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OUTLINE:• What is data?
• What data should you collect?
• What can data do for you?
• How do you analyze data?
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
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
Sample DataLAUNCH GROWTH MATURITY
DIRECT EMAIL• Opt- in email list• Professional
association lists• Symposium &
events
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
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
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
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
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
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
In Excel…• Excel spreadsheets with pre-entered formulae
In Excel…• Can do this with various statistics
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%
Recency/Frequency/Monetary
Creating an RFM Summary Using Excel: http://www.brucehardie.com/notes/022/RFM_summary_in_Excel.pdf
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
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
Machine Learning is Easy• Predict whether a given person has cancer
• Difficult problem
• Can build a predictor with 97% accuracy • “No”• Not useful
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
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
Decision Tree
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…”
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
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
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
Strange Data Examples• Big Bang radiation
• Ozone layer hole
• UPS route changes
• Canada Post
• Paralyzed veterans
