 Data-led Fundraising… …is about the ‘accumulation of marginal gains’ Dave Brailsford – Performance Director at British Cycling Summary  Wealth screening… …is no longer be just about flagging millionaires 1  Ability to give and propensity to give… …are two totally different concepts!

Indication of a supporter’s wealth should always be looked at in conjunction with their propensity to give You need some indication of wealth… 2 ACORN, Mosaic, Financial Mosaic, historical giving as a proxy for wealth, professional title, house name, property price, Zoopla, high net worth bank sort codes, age, council tax band, social class, surname, etc. …across the whole supporter base to implement an effective mid and high value donor strategy. It is heavily descriptive of value, response, retention, break even point and ROI.

Build Your Own Geo-dem 3 ‘The Case for a Simple, Standard Postcode Sector Classification’ John Whitehead – April 1992 Journal of Targeting, Measurement & Analysis for Marketing 14 distinct classifications for UK Adults, largely defined by wealth: Affluent family sectorsBetter-off retired Older affluent sectorsLess well-off retired Deprived family sectorsAverage middle Britain Ruraletc. If anyone would like a copy please

Loyalty & Engagement 4 Response ratios, average value, lifetime value, etc. MONTHLY retention/attrition reporting LYBUNT Profiling Targeting models Build an engagement score Adrian Sargeant estimates that a 10% increase in retention can result in a 200% increase in supporter Lifetime Value

What does your typical long-term loyal supporter look like? 5 Long-Term Support

If you are short on time, budget or the internal skills necessary to build a regression model, a useful way to accurately weight drivers of engagement or wealth is to apply z-score values from a supporter profile as coefficients: 6 Weighting Variables Wealth Band % 10yr Loyal Donors %Active BaseIndexZ-Score : Wealthy22.318, , | ████ 2: Affluent/Aged or Retired11.59, , | █████ 3: Educated/Mostly Prosperous/Towns/Cities29.123, , | ██ 4: Comfortable Existence A (Families/Young)20.216, , █ | 5: Comfortable Existence B (Aged/Retired)3.52, , | 6: Getting By/Blue Collar, Services & Retail7.46, , ████ | 7: Financially Pressured/Families & Singles4.13, , ██████ | 8: Hard Up/Struggling/Council Flats/Inner City1.81, , █████ | Total , ,993

Z-Score Weightings 7 The z-score shows the weighting and direction (positive or negative effect) of each value with each variable you are considering. Aggregate these z-scores and then band into an overall engagement score: Engagement Score Total High ValueLow ValueNever Given , , , , , , , , ,4622,1548, ,4612,4879, ,8412,94711, ,0423,01412, ,3713,45713,828 Total1,49150,14515,89967,535

Measuring Engagement 8 Take all positive supporter actions.. Giving (measured by recency, frequency and value) Response ratio (number responses divided by number solicitations) No. areas ‘present’ in (cash giving, DD, Legacy, volunteering, etc.) Tenure (with evidence of y-o-y continuous giving) actions (open, click through, response) Membership and specialist communications/publications sign ups Has a valid address, and telephone Opt-outs (downgrade their score) Then build an aggregate score for every supporter to gauge how into you your supporters are This has direct application to your communications planning, supporter development program, pricing strategy and targeting AND can also be used as a variable in itself within your data models

Drivers of Giving 9 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.

Potential Drivers of Engagement & Value 10 Active Committed Giver?No. non-CG Gifts TenureGender, Marital Status Current Lifetime ValueNo. Active Relationships First & Last Gift non-CG AmountRecruitment Source Gift Aid sign-upResponse Ratios Questionnaire ResponseACORN, Mosaic, Cameo Maximum Gift AmountAge (capture Date of Birth not Age!) Proximity to CauseFlags from Wealth Screening Legacy supporter?‘Miss’ aged 55+ (for Legacies) Recruitment DateFirst gift amount opens & click-throughProfessional title Event participationQuestionnaire responder RFVLifestage Opt-ins & opt-outsMembership Property value Average non-CG Gift Amount Did they inform you of a change of address without being prompted?

 Don’t submit cohorts of donors across broad supporter segments with little further targeting as your match rate will suffer. This can also allow you to reduce your costs. Getting the most out of the wealth screening process  Produce a high value wealth propensity score that also includes drivers of engagement (use z-scores?) 11  Submit for screening those with the best scores, going as far down the model as your budget allows

Price Pointing NB: R F & V are not the only variables that can drive an upgrade in giving! 12 Reservation Price Price Elasticity In a Fundraiser’s paradise every supporter will be asked to give at their reservation price and not a penny less. This is price optimisation. Objective is not to set a uniformly high ask level but to match the price to the supporter and in reality we have to connive to estimate the reservation price by testing and modeling.

Testing Price Points 13 This? < £5 £5-£9.99 £10-£19.99 £20-£49.99 Or This? <= £5 £5.01-£10 £10.01-£20 £20.01-£50

Data Quality 14 The higher value your supporter or prospect… …the better your data quality and data management needs to be  personalisation, targeting, 121 approaches

Stuart McCoy Data Strategy Consultant DM Insight (m) ‘The most exciting phrase to hear in science, the one that heralds new discoveries, is not ‘eureka!’ but ‘that’s funny…’ - Isaac Asimov Marcelle Jansen Garrick House 26 – 27 Southampton St. London, WC2E 7RS