Meg Weber. Analytics for Annual Giving Meg Weber January 19, Executive Director of Annual Giving Colorado State University

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1 January 19, 2016 Executive Director of Annual Giving Colorado State University 17 years in annual giving Bachelor of Arts in Communication Page 1 January 19,

2 Colorado State University Large Public Research University FY15 Raised $173 Million from 34,000 donors Annual Giving Program is participation focused 136,000 Undergraduate Alumni APR 10.34% in FY15 up from 7.24% in FY11 Page 2 How to Get Nerdy With Numbers When You Are Not a Numbers Nerd January 19, 2016 January 19,

3 Agenda Using analytics to measure outcomes Reports that tell the right story to the right audience Use data to drive your strategy Using data to set achievable goals Maximizing your resources by making data driven decisions Page 4 Poll: Is your annual giving program data driven? Yes No Not Sure Page 5 January 19,

4 Why are analytics important? You achieve what you measure More likely to achieve goals when they are meaningful and possible Understanding what drives the big numbers leads to thoughtful planning Proving (or disproving) hunches brings greater intention into your program Page 6 MEASURING OUTCOMES Page 7 January 19,

5 What to Measure High Level Basics: Number of Donors Revenue Alumni Participation Rate Page 8 Driving Reports Under the Surface Donor Types Retention Reactivation Acquisition Channels Page 9 January 19,

6 Basic Formulas Retention Rate: Donors with gifts in CY and PY Donors with gifts in PY Reactivation Rate: Donor in CY and any year other than PY Donor with gift in any year other than PY Acquisition Rate: Donor with first gift ever in CY All Never Givers CY = Current Year PY = Previous Year Page 10 Channel Metrics Direct Mail Phonathon Response Rate # of responses # of pieces mailed Cost Per Piece total spend # of pieces mailed Cost Per $ Raised total $ raised total spend Cost Per Donor total donors total spend Average Gift* $ raised # of gifts Complete Rate # of completed records Total records Contact Rate # records with response # of completed records Pledge Rate # of pledges # records with response Average Pledge* $ pledged # of pledges Pledge Fulfillment # gifts or $ raised # of pledges or $ pledged Open Rate # opened total sent - bounces Click Rate # clicked total sent - bounces Bounce Rate # bounced total sent Conversion Rate # actions total sent - bounces Average Gift* $ raised # of gifts *Beware of Spikes... Median and Mode are good to know as well! Median is the number in the middle, mode is the most common number. Page 11 January 19,

7 Slicing and Dicing Apply analysis to smaller segments by years of giving by donor type by class year by channel by unit movement up or down donor pyramids average gifts Page 12 Benchmarking Benchmarking Against yourself (year over year) Against your peers Against your aspirational peers Tools for Benchmarking Council for Aid to Education (CAE) - VSE report U.S. News & World Report Vendors Target Analytics, Reeher, consulting firms Page 13 January 19,

8 REPORTING 101 Page 14 Poll: When it comes to reports, do you have? Not Enough Too Many Just Right Page 15 January 19,

9 Know Your Audience Administration: High level view Colleagues: Good understanding of goals and progress/how to get there Your staff: Need access to all the metrics for which they are measured, then however nerdy they want to be You: Need to understand everything driving all your metrics Page 16 50,000 View Leadership Page 17 January 19,

10 30,000 View Colleagues and Staff Source: University of Delaware Page 18 30,000 Close Up Page 19 January 19,

11 30,000 Colleagues and Staff Page 20 30,000 Close Up Page 21 January 19,

12 5,003 Annual Giving Staff Page 22 In the weeds Page 23 January 19,

13 In the weeds Page 24 USING DATA TO DRIVE STRATEGY Page 25 January 19,

14 Partners in Advancement Page 26 Migrating Donors Page 27 January 19,

15 Gift Bands Show Donor Behavior Are people down-grading in a specific band? use a second ask to move them up Big donors moving significantly down? pick up the phone Small donors moving significantly up? pick up the phone Page 28 Poll: Have you used predictive modeling in annual giving? Yes No Not Sure Page 29 January 19,

16 Predictive Modeling Leverages statistics to predict outcomes What do your loyal donors have in common? Which of your sporadic or non-donors fit the mold? Page 30 How to Get a Predictive Model 1. Buy One Custom to your data 2. Build Your Own Start small (10,000 records) Assign positive and negative values to characteristics on record Test it out Page 31 January 19,

17 Build Your Own A Simple Score by Peter Wylie Applied to 10 diverse universities... A good starting point To start: Ply your IT staff with wine or cookies Choose 10,000 random records Page 32 Data Points to Build a Model ID Number Home phone listed Business phone listed address listed Oldest or youngest 25% of alums Marital status is null or single Lifetime Giving Page 33 January 19,

18 Building the Model Home Phone Listed 0/1 + Business Phone Listed 0/1 + Listed (any type) 0/1 + Oldest Grad Quartile 0/1 - Youngest Grad Quartile 0/1 - Marital Status Unknown 0/1 - Single 0/1 + 3 ID HP BP EM OQ YQ MSU S +3 Score Page 34 Applying the Model High Likelihood/ High Capacity High Likelihood/ Low Capacity Low Likelihood/ High Capacity Low Likelihood/ Low Capacity Page 35 January 19,

19 SETTING (AND REACHING) GOALS Page 36 Realism and Managing Expectations Use data to keeps your goals realistic If you don t set your own goals, data can help you show what is actually possible Resources need to meet expectations Show me the money! Page 37 January 19,

20 Use the Past to Predict the Future What are your average retention rates? Account for outliers What are your average response rates by channel? How many records for each... Average (mean) gift amounts by channel Page 38 Setting Your Goals Donor Goal Setting Previous Fiscal Year Current Year Giving History 2+ Year Consecutive New Reactivated after Lapse 1 Year Lapsed 2 Year Lapsed 3 Year Lapsed 4 Year Lapsed 5 + Year Lapsed Never Givers Base Donors Conversion Rate Base Donors Number of donors/ alumni within each segment (beginning of previous FY) Number of donors/ alumni within each segment who gave Calculate Conversion rate for each group: Donors/ Base Number of donors/ alumni within each segment (at beginning of current FY) Calculate based on Base X Goal Conversion Rate Goal Conversion Rate Set goal for conversion Rate with each group TOTAL PFY BASE PFY DONORS PFY CONVERSION CFY BASE ESTIMATED DONORS CFY GOAL CONVERSION Page 39 January 19,

21 Examining ROI What s your spend for Retention? What s your spend for Reactivation? What s your spend for Acquisition? Does this align with your goals? Page 40 Data Driven Decision Making Use data to show you where you have diminishing returns Direct Mail (how far back for reactivation) Reinvest existing resources Data can help you lobby for new resources Positions (projections for revenue) Show success as you are building Page 41 January 19,

22 Key Takeways Don t be afraid to use data to help you identify challenges and successes in your program Make sure that the data you share tells the right story for the audience you are sharing it with Predictive modeling helps you narrow a big task into a more manageable one Align your resources with your goals and use data to tell you where there may be waste Use data to drive changes in your program Page 42 Resources Benchmarking: donorcentrics Annual Report on Higher Education Alumni Giving CAE: VSE Survey and Data Miner Predictive Modeling: Peter Wylie Simple Score White Paper: Build Your Own Reporting/Analytics Resources (cost associated) : Tableau Salesforce Books and Blogs cooldata.wordpress.com Fundraising Analytics Josh Birkholz Page 43 January 19,

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