Fundraising Intelligence: Data Mining & Analytics RIF Scotland 3 rd October, 2011

Fundraising Intelligence:Data Mining & Analytics RIF Scotland 3rd October, 2011 Marcelle Jansen, WealthEngine

Agenda • Definitions • Data Mining • Bringing Analytics to your Organization • Harnessing the Power of Data through Analytics • A Case Study

Definitions • Data Mining: “the extraction of meaningful patterns of information from databases” • Analytics: “how an entity arrives at an optimal or realistic decision based on existing data” • Predictive Modeling: “the process by which a model is created or chosen to try to best predict the probability of an outcome”

The Goal: Fundraising Intelligence “Fundraising Intelligence can be described as the process of gathering data, turning it into actionable information through analysis, and making it accessible to the right people, at the right time, to support fact-based decision making.”

“In God we trust. All others bring data.” -- Barry Beracha Sara Lee Bakery retired CEO

Data Mining • What data is important? • What type of data should we collect? • Where are the sources for data?

Key Considerations with Data • Rules to Follow: • Clean • Consistent • Structured • Codified • GIGO: Garbage In Garbage Out

Data: Emirates Airlines Flight 407 • Context: Airbus A340-500 • Flight from Melbourne, Australia to Dubai on March 20, 2009 • Flight included 257 passengers and crew of 18 • Events • Aircraft not accelerating normally • Traveled the length of the runway (more than 2 miles) still unable to lift-off • Plane’s tail struck the ground at least 5 times before lift-off • Clipped a strobe light and flattened a navigation antenna as it struggled to gain altitude • Reaction • “This would have been the worst civil air disaster in Australia’s history by a very large margin” Ben Sandilands, Aviation Expert • “They were lucky that…a lot of people (didn’t lose) their lives” Dick Smith, former head of the Civil Aviation Safety Authority What Happen on Flight 407!?!

Data: Emirates Airlines Flight 407 • Flight 407 had four experienced pilots in the cockpit • The captain and first officer completed a preflight checklist including a four-part process cross check • Data entry of plane’s calculated weight: 262 metric tons • Flight 407’s actual calculated weight: 362 metric tons • This is the equivalent of not calculating the weight of 20 African elephants stored in the belly of the plane • The Australia headline said it all:

“The Devil is in the Data” ‘The Devil is in the Data” The Australian, September 12, 2009

The Devil is in the Data: Types of Data • Financial • Biographical • Philanthropic • Behavioral • Other………..

The Devil is in the Data: Sources of Data • Internal • Volunteer Information • Research Information • Electronic Screening

Data, Data and More Data • Giving History • First Gift Date / First Gift Amount • Last Gift Date / Last Gift Amount • Total Giving / Total # of gifts • Largest Gift Amount / Largest Gift Date • Average gift (annual vs. major) • Other factors?

Data, Data and More Data • Relational • Family Ties (legacy alums, multiple family members with affiliations) • Alumni Association or Member • Volunteer Roles • Connection to an organization insider • Product Purchases

Data, Data and More Data • Biographical • Age • Marital Status • Gender • Business Title • Email address • Business/home phone • Others?

Data, Data and More Data • Contact • Last Staff Contact • Event Attendance • Last Solicitation • Amount of Last Solicitation • # of Contacts Overall • # of Contacts in last 3 years, 5 years • Others

Electronic Screening: Making It Work • What should screening my data accomplish? • Identify new prospects • Qualify existing prospects • Prioritize existing prospect pool • Segment prospects into solicitation pools

Electronic Screening Data • How do I select the right type of screening for my organization? • Determine your organizations needs • Do you need to screen your entire database or does it make more sense to screen a targeted sample • Do you want hard asset data? • Or demographic data?

Electronic Screening Data • Types of Data Returned • Geo-demographic Data • Hard Asset Data • Wealth Indicators

Electronic Screening Data: Results • Capacity Ratings • Propensity to Give Ratings/Indicators • Financial Information • Income • Real Estate • Stock Holdings • Gifts to Others • Age/Children • Household Interests

Data Analysis vs. Statistical Modeling Data Analysis Statistical Modeling Definition • Analysis of specific business • questions and the development • of foundational insights that feed • into statistical modeling • Hypothesis Based Approach • (vs. Boiling the Ocean) • Univariate & Bivariate Analysis • MS Excel most commonly used • Building statistical models to • predict desired behaviors • Multivariate Analysis • Linear/Logistic Regression, • Cluster Analysis, etc • SAS, SPSS are most popular Techniques Tools

Modeling: What Do You Want To Accomplish • Major Gift Model • Annual Fund Modeling • Planned Giving Model

Model Variables Independent Variables Dependent Variables • Overall likelihood of giving a gift • Likelihood of giving a gift over $X • Likelihood of being a Major Donor • Likelihood of upgrading a gift • Next Gift Amount • Lifetime Giving Amount • Next Ask Amount • Giving History • RFM & Trend Attributes • Constituent Type • Parents vs. Alumni • Wealth Indicators • Capacity Ratings & Wealth Components • Demographics • Age, Marital Status, Education • Contact Info • Phone number, email address * *RFM corresponds to Recency, Frequency, Monetary Value

A Non-Profit Corporation Illustration • Context – A Public Radio Station • Understand the profile of the organization’s client base • Rank order the client base on desired behavior using statistical models • Determine criteria to identify best prospects in the broader universe • Available Data • Organization data– Biographical, Giving History and Relational • Screening data – Wealth Attributes, Geo-demographic, and • Philanthropic • Objectives • Profiling analysis identified predictors of the desired behavior - Major Gifts greater than $250 • Rank order the client base • Profiling insights were used to develop a custom prospect identification strategy

Dependent Variable Illustration Distribution by Largest Gift Amount • Selected ‘Largest Gift of at least $250’ as the dependent variable • 6,149 donors met this threshold (incidence of 15% in the sample of 41,759 records) • Identified predictive attributes by analyzing across giving, wealth and demographic variables • Metrics used for analysis • Incidence = (# of constituents with largest gift of at least $250)/(total number of constituents) • Distribution = % of total constituents in a segment Dependent Variable Largest Gift >=$250 % of Total Donors Largest Gift Amount

Illustration: Number of gifts and years since first gift are strong predictors of giving at least $250 By Number of Gifts By Years Since First Gift

Illustration: Political giving and number of contact data points also strongly differentiate givers over $250 By Political Contribution Level By Number of Contact Methods

Illustration: Property Value and Giving Capacity scores slope giving behavior (>=250) Property Value Giving Capacity Incidence

Statistical Modeling: Making it Work • How will all this help me determine the philanthropic characteristics of my organization’s donor base? • Allows you to determine which assets have the most impact on organizational giving • Allows you to become more efficient in selecting the best prospects for your programs and organization • Allows you to segment, build and grow your prospect pool to focus on your best prospects

Summary • Fundraising intelligence allows you to optimize your data for: • maximum return on investment, • effective strategy development • efficient fundraising management • It all starts with good data!

Questions?Contact:020 3318 4835mjansen@wealthengine.comwww.wealthengine.com

Fundraising Intelligence: Data Mining & Analytics RIF Scotland 3 rd October, 2011