WHAT IS THIS ANALYTICS NONSENSE, ANYWAY?

Transcription

1 WHAT IS THIS ANALYTICS NONSENSE, ANYWAY? Marianne M. Pelletier, Staupell Analytics Group APRA North Texas November 17, 2017 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. 1

2 There Are Even Contests Kaggle.com contest listing Tons of Techniques and Articles From tascientist1.png What Does It All Mean? 2

3 11/14/2017 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 Copyright 2016, Staupell, LLC Yeah, But How Does It Work? Borrowed from Wikipedia, equations for logistic regression. Honest! Copyright 2016, Staupell, LLC 3

4 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 Engagement Event Analysis/Social Media Mining/Sentiment Analysis/Membership Modeling Annual Giving Timing/Segmentation/Behavior Chain/Renewal & Upgrade Volunteers Modeling/Tracking/Assi gnment Major/Principal Gifts Modeling/Timeline/Portfolio Management/Assignment/Be havior Chain Planned Gifts Modeling/Marketing Segmentation Board Membership Modeling/Advanced Modeling 4

5 Engagement My Experience Annual Giving 1 st phonathon gift 8/29 phonathon starts Bulk of phonathon returns Bulk of mail returns Last phonathon gift 30 9/9/2009 Mailing date 1 st mail gift What could we have been doing here? Last mail gift Measuring Volunteers and Gift Officers Portfolio: Johnny Seacrest Prospect Capacity Primary Giving % Stage 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 Probability of Giving 5

6 Segmenting Between Annual and Major Gifts If (LEN_JOB_TITLE = 3) and (GENERATION = Boom) and ( _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 Scoring Major Gifts Prospects Largest donors = (Life giving * ) + (age * ) (class year * ) + (children * ) 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 Finds which techniques move the relationship forward. Normally, 12% of assigned prospects make a major gift. Welcomed a visit within 30 days of assignment Yes 55% No 45% Cultivated by Volunteer or Dept Head Yes 75% Solicited by IGO Yes 85% Yes 25% No 25% No 75% 6

7 Sentiment TYPES OF ANALYTICS PROJECTS Cluster Analysis: The Soccer Mom Thing Tool: WEKA 7

8 Frequency -$149 $592 $1,333 $2,075 $2,816 $3,557 $4,299 $5,040 $5,781 $6,523 $7,264 $8,005 $8,746 $9,488 $10,229 $10,970 $11,712 $12,453 $13,194 $13,935 $14,677 $15,418 $16,159 $16,901 $17,642 $18,383 $19,124 $19,866 $20,607 $21,348 11/14/2017 Cluster Analysis Translated to Action Giving Group Donor Leadership Annual Giving Donor Major/Lead Gifts Characteristics Record of an address Attended certain events Live in specific states Cultivated by phone more than twice Cash total is $1,100 or more Belong to a committee 64 years old or older Cash total is $1,100 or more Has made stock gifts Copyright 2016, Staupell, LLC Linear Regression Used to put prospects in order. Can suggest ask amount. Unstandardized Standardized Model Coefficients Coefficients t Sig. B Std. Error Beta B Std. Error (Constant) a Largest_gift x YOB b Zip_code_av_income Zip_code_median_home Wealthy_zip_index JobFlag Lifetime giving = ( * Largest_gift) + ( *YOB) + ( *Zip_Code_av_income) ( *Zip_code_median_home) + ( *Wealthy_zip_index) + ( *JobFlag) Tool: SPSS Scored Data ProspectID Predicted Gift $11, $3, $6, $5, $3, $6, $12, $6, $4, $1, $5, $2, Predicted Gift Amounts 8

9 Logistic Regression Estimates the probability of belonging to one of two groups Variables in the Equation B S.E. Wald df Sig. Exp(B) Step 1 a WorkerFlag(1) BirthdateFlag(1) Gender Gender(1) Gender(2) AddressType AddressType(1) AddressType(2) AddressType(3) Constant a. Variable(s) entered on step 1: WorkerFlag, BirthdateFlag, HOHGender, AddressType. Tool: SPSS Trees Points out natural segments Tool: SPSS Rules If (LEN_JOB_TITLE = 3) and (GENERATION = Boom) and ( _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) Color Key: Information Given Demographic Engagement/Affinity Indicator Wealth Screening Organization Activities If (CULTIVATED_BY_VOL = Y) and (MGO_SOLICITED = Y and (VISITED_WITHIN_30 = Y) then DONOR = Y Labels interactions among characteristics Tool: WEKA 9

10 Text Mining For sentiment analysis Tool: SPSS Text Analytics Behavior Chain Year 2 to 4: If prospect brings guest to 2 nd event, then 89% likely: Annual Giving donor in Year 2. Year 1 to 4: If prospect volunteers, then 67% likely: MG donor by Year 6 Year 2 to 4: If prospect gives to annual giving, then 56% likely: Leadership Annual Giving by Year 5. Year 1: Prospect attends event If prospect joins Facebook page in Year 1, then 70% likely: Annual Giving donor in Year 1 Year 3 to 5: If Year 2: If prospect responds positively to survey & attends 2 nd event, 93% likely: Annual giving donor in Year 2 prospect goes to 3 rd event, then 55% likely: Annual Giving Donor in following year The Prospect Development Hopper Use big data techniques to identify future donors Use modeling and forecasting techniques to identify leadership giving and MG prospects Donors Give Constituents Attend Events Prospects Accept Visits Major Gift Donors Your domain: Use modeling, dashboards, flow charting to move prospect to Major Gifts 10

11 Organizing Re-Asks 100 prospects 29 pledge 25 Refuse 46 defer 11 refuse? 13 pledge? 22 defer again? Tool: Tableau For every 100 prospects, 42 pledge. You need 2 or more prospects for every gift you need. Visualization and Mapping $5 million prospect no one wants to visit because he lives out there Committees meet for hours on these highend prospects but no one makes the ask.. Who the Researchers found The place where management thinks you should be looking 100 new suspects someone dumped on your lap yesterday Source: IS IT WORTH DOING? 11

12 Poor Event Attendance 11/14/2017 Addressing Event Attendance Issues Bad Time or Place Wrong Themes Outmoded Venues Gauging Time or Place Model who comes to inperson vs. online events Explore events during the week vs. on the weekend/with or without kids Discerning the Right Themes Tool: NodeXL 12

13 Annual Giving Totals Dropping 11/14/2017 Identifying Venues Tool: Tableau Your Org s Pain Points Poor Donor Acquisition Delayed Stewardship Inconsistent Timing Studying Donor Acquisition 13

14 Years Between First & highest Gift Not Raising Enough MG 11/14/2017 Determining the Stewardship Sweet Spot If first time attendees do not attend a second time... Grasping Timing Red = record count Green = Average High Gifr Your Org s Pain Points Not Enough Prospecting Poor Gift Officer Adoption Low Pipeline Efficiency 14

15 Rapid Prospecting: Giving by Job Title Tracking Gift Officer Performance Underway / In Negotiation Gift Officer Assigned On Hold Planned 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? 15

16 BUILD OR BUY? Build: Articulate Your Needs to Your Staff 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 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) 16

17 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 17

18 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 18

19 In case we have extra time Questions? Marianne