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Data Analytics: A powerful insight into your donors’ giving potential
Insight SIG19th 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
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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
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Internal
Volunteer Information
Research Information
Electronic Screening
Types of Data Sources of Data
Data, Data and More Data
Age
Marital Status
Gender
Business Title
Email address
Business/home phone
Others?
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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?
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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
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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
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£1-£1,249
£2,500-£4,999
£7,500-£12,499
£25,000-£49,999
£125,000-£249,999
£500,000-£2,499,999
0 200 400 600 800 1,000 1,200 1,400
Distribution of data match results
Number of matches
Segmenting Your Data
£1-£1,249
£2,500-£4,999
£7,500-£12,499
£25,000-£49,999
£125,000-£249,999
£500,000-£2,499,999
0 200 400 600 800 1,000 1,200 1,400
Distribution of data match results
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 House26 – 27 Southampton St.London, WC2E 7RS+44 20 3318 4835+44 20 7717 [email protected] www.wealthengine.com
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