Beyond Prospects: How Data Mining can Uncover Insights and Guide Program Decisions

Transcription

1 Beyond Prospects: How Data Mining can Uncover Insights and Guide Program Decisions Christopher Seguin Vice-President, Advancement, Thompson Rivers University Celeste Bannon Waterman Vice-President, Research & Analytics, KCI Edmonton, Albert June 4-6 juin

2 Session Agenda Overview of Analytics techniques used for prospect identification How this process can also provide insights into: Alumni / donor interests and behaviours Overall capacity of your base of support Quantitative input for campaign planning Case Study Thompson Rivers University Recommendations for analyzing your own constituents Q&A 2

3 Fundraising Analytics What is it? Along with the trend toward big data, there are many tools, techniques (and vendors) in the field, and terminology can be challenging Fundraising analytics can be broken down into four main techniques Reporting Screening Data Mining Predictive / Prescriptive Modeling 3

4 Data Mining A type of reporting that seeks to uncover trends and linkages in data Begins by defining behaviour of interest Characteristics of monthly donors? Alumni who are more likely to become donors? Then cross-reference with other data to uncover trends Monthly donor percentage by postal code Donor percentage by age or degree type Simple scoring of records can be done based on results 4

5 Predictive / Prescriptive Modeling Begins with a Data Mining approach to uncover potential characteristics Predictive Modeling uses statistical techniques to assess relative impact of characteristics, and test the ability to predict the behaviour of interest Usually likelihood of giving in some way Can also predict giving levels Prescriptive Modeling is similar, but uses existing knowledge to build the model i.e. a prescription Either way, the results used to build a model of the ideal case Actual records are scored against the model the better the fit the higher the score 5

6 Likelihood Why do Analytics? Common reasons for conducting analytics, especially on an occasional project basis, can include: Identification of Prospects Major Giving Planned Giving Mid-level Etc. Segmentation of the alumni / donor base Guidance on allocation of resources for greater return on investment 0 Who gets all the Attention? Likely Donors Everybody Else Capacity Major Giving Prospects! Potential Prospects! 6

7 But There s More! What we learn about our donors through the analysis process can in some ways be more powerful, and have more impact, by telling us about our practices and what s really succeeding (or not) Donor-focused analytics brings a different perspective to assessing activities 7

8 BEYOND PROSPECTS: PROGRAM INSIGHTS 8

9 Alumni / Donor Interests Looking for factors related to giving can tell us what s important to donors. The graph below shows the proportion of alumni who had given by the count of children their alma mater had on file in their records. Most of this information was coming from birth announcements in the alumni magazine alumni who gave back to the school were more likely to want to share information with fellow grads. 30% of all grads had information about children on file, a relatively high proportion and an important signal of engagement. 80% 70% 60% 50% 40% 30% 20% 10% 0% Number of Children on File Non Donor Donor 9 9

10 Alumni / Donor Interests Similarly, responding to an event invitation particularly for reunion events can be a sign of greater connection to the institution and fellow alumni. The graph below shows the proportion of donors and top donors by the number of reunion events attended. Note that the number of potential reunion events is also an indication of age of the graduate, but still the difference is remarkable. Donor Level by Number of Reunion Events Attended 100% % 80% % 60% 50% 40% NonDonor Donor Top Donor 30% Count 20% % 0%

11 Alumni / Donor Behaviours The graph below shows the factors of a predictive model to identify potential $1,000+ donors for an organization. The single largest factor was a first gift size of $75 or more, 3x more than the typical first gift for donors. In other words, donors who eventually gave 4-figure+ gifts started out giving gifts higher than the typical first gift. This information can be used to change stewardship practices for first gifts to ensure these donors are well-stewarded, and flagged for personal attention if they continue to give. First Gift Was $ Years of Recent Giving (1-5) 26.4 Business Info. on File 6.2 Estimated Avg. Income (Appended) 4.6 Lives within 50km 2.8 Years Since 1st Gift 1.9 Has Free Acct Factors Predicting Future Giving at $1,000+ level 11 11

12 Program Outcomes Analysis helped the organizations realize they should: Maintain the engagement vehicles that donors were responding to Birth announcements in the alumni magazine Reunion events Adjust stewardship practices for first-time donors. Most charities have distinct stewardship practices for high-level gifts, but adjusting thresholds for first gift levels can influence subsequent giving. 12

