Hidden Potential- Using Data to Raise More Money

Dr. Greg LeeGreg@fundmetric.com

Chris SteevesCsteeves@fundmetric.com

Hidden Potential:Using data to raise more money!

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

Dr. Greg LeeGreg@fundmetric.com

CHRIS SteevesCsteeves@fundmetric.com

fundmetric.com902-233-8243