Data Analytics : A powerful insight into your donors’ giving potential Insight SIG 19th February, 2013

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” 2

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.” 3

Data Mining 4 What data is important? What type of data should we collect? Where are the sources for data?

The Devil’s in the Data….  Financial  Biographical  Philanthropic  Behavioral 5  Internal  Volunteer Information  Research Information  Electronic Screening Types of Data Sources of Data

Data, Data and More Data  Age  Marital Status  Gender  Business Title  address  Business/home phone  Others? 6 Biographical Contact Giving History  Last Staff Contact  Event Attendance  Last Solicitation  Amount of Last Solicitation  # of Contacts Overall  # of Contacts in last 3 years, 5 years  Others?  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?

Electronic Screening of Data Q: How do I select the right type of screening for my organization? 7 A: 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: Results  Capacity Ratings  Propensity to Give Ratings/Indicators  Financial Information  Income  Real Estate  Stock Holdings  Gifts to Others  Age/Children  Household Interests 8

Data Analysis vs. Statistical Modeling Statistical ModelingData Analysis Analysis of specific business questions and the development of foundational insights Hypothesis Based Approach 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 Definition Techniques Tools 9

Number of matches Segmenting Your Data

Number of matches More than just the Millionaires……

Filter Criteria: 1) Age > 60 Filter Criteria: 2) Property Value Filter Criteria: 3) Company Director Filter Criteria: 4) Recent Gifts Filter Criteria: 5) Event Attendance Most promising legacy prospects Vendor screen output Internal data Segmenting Prospects into Solicitation Pools

Individual’s record in DMS New Data from Screening Classify by Donor Pool Calculate Ratings & Scores Bring Ratings & Key Datapoints into the DMS Integrate Wealth Intelligence Integrating wealth data into your DMS/CRM helps you:  Prepare for a meeting  Refine ask amounts  Make informed decisions  Automate marketing and donor outreach activities  Save time and streamline workflows

Analytics = Powerful Insight into your Data = Actionable Results Summary  Consider the predictive value of your internal data  Use screening to add external data that increases your knowledge about your prospects  Combine internal and external data to segment your database  Create data-driven donor pools for every fundraising campaign

Marcelle Jansen Garrick House 26 – 27 Southampton St. London, WC2E 7RS Contact Details