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First Steps in Predictive Modeling & Analytics Presented By: Mark Mathyer, Greater Chicago Food Depository Tr Geoghegan, Museum of Science and Industry Pamela Jenkins, Museum of Science and Industry

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Defining Predictive Analytics Predictive analytics uses various techniques, including data mining, statistics and modeling, to make predictions about future actions. The model will identify patterns and relationships that influence actions. GOAL: Maximize fundraising results Source: http://www.predictiveanalyticstoday.com/what-is-predictive-analytics/http://www.predictiveanalyticstoday.com/what-is-predictive-analytics/

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Do I Need to Be an Expert? Prospect research experience? Statistics experience? Data mining, querying experience? Software expert? Visualization, reporting export? In-depth knowledge of your data?

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Basic Steps Define your project Collect data (two or more resources) Data analysis (cleanse, transform and model) Statistics (validate and test hypotheses) Deploy the model (make business decisions) Source: http://www.predictiveanalyticstoday.com/what-is-predictive-analytics/http://www.predictiveanalyticstoday.com/what-is-predictive-analytics/

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Figuring Out Your Goal Build a model to predict a constituents behavior Which prospects are likely to give a 6-figure gift? Which alumni are likely to respond to telemarketing or email? Which prospects are likely to attend our fundraising event? Design an analysis to describe our donors What are our donor demographics, giving patterns, capacities, affinity, etc.? How can we target more prospects like them? Evaluate a program, mailing, campaign or project Is our annual fund mail campaign effective gaining and retaining donors, increasing gift amounts, etc.?

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What Data Do I Have and Need? Assess your current data warehouse(s) Are the records accurate? i.e. home address field contains the business address Are the records consistent? data entry rules, historical info, etc. Are the records complete? marital status, gender, age, address, giving, event attendance, graduation info, student activities, membership status, relationships, employment, etc. How do I get the information I dont have? Do my colleagues have separate databases or spreadsheets? Purchase data: ages, wealth indicators, interests, ethnicities, households with children, etc.

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Resources and Tools Who can assist, mentor or work with me? Data team/IT team? Annual Giving team? Prospect Research team? What tools do I need? Analytics software in-house modeling Unrestricted access to data Plenty of time Easy to Read Books Fundraising Analytics: Using Data to Guide Strategy, Joshua M. Birkholz (AFP, 2008) Data Mining for Fund Raisers: How to Use Simple Statistics to Find Gold in Your Donor DatabaseEven if You Hate Statistics, Peter B. Wylie (CASE, 2004)

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What We Did and What We Learned 2013 AF Predictive Model

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Cost Savings If following the predictive model, the savings for the mail campaign would be roughly 50%!!! Sample mailing production and postage costs: $10,000 expense reduced to $5,000 to capture roughly the same fundraising total l

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Analysis Profile DifferencesAF DonorGiving Society RE DataGift Funds - Capital/ExhibitsGift Funds - Education & Collections Census DataPopulation More FemalesPopulation More Males Avg House Value $500,000 - $749,999Home Property Value $1 Million and Up Purchasing Power $100,000Purchasing power $500,000 Affected One More than the OtherColumbian Ball GiftFirst Gift NOT AF/MB Member Events GiftDistance from MSI Fundraising Events GiftAge Event Response FlagAction Flag Days Since Last GiftMembership Standing, Active Gender, Male Years Between Gifts (negative) Ethnicity, Unknown Ethnicity, IndianEthnicity, Arab Ethnicity, Jewish Ethnicity, Scottish Ethnicity, African American Ethnicity Lithuanian Ethnicity, Swedish

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Realistic Plan Allot enough time: how does this affect my workload? Build a budget Leadership support when do I ask for the green light Ooops I started and now Im struggling now what?

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Next Steps at MSI Expand the number of analytics users Use predictive modeling and analytics for our membership program Apply predictive modeling and analytics for corporate fundraising (in addition to individuals)

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Follow Up Mark Mathyer Director of Donor Services, Greater Chicago Food Depository MMathyer@gcfd.org Tr Geoghegan Prospect Research Analyst, Museum of Science and Industry TreG@msichicago.org; 773-947-3737 Pamela Jenkins Stewardship and Database Coordinator, Museum of Science and Industry Pamela.jenkins@msichicago.org; 773-753-2762