The document discusses the importance of data in fundraising, outlining what data to collect, analyze, and how to keep it clean for better insights. It emphasizes the use of quantitative and qualitative data, and introduces machine learning as a means to predict donor behavior and campaign effectiveness. Ultimately, it provides strategies for leveraging data to enhance fundraising efforts and make informed decisions.

1. Dr.GregLee
Greg@fundmetric.com
ChrisSteeves
Csteeves@fundmetric.com
Hidden Potential:
Using data to raise more
money!

2. OUTLINE:
• What is data?
• What data should you collect?
• What can data do for you?
• How do you analyze data?

3. What Is Data?
• Pieces of information (One piece = a datum)
• Can be qualitative or quantitative
• Age = 34 Quantitative
• Demeanor = Happy Qualitative
• Quantitative is the easiest to work with
• Qualitative can be categorized
• “Friendly” = 2
• ‘Aggressive” = 1

4. What Data is Useful?
• Most data is useful
• Anything that can be used to distinguish between donors
• Or events
• Or appeals
• Anything that you would like to know about donors
• Or events
• Or appeals

5. Sample Data
LAUNCH GROWTH MATURITY
DIRECT EMAIL
• Opt- in email list
• Professional
association lists
• Symposium & events

6. What Data to Record
• Good Features
• Split data in interesting ways
• Gender, age, location, date, income
• “Bad” Features
• Provide little information
• Name, ID number, phone number
• “Growing Features”
• Email, address, postal code

7. Dirty Data
• Data that must be “cleaned” in order to be processed
• ID’s that are not unique (duplicate records)
• Mixed up collumns
• Ambiguous terms
• Missing fields
• Campaigns referenced in multiple ways
• “Fall fundraiser 2013”
• FF2013

8. Keep Your Data Clean
• Enforce standards
• Unique ID’s
• Defined names (for campaigns, events, appeals)
• Include fail-safes
• Search for duplicates
• Emphasize the importance of data to everyone
• “That’s not important”
• Disconnect between data entry & data analysis

9. What Can Data do for You
• Increase your fundraising knowledge
• With respect to your particular area
• That’s nice, how does that help?
• Saving money through:
• Targeted campaigns
• Eliminating unprofitable campaigns

10. Simple Analysis
• “We are drowning in data but starving for information’
• John Naisbitt
• We want to make informed insights from data
• To do this you need years of training in statistics, data
processing and machine learning
• Not really

11. Simple Analysis
• What is the average donation?
• Within a given campaign
• Within a geographic area
• Within a gender
• What campaigns generate the most new donors?
• Which are best at keeping donors?
• Numbers can surprise you

12. In Excel…
• Excel spreadsheets with pre-entered formulae

13. In Excel…
• Can do this with various statistics

14. Recency/Frequency/Monetary
• Sort your donors by:
• Recency: The last time they donated
• Frequency: How many times they’ve donated
• Monetary: How much they have donated
• Bucket donors in each category:
• 5 buckets
• Donor X is R=4, F=3, M=5
• 80% of donations come from top 20%

15. Recency/Frequency/Monetary
Creating an RFM Summary Using Excel:
http://www.brucehardie.com/notes/022/RFM_summary_in_Excel.pdf

16. Sophisticated Analysis
• Basic statistics give valuable information
• Historical information
• But what if we want to predict what donors will do?
• Or how profitable a campaign was
• Patterns in data can provide statistical bias for predictions
• Machine learning can find these patterns

17. Machine Learning
• A subfield of artificial intelligence
• A computer finds patterns in data & predicts based on them
• Sometimes are understandable to humans
• Other times, it is hard to tell
• Can only work with the data provided
• Except when expert knowledge is included
• Generally classified into two categories:
• Classification
• Regression

18. Machine Learning is Easy
• Predict whether a given person has cancer
• Difficult problem
• Can build a predictor with 97% accuracy
• “No”
• Not useful

19. Machine Learning is Hard
• Requires useful data
• Features relevant to the program
• If they help distinguish between donors
• Not always clear what a “relevant” feature is
• Beware of red herrings/correlation
• “85% of repeat donors have their favourite colour as blue”
• Make everything blue

20. Decision Tree
• A flow chart
• Used to classify input
• At each step:
• Pick a feature of the input
• Pick a value of that feature that splits the data
• Split the data

21. Decision Tree

22. Decision Tree
• Tree is an output of the tree algorithm
• Algorithm splits data on information gain
• Whatever divides data in a meaningful way
• “If you tell me how old he/she is I can tell you…”

23. Machine Learning Algorithms
• Linear regression
• Fit a line to data
• Artificial Neural Networks
• Mimics the brain, neurons “fire’
• Bayesian Learning
• Uses prior probabilities to infer probabilities
• Clustering
• Puts similar data together in groups

24. What’s the Point?
• Machine learning algorithms output a model
• We feed the model new data
• And out pops a prediction
• Learn a model to predict planned giving
• Use it to predict which donors to approach about this

25. What Can I do With the
Results?
• Predict which donors to steward
• Or which not to waste time on
• Predict which campaigns will make money
• Predict which events to run
• Find patterns that you didn’t know were there
• Confirms patterns you thought were there
• Defy conventional knowledge

26. Strange Data Examples
• Big Bang radiation
• Ozone layer hole
• UPS route changes
• Canada Post
• Paralyzed veterans

27. Dr.GregLee
Greg@fundmetric.com
CHRISSteeves
Csteeves@fundmetric.com
fundmetric.com
902-233-8243