Download to read offline
![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”](https://image.slidesharecdn.com/introducingdataanalytics15062015-150618000625-lva1-app6891/85/Introducing-data-analytics-15062015-8-320.jpg)
Uploaded byBen Rymer
Introducing data analytics 15062015
Introducing Data Analytics This presentation discusses how data analytics can help prospect researchers increase donations by better understanding donors and identifying new prospects. Analytics can improve donor retention and targeting of high-value donors. While models simplify reality, they provide useful insights if used properly. Building a data-driven culture requires curiosity, communication and treating analytics as an ongoing learning process rather than a top-down mandate. Key drivers of giving vary by organization but may include factors like donation history, wealth, engagement, and responses to appeals. Basic affinity models can identify patterns in existing donor data, while more advanced techniques like regressions and machine learning improve targeting of new donors. Analytics offers a promising career path for researchers but also risks like overreliance on correlations
Introducing data analytics 15062015
- 1. Introducing Data Analytics BEN RYMER PRESENTATIONFOR RESEARCHERS IN FUNDRAISING 16TH JUNE 2015
- 2. Why prospect researchersneed 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
- 4. Picasso, ‘The FirstCommunion’, 1895-6 accessed at www.artexpertswebsite.com/ pages/artists/picasso-gallery.php
- 5.
- 6. Jacqueline with Crossed Hands,1954, http://www.artexpertswebsit e.com/pages/artists/picasso- gallery.php
- 7. A Lie ThatReveals 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 Teamand 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 ofEngagement & 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 ofgiving 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 AffinityModel Principle of analytical thinking more important than specifics of the method “Go where the money is, and go there often"
- 13. More advanced analysis Regressions TextAnalytics Algorithms Automated scoring and screening Machine Learning
- 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 doesnot 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 powerfulinsight 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”
- 17.
Editor's Notes
- #2 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
- #3 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
- #4 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.
- #5 The early work is detailed and lifelike It is also prodigious – Picasso was 14 when he painted this!
- #6 Detail is sacrificed for expression here More abstracted and expressive
- #7 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
- #8 ‘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
- #9 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
- #10 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.
- #11 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
- #12 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
- #13 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)
- #14 But remember: regressions are studied in year 10 at secondary schools…
- #16 Beware the spurious relationship Make friends with your data people. Data handling and cleaning are time-killers.