1. Data modelling in not for profit marketing Case studies & discussion David Dipple & John Sauvé-Rodd

2. Who are we? John Sauvé-Rodd – Director of Datapreneurs – Lifetime dataholic – New grandad David Dipple – Fellow of the Royal Statistical Society – Consultancy Director of Tangible Data 2 Modeling not for profit marketing Dipple/Sauvé-Rodd

3. 3 Fundraising by charities in the UK is a substantial business. Nearly £10 billion a year is raised by non-profits from the mega- large to tiniest organisations. In larger charities the deployment of predictive models has been found to add effective impact to marketing operations. This presentation is from two long-time charity data practitioners: model maven David Dipple and dataholic John Sauvé-Rodd. It is based on case studies and modelling methods with SPSS syntax that will be demonstrated live. Specifically we'll show models for (1) legacy marketing and (2) donor attrition.

4. Overview Data analysis and is a key area in the NFP sector as recruiting new supporters is becoming increasingly difficult The use of data analysis can make all the difference when trying to improve recruitment, retention and activity 4 Modeling not for profit marketing Dipple/Sauvé-Rodd

5. Modelling Modelling in NFP terms can be a much looser term than in other arenas Refers to techniques from classical propensity to basic value and frequency models 5 Modeling not for profit marketing Dipple/Sauvé-Rodd

6. Key propensity modelling methods Binary logistic is often seen as the preferred method But CHAID often used due to the graphical output Discriminant used where the more advanced modelling module has not been purchased 6 Modeling