2. Why prospect researchers need analytics
To increase the value of donations in the UK; no overall rise for decades
Building stronger relationships and increasing donor retention
To improve high-value strategy and product offerings (‘missing’ middle donors
and philanthropists)
Maximum HNWI coverage of 50% in screenings, many of the best prospects not
Rich List-ers
It need not be rocket science
The robots are coming to get us: automation is real
3.
4. Picasso, ‘The First Communion’, 1895-6
accessed at www.artexpertswebsite.com/
pages/artists/picasso-gallery.php
7. A Lie That Reveals the Truth
“It took me four years to paint like Raphael, but a lifetime to paint
like a child”: abstraction reveals more than realistic representation
Analytics and modelling seek leverage through stylised,
simplifications of things ‘in the world’
However:
◦ Remember models and analytics are useful but not ‘true’
◦ Epistemology/assumptions of knowledge
◦ Keep the overall objective in mind
8. Building a Team and Culture
A culture of data analytics can be built from the
bottom up or middle out
Key qualities are “curiosity, communication and
common sense”
Clara Avery, Head of Insight at Macmillan: “we
probably called ourselves an evidence-based
organisation for two years before we really
were one” available at http://insightsig.org/wp-content/uploads/2013/11/6a-
Macmillan-love-Insight.-pdf1.pdf
MacDonnell & Wylie: “In
our experience, [improved]
analytics has not come
from the top. It’s come
from staffers who attend
conferences, read books
and blogs on their
weekends and conduct side-
projects on their own
initiative”
9. Drivers of Giving (McCoy, 2013)
Most predictive models require you to first look at a group of supporters who
have already been seen to ‘do the thing’ that you are modelling, eg: make a
major gift, and build a picture of who they are
For your own organisation, you need determine what are the significant,
defining characteristics of giving across the base, ie: the key drivers?
These characteristics might be demographic, behavioural or attitudinal
The elements that make a good supporter may differ from charity to charity.
Certainly the importance, or ‘weighting’, that you put on each element, or
variable, will differ greatly
Accessed at http://insightsig.org/networking-events-20122013/
10. Potential Drivers of Engagement & Value (McCoy,
2013)
Active Committed Giver? Number non-CG Gifts
Tenure of giving Gender, Marital Status recorded?
Current Lifetime Value Number Active Relationships
First & Last Gift non-CG Amount Recruitment Source
Gift Aid sign-up Response Ratios
Questionnaire Response ACORN, Mosaic, Cameo
Maximum Gift Amount Age (capture Date of Birth not Age!)
Proximity to Cause Flags from Wealth Screening
Legacy supporter? ‘Miss’ aged 55+ (for Legacies)
Recruitment Date First gift amount
Email opens & click-through Professional title
Event participation Questionnaire responder
RFV Lifestage
Opt-ins & opt-outs Membership
Property value Velocity of Giving
Average non-CG Gift Amount
Did they inform you of a change of address without being prompted?
Accessed at http://insightsig.org/networking-events-20122013/
MacDonnell &
Wylie: “You have
to start by
properly framing
the question. The
rest is just
technique”
11. Suggested starters
Tenure/continuity of giving
Giving velocity
Recruitment Date
Response Ratios
Unprompted communications
Wealth flags: private bank,
property value, equity sales
First gift amount
Current Lifetime Value
Questionnaire responder
Event participant/volunteer
First gift date-present
This years cash total/av previous three
Date added
Appeals/responses
Sum total no. comms
NO MATHS REQUIRED
NO MATHS REQUIRED
Sum total giving
NO MATHS REQUIRED
NO MATHS REQUIRED
12. A Basic Affinity Model
Principle of analytical thinking more important than specifics of the
method
“Go where the money is, and go there often"
14. Resources
Kevin MacDonnell’s blog: Cooldata
His and Peter Wylie’s 2014 book ‘Score!’ (ISBN 0899644457)
Josh Birkholz: Fundraising Analytics
See list at:
https://www.worldcat.org/profiles/BenRymer/lists/3257763
Join Prospect-DMM: scary but well worth it
https://mailman.mit.edu/mailman/listinfo/prospect-dmm
Twitter: @joshbirkholz @iofinsight @n_ashutosh
15. Pitfalls
Causality; correlation does not equal causation
MacDonnell and Wylie on roadblocks: “conservative nature of our institutions, a
natural preference for intuition and narrative over data and analysis, a skills
shortage, a fear of disruptive change, scepticism over the claims made for
algorithms and a lack of time and resources”
RFV: only part of the picture
Just because you get the result you want might not mean it is accurate!