13 BEYOND PROSPECTS: OVERALL CAPACITY AND CAMPAIGN PLANNING 13

14 Overall Capacity Most analytics processes estimate giving capacity for individual donors / alumni in some way In one respect, capacity of the entire database is the total of the individual estimates; eg. 17 donors X $50, donors X $20,000 and so on Sample Capacity Distribution L10: $50,000+ L09:$20,000-$49,999 L08: $7,500-$19,999 L07: $2,000-$7,499 L06: $900-$1,999 L05:$500-$899 L04: $300-$499 L03: $150-$299 L02: $75-$149 L01: $25-$ Individual Record Count 14

15 Overall Capacity BUT capacity does not necessarily equal philanthropy Coupled with likelihood ratings, we can get a more realistic estimate. Using our example, let s say that of the 38 donors capable of giving $20,000 to $49,999 (38 X $20,000 = $760,000): 11 are VERY Likely to give ( 75% ) 18 are SOMEWHAT Likely to give ( 33% ) 9 are UNLIKELY to give ( 5% ) We can use these to adjust our calculation, and have a much more realistic estimate for planning: $20,000 = $165, $20,000 = $118, $20,000 = $9,000 Total: $292,000 In this example, the estimated total annual capacity of the database was $6.7M, but the likely capacity was $2.4M 15

16 Campaign Planning The overall scoring of a donor base can also provide quantitative inputs to an overall campaign Chart of Standards and the gap between prospects needed and identified Sample Campaign Chart of Standards with Gap Analysis Number of Gifts Amount Total Prospects Needed Prospects Identified Prospect Gap Suspects Needed Suspects Identified Suspect Gap 1 $50,000,000 $50,000, $25,000,000 $50,000, $10,000,000 $30,000, $5,000,000 $35,000, $3,000,000 $36,000, $1,000,000 $20,000, $500,000 $20,000, $250,000 $20,000, $100,000 $12,500, $50,000 $12,500, $25,000 $10,000, $10,000 $8,000, $5,000 $8,000, Numerous < $5,000 $38,000, * Prospects 3:1 Ratio 16

17 Campaign Planning The overall scoring of a donor base can also provide quantitative inputs to an overall campaign Chart of Standards, and the gap between prospects needed and identified Sample Campaign Chart of Standards with Gap Analysis Number of Gifts Amount Total Prospects Needed Prospects Identified Prospect Gap Suspects Needed Suspects Identified Suspect Gap 1 $50,000,000 $50,000, $25,000,000 $50,000, $10,000,000 $30,000, $5,000,000 $35,000, Prospect $3,000,000 $36,000, $1,000,000 $20,000, List $500,000 $20,000, $250,000 $20,000, $100,000 $12,500, $50,000 $12,500, Analytics $25,000 $10,000, $10,000 $8,000, Input $5,000 $8,000, Numerous < $5,000 $38,000, * Prospects 3:1 Ratio 17

18 CASE STUDY: THOMPSON RIVERS UNIVERSITY 18

19 The University Originally Cariboo College, became a degree-granting institution in 1988, and became Thompson Rivers University in 2005 All courses and programs offered by the British Columbia Open University also became part of TRU Created an Advancement Office in 2008, and hired full time Faculty fundraisers in 2013 Alumni activities including data base management were negligible until the last decade Major Gift Campaigns and annual alumni campaigns were started in

20 The Project KCI was retained by Thompson Rivers University to conduct a strategic campaign study including analytics. Inputs and analysis elements in the study included: Internal and External Consultations Analysis of 10-year fundraising results Donor Analytics Donor Profile Analysis to identify key characteristics of current donors and top donors Predictive Modelling to predict giving likelihood Capacity Level Assessment to estimate donors potential giving capacity to TRU Pipeline Assessment and Gap Analysis Revenue Projection Scenarios Goal of the overall project was not to determine if a campaign was feasible, but to quantify potential revenue and key action items for success 20

21 Giving at TRU Overall, historical alumni giving rates are modest as would be expected given the relative youth of TRU and a high component of distance education However, donor counts and revenue are on a positive trajectory; since becoming TRU in 2005: The number of individual annual donors has doubled Revenue from alumni and individuals has had an average annual growth rate over 13% (excluding bequests) Recently celebrated three gifts of over $1.5 million including the largest gift in Kamloops history at $2.25 million 21

22 Giving Insights Data Mining for TRU reinforced understanding of alumni giving rates relative to other areas of support. Note that some constituency types have high donor rates because they are only entered into the donor database when they give (i.e. Faculty / Staff) 120% 100% 92% 97% 99% 80% 60% 40% 20% 0% 59% 56% 37% 23% 7% 3% 1% 0.9% 0.2% 0.3% 3.6% 21.3% 7% 70% 23.3% 46% 19% 35.1% Non-Donor Donor Top Donor 22

