A Lie That Reveals the Truth

This is adapted from a presentation I gave last week for Researchers in Fundraising, titled ‘Introducing Data Analytics’.  Enjoy!

Prospect researchers should care about analytics for many reasons.  There has been no rise in charitable donations in the UK or US for decades; charities urgently need to build stronger relationships with supporters; only half of high-value individuals are covered in wealth screenings; it need not be rocket science, and automation is real, and prospect researchers must remain relevant.

The presentation shows what analytical thinking is using the example (originally from Ernst Gombrich’s ‘Introduction to Art’, quoted in John Kay’s book ‘Obliquity’) of Pablo Picasso.  The Picasso museum in Malaga (where he was born) is ordered chronologically, and the level of realistic detail in his work decreased through his career.  He abstracted more to reveal more; he became more expressive by using ‘styilised simplifications’, a term which also describes quantitative models.  To paraphrase him, Picasso ‘lied to reveal the truth’.

Building a team and culture are central to using analytics and being evidence-based.  As Martin Squires, Head of Insight at Boots the Chemist, said at the Insight Special Interest Group conference in 2014, the essential qualities for an analyst are more than anything “curiosity, communication and commons sense”.  This culture can be built from the bottom up or the middle out.  As Clara Avery (Clara Avery interviewee on this site) has said, Macmillan were “probably calling ourselves an evidence-based organisation for two years before we were one.”  Slide eight below shows how Macmillan use evidence at each stage of the innovation process, a process which, as Clara says, took time to grow but is now established.

In terms of methods, analytics for fundraising often identifies a group of supporters, profiling them using behavioural, demographic and/or attitudinal data and looking to the wider population of supporters to try to identify those with a similar profile.  Different organisations will have different supporter profiles; likewise, appeals and products will have different ‘typical’ supporters.  Slide 11 of the deck shows some of what I think are particularly insightful data points, none of which need any great numeracy to work with.  Indeed, none of the methods I’ve quoted in the slides needs advanced numeracy, let alone a background in statistics or econometrics.  The slides in the deck here are from a great Stuart McCoy/Marcelle Jansen presentation from the IoF Insight special interest group.

Analytical methods don’t need to involve statistical packages, and one of the points of the presentation is that we can gain significant insight using Excel and other widely-used packages.  In my example, I create a simple summary spreadsheet with weighted measures (scored at 1-10) of affinity and capacity.  These are pulled through into the first tab to create a score, indicating overall likelihood to give at a major level.  Some of the major indicators of affinity and capacity mentioned in the slides are:

  • Giving tenure/Continuity of Giving: the length of time a donor has been donating to your organisation, and how continuous this giving has been.  Continuous giving is great, but a high hit rate can also be really useful to measure
  • Giving ‘velocity’: basically the uplift in giving.  Dividing the current years total giving by the average of the previous three is a good way of doing this; another method is called ‘Compound Annual Growth Rate’ (CAGR)
  • Recruitment date: date of first contact with your organisation.  Interesting to contract this with the first gift date
  • Response ratios: rule of thumb is that people responding to more than one in 10 of your appeals is pretty engaged.  This is simply the total number of appeals divided by the number of responses
  • Unprompted communications: how often are supporters contacting you with being prompted?  Updating changed addresses, responding to surveys or questionnaires, signing petitions…all good signs of engagement
  • Wealth flags: setting up alerts for equity sales, or first-time donors who work with wealth managers are just two examples of how screening can be part-automated to help discovery through analytics
  • First gift amount: big first gift amounts are always to be followed up on
  • Current Lifetime Value (LTV): a great measure of potential and engagement, and very simple to calculate
  • Event participant/volunteer: again, simple to measure and a really strong signal of affinity and connection

More advanced methods of analytics include:

  • Regressions: these are a family of mathematical methods which aim to discover how important given variables are in a given situation
  • Text analytics: this uses software to scrape websites to carry out ‘sentiment analysis’, ie how users feel about a product or topic
  • Algorithms: an algorithm is a mathematical model to represent the relationship between variables
  • Automated scoring and screening: using business rules to automate database processes of screening
  • Machine learning: a branch of Artificial Intelligence which aims to teach computers to recognise logic, humour or other complex concepts

And before we get to advanced methods, a few resources to get prospect researchers started in using analytics:

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: scarily advanced at times but well worth it: https://mailman.mit.edu/mailman/listinfo/prospect-dmm

Twitter: @joshbirkholz @iofinsight @n_ashutosh, @mpellet771, @mueggenburg

Finally: some potential pitfalls for those of us looking to use more analytics.  The main ones in my mind are:

  • Just because you find the answer you want(ed) doesn’t mean it is the right one.  Correlation does not equal causation.
  • Various factors quoted by Kevin MacDonnell and Peter Wylie in their great book ‘Score!’: “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”
  • A popular method in non-profit donor analytics is called ‘recency, frequency and value’ (RFV for short).  For me, this is part of the solution in understanding who is engaged with you organisation, but often leads back to those giving regular gifts by direct debit.  RFV therefor gives important insights, but is not the whole picture
  • With data, it is still true that ‘garbage in, garbage out’.  Take care of your data!  It will pay you back
  • Complex maths ≠ better results! There is no substitute for your expertise, judgement and attention

And the final word to MacDonnell and Wylie, who give a great summation of why prospect researchers should move into analytics as soon as possible:

“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”

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