It all comes back to data quality: ‘garbage in, garbage out’
Complex maths ≠ better results! Judgement, expertise are key
16. Summary
Analytics offers powerful insight using sometimes simple methods
Huge potential to identify wealth and understand affinity, much of which is already in our
supporter base, and a great career move for prospect researchers
And the final word to MacDonnell and Wylie:
“Data analysis is a rewarding, challenging, and above
all fun line of work that will provide much value to
your employer and a stepping stone in your career in
fundraising to you”
Data analytics is important, interesting and timely.
Recent debate over how (and how much) charities communicate with donors, it is for prospect researchers to play a part in the conversation
But what do we actually mean by the phrases data analytics, database mining etc? What are the processes involved? What training do we need?
I’ll run through today:
Principles underpinning analytics and modelling
Why prospect researchers should pay attention to analytics and data
Applied examples of how we can do this and look at a basic affinity model
Some more advanced techniques
Summary & Q&A
None of our organisations are anywhere near meeting the need they were created to address.
‘Civic Core’ will not be around forever
Retention: charities struggle to build strong supporter relationships attrition rates massive, big acquisition budgets, business model that does not recoup CPA for 18-24 months
Many of the best donors & prospects are not covered in screenings or rich lists
Automisation: new processes mean traditional prospect research will not last forever.
Not rocket science: insight through simple techniques
Measuring affinity AND capacity to create strong relationships with donors
Analogy from Ernst Gombrich’s Introduction to Art, quoted by economist John Kay in his book Obliquity
Lets begin in Malaga, birthplace of Pablo Picasso, the greatest artist of the 20th century.
The Picasso Museum is ordered chronologically, which gives a fascinating insight into the development of Picasso’s work.
The early work is detailed and lifelike
It is also prodigious – Picasso was 14 when he painted this!
Detail is sacrificed for expression here
More abstracted and expressive
This piece from the late period captures the subjects melancholy detachment and estrangement more than a photograph would
Picasso recognised the power of abstraction to communicate more than realistic representation alone
‘Stylised simplification’ describes both art and analytics
Picasso abstracted to reveal more
Analytics is, in Picasso’s words, is ‘a lie that reveals the truth’
Beauty is not truth: models, whether paintings or numbers, are not true
How do you know what you know?
Keep coming back to the goal
Evidence driven culture comes first
Martin Squires, Head of Insight for Boots said essential qualities of analysts are curiosity, communication and common sense
Picture shows how Macmillan deal with evidence at each stage of product development
[QUOTE] Culture can be built from the bottom up or the middle out
Build a profile using attitudinal, behavioural and/or attitudinal data, then look outside the group for others who are (or are likely to) ‘do the thing’, ie make a donation.
These profiles vary from organisation to organisation and between appeals and products
Important to pay attention to ‘weighting’ assigned to each value.
Identify affinity / personal & functional connection to the organisation.
Money is a byproduct of the relationship, not vice versa. Direction of causality
[QUOTE] No success without properly understanding the question
Simple and powerful insights into affinity and capacity
Many require only one data point and very little
First two are alternatives to RFV
Tenure can be continuous or not. Density measures gift frequency
Giving ‘type’ = pay method, cheque, internet payments active, direct debits not active
MacDonnell & Wylie: “even the crudest model is an improvement (on guessing).”
Willie Sutton, famous American bank robber said "go where the money is, and go there often"
Many analytical measures require little calculation – continuous years giving, life time value.
Even calculating a ‘giving velocity score’ uses basic arithmetic (divide the total cash gifts of the current year by the average of the previous three years for a score showing how quickly giving is increasing)
But remember: regressions are studied in year 10 at secondary schools…
Beware the spurious relationship
Make friends with your data people. Data handling and cleaning are time-killers.