Hidden Potential- Using Data to Raise More Money
- 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