Summary

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

2. You need some indication of wealth… …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. • 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. • Indication of a supporter’s wealth should always be looked at in conjunction with their propensity to give

3. Build Your Own Geo-dem ‘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 sectors Better-off retired Older affluent sectors Less well-off retired Deprived family sectors Average middle Britain Rural etc. If anyone would like a copy please email JohnWhitehead@wateraid.org

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

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

6. Weighting Variables 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:

7. Z-Score Weightings 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:

8. Measuring Engagement • 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) • Email actions (open, click through, response) • Membership and specialist communications/publications sign ups • Has a valid address, email 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

9. Drivers of Giving • 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.

10. Potential Drivers of Engagement & Value Active Committed Giver? No. non-CG Gifts Tenure Gender, Marital Status Current Lifetime Value No. 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 Average non-CG Gift Amount Did they inform you of a change of address without being prompted?

11. 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?) • Submit for screening those with the best scores, going as far down the model as your budget allows • 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.

12. Price Pointing Price Elasticity NB: R F & V are not the only variables that can drive an upgrade in giving! Reservation Price 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.

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

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

15. ‘The most exciting phrase to hear in science, the one that heralds new discoveries, is not ‘eureka!’ but ‘that’s funny…’ - Isaac Asimov Stuart McCoy Data Strategy ConsultantDM Insight 07980 767566 (m) stuartdmi@ymail.com Marcelle Jansen Garrick House 26 – 27 Southampton St.London, WC2E 7RS +44 20 3318 4835 +44 20 7717 8483 mjansen@wealthengine.com www.wealthengine.com