Major Gifts

The Prospect Development Hopper

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Stage 1:

25 suspects added to the

pool

Stage 2:

20 prospects enter

cultivation

Stage 3/4:

5 prospects are asked

Complete:

3 prospects give

Estimating Prospect Churn


Prospect Churn Timeline


Just in Time Prospecting

Opportunity

39507583

1052

2802 145 248382 87

77038

893

1668

4482

340581 625

158

45740

675

1374

3359

157

270

415 116

55347

400

3012

2598100

99 154 33

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Under $33,333 $33K to 74.9K $75K to $149K $150K to $232K $233K to $399K $400K to $1.29M $1.3M to 2.19M $2.2M to $5.9M

Refusal Deferral Pledge Not Yet Solicited

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Organizing Re-Asks

100 prospects

29 pledge 25 Refuse 46 defer

11 refuse?

13 pledge?

22 defer again?

For every 100 prospects, 42 pledge.

You need 2 or more prospects for every

gift you need.

Tool: Tableau


Mapping the Pool

Source: http://web.mta.info/lirr/Timetable/lirrmap.htm

$5 million prospect no one wants to visit because he lives “out there”

100 new suspects someone dumped on your lap yesterday

.

Committees meet for hours on these high-

end prospects but no one makes the ask.

The place where management thinks

you should be looking

Who the

Researchers

found


Relationship Mapping

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IS IT WORTH DOING?


Poor

Event A

ttendance

Bad Time or Place

Wrong Themes

Outmoded Venues

Addressing Event Attendance Issues


Gauging Time or Place

• Model who comes to in-person vs.

online events

• Explore events during the week vs.

on the weekend/with or without kids

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Discerning the Right Themes

Tool: NodeXL


Identifying Venues

Tool: Tableau


Annual G

ivin

g T

ota

ls D

roppin

g

Poor Donor Acquisition

Delayed Stewardship

Inconsistent Timing

Your Org’s Pain Points

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Studying Donor Acquisition


Determining the Stewardship Sweet SpotIf first time attendees do not attend a second time...


0 1000 2000 3000 4000 5000 6000 7000 8000 9000

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

Red = record countGreen = Average High Gifr

Ye

ars

Be

twe

en

Fir

st

& h

igh

es

t G

ift

Grasping Timing

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Not R

ais

ing E

nough M

G

Not Enough Prospecting

Poor Gift Officer Adoption

Low Pipeline Efficiency

Your Org’s Pain Points


Rapid Prospecting: Giving by Job Title


Gift Officer Assigned On Hold Planned

Underway / In

Negotiation Grand Total

Name $1,750,000 $1,050,000 $2,800,000

Name $12,625,000 $33,575,000 $46,200,000

Name $100,000 $1,500,000 $500,000 $2,100,000

Name $1,400,000 $100,000 $1,500,000

Name $2,550,000 $1,610,000 $4,160,000

Name $650,000 $10,200,000 $10,850,000

Name $225,000 $230,000 $455,000

Name $1,410,000 $5,360,000 $6,770,000

Tracking Gift Officer Performance

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Pipeline Efficiency or Gap Trigger Reports

Prospects

Desperately Needed

or Gift Officer

Performance?

BUILD OR BUY?


Discuss the pain point(s)

State specific outcomes

Name the data you think should be used but allow for creativity

Be clear about how you want the results implemented• Scores in database

• Visualizations

• Presentation

Get buy-in from management to stay on the priority list

Build: Articulate Your Needs to Your Staff

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❑ Determine and articulate a specific outcome

❑ Discern the data you have and want

❑ Determine if you want to append data

❑ Name a reasonable timeline

❑ Determine your budget (but don’t share it)

Buy: Articulate to Vendors and Consultants


Specific Outcome Examples

• We need to prioritize our major gifts pool

• We need to hone our annual giving program to fit solicitation

methods to the right audiences

• Our gift officer performance is not always clear to management

• We want to know what the right engagement mix is to bring in new

donors


Reasonable Timeline

1. Time to organize and connect the players

2. Conversations with vendor or internal analysts on available data

and outcomes

3. Allowance for data preparation

4. Check in after data preparation stage

5. Allowance for modeling

6. Question and answer session on initial outcomes

7. Allowance for final modeling

8. Presentation and implementation

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Budget Considerations

• Training

• Software

• Talent

Internal

• Cost effective

• Less expensive

• Standardized results

Product Vendor

• Adapts to your data and style

• Stays with you through process

• More expensive

Service Vendor

WRAP UP


When to Add Analytics

• While planning a campaign

• After a screening project

• When the major gifts pool is getting low

• When annual giving participation or totals are dropping

• To assess the quality of the entire pool

• To check in on social media strategy

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When Not to Add Analytics

• When your database is below 1,000 records

• When you want to do it to appease a trustee

• Before you audit your database

• If all of your donors look the same


Questions?

• Marianne Pelletier

• [email protected]

• @mpellet771

• 607-592-3797

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