11/14/2017

1

WHAT IS THIS ANALYTICS

NONSENSE, ANYWAY?

Marianne M. Pelletier, Staupell Analytics Group

APRA North Texas

November 17, 2017

Analytics in Your Daily Life

Thanks to Joe Loong: https://www.flickr.com/photos/joelogon/2819512729/

And http://clipart.me/free-vector/credit-score

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 ContestsKaggle.com contest listing

Tons of

Techniques

and Articles

From http://nirvacana.com/thoughts/wp-content/uploads/2013/07/RoadToDa

taScientist1.png

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.

Copyright 2016, Staupell, LLC

What Modeling/Mining Will Do

Copyright 2016, Staupell, LLC

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/Be

havior 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 phonathonstarts

60 90 120 150 180 210 240

Bulk of phonathon returnsLast 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

• 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 GivenDemographic

Engagement/Affinity Indicator

Wealth ScreeningOrganization Activities

Tool: WEKA

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

Cultivated by Volunteer or Dept

HeadNormally, 12% of

assigned

prospects make a

major gift.

Welcomed a visit within 30

days of assignment

Solicited by IGO

Yes 85%

Yes 75%

Yes 55%

No 45%

No 25%

Yes 25%

No 75%

Finds which techniques move the relationship forward.

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Sentiment

TYPES OF ANALYTICS

PROJECTS

Cluster Analysis: The Soccer Mom Thing

Tool: WEKA

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Copyright 2016, Staupell, LLC

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

Linear Regression

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

Scored Data

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

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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.

Logistic Regression

Estimates the probability of belonging to one of two groups

Tool: SPSS

Trees

Tool: SPSS

Points out natural segments

Rules

• 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

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Text Mining

Tool: SPSS Text Analytics

For sentiment analysis

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

The Prospect Development Hopper

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

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

prospect to Major Gifts

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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

Visualization and Mapping

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

IS IT WORTH DOING?

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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

Discerning the Right Themes

Tool: NodeXL

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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

Studying Donor Acquisition

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Determining the Stewardship Sweet Spot

If first time attendees do not attend a second time...

Grasping Timing

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

Not R

ais

ing E

nough M

G

Not Enough Prospecting

Poor Gift Officer Adoption

Low Pipeline Efficiency

Your Org’s Pain Points

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Rapid Prospecting: Giving by Job Title

Tracking Gift Officer Performance

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

Pipeline Efficiency or Gap Trigger Reports

Prospects

Desperately

Needed or Gift

Officer

Performance?

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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

Buy: Articulate to Vendors and Consultants

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)

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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

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

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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

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

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In case we have extra time

https://www.youtube.com/watch?v=TdqRqRXeS-Q

Questions?

•Marianne Pelletier

•marianne@staupell.com

•@mpellet771

•607-592-3797