23 Giving Insights Predictive modeling confirmed that while there are some groups who are more likely to give (eg. former varsity athletes, Business & Economics grads); previous donors and other members of the campus community were much more likely to support TRU than alumni who have not yet given. Is Admin/Faculty/Staff (FACT) Gave Year Before Last # of Donor Codes on File Years Since First Gift Gave Last Year Has Played Athletics Home Phone # on File Studied at Kamloops Campus Business & Economics Grad School of Nursing Grad After-Tax Income (Appended) Trades & Technology Grad Age Has Exclusion Code Is an Alumnus (ALUM) Is a Non-Graduate Alumnus (ALUT) Supporter Model - Factors 23

24 Pipeline and Capacity Assessment Pipeline analysis, and consultations, helped define projected capabilities Prospects have been identified at all key levels, and most importantly TRU has good links to over 80% of those prospects Number of Donors Needed Gift Level Number of Prospects Needed Prospects in Pipeline Prospect Gap 1 $10,000, $5,000, $1,000, $500, $250, $100, Link to Prospect 13% 18% 69% Link identified and activated Link identified, not activated Link not identified 24

25 Campaign Implications & Insights Along with other inputs to the study, the analytics process helped outline important implications for TRU, including: Cultivation of top prospects and confirmation of anticipated gifts must be a top priority Individuals will be an important component of total revenue, but major donors are more likely to be friends, parents and community members Ongoing prospect identification to feed the pipeline with new donors beyond the current base of support must become a way of life Communities of affiliation will be important to engage new donors and build on current area of support Eg. Professional faculties, Athletics, etc. 25

26 Campaign Implications & Insights Analytics found individuals had likely capacity ~40% higher than current giving There is room to grow revenue from current support base, but will be conservative This, coupled with recent growth rate, used to inform projected revenue scenarios and potential goal Revenue (cumulative) 2013 (realized)-2020 (extrapolated) 3% 30% 1% 29% 17% 16% 4% Individual Donors Corporations Foundations MG Pipeline and Study Identified Gifts Pledge Expectations Government Funding Other 26

27 Final Outcomes Overall, Using a combination of quantitative and qualitative inputs, including analytics, the study was able to provide a potential target for a comprehensive campaign with realistic and achievable targets Provided data to help manage expectations By unifying our Government Relations and Fundraising efforts we know how much we can generate with the necessary steps in place including alumni cultivation and research Analytics provided a stark reality to our ability to move forward, giving us context, priority and a realistic goal 27

28 TAKING IT HOME: GETTING INSIGHTS FROM YOUR DATA 28

29 Start with Good Data Invest in thorough and consistent data management Code events, appeals, or other initiatives so that long term year-overyear reporting is possible Identify and record relationships between individuals, and between the individuals and organizations Employment, children, spouses, etc. Soft credit linking for individuals who give through corporations / foundations particularly important Prospect management rigour critical Ensure leadership and those managing data are collaborating Ensure all types of support are captured events, volunteer positions, etc. Keep addresses up to date and maintain contact 29

30 Make the Most of Reporting Your database is a source of rich data on your constituents. Use it to maximum advantage by: Keeping your finger on the pulse ensure you have reports that can keep you up to date on first-time donors, loyal donors, donors about to come off pledge, etc. And use them! Do periodic global reviews of your donor statistics make sure you know your donor acquisition, renewal, attrition rates, etc. By donor type, alumni faculty, program, channel, etc. 30

31 Do Your own Data Mining Keep it simple. Define key donor levels 3 is ideal, no more than 5 Use data your existing reports can produce i.e. lifetime giving, total giving last year, etc. Contrast these donor categories with data not related to giving, eg. Event attendees vs. non-attendees Constituent type Student activities Get creative, but watch out for recognition-related data i.e. stewardship events vs. reunion events 31

32 Sample Table Start with data structured like this example. Each donor category is contrasted with a single factor to identify trends. In the example below, individuals with addresses on file have a higher proportion of donors and top donors. on File? Non Donor Donor Top Donor Total No - Count No - Percentage 88.21% 10.29% 1.50% Yes - Count Yes - Percentage 68.95% 23.73% 7.32% Total

33 But Pictures Tell a Better Story 80% 70% 60% 50% 40% 63.7% 72.6% Non-Donor Donor As often as possible, present data graphically to illuminate trends. 30% Top Donor 20% 10% 0% No on File 0.4% 1.5% on File 33

34 The Final Word Relationships ( It s what it s all about, the data just tells you the story ) 34

35 Questions? 35

36 Thank you! Presenter: Christopher Seguin Firm: Thompson Rivers University Vice-President, Advancement Presenter: Celeste Bannon Waterman Firm: KCI Vice-President, Research & Analytics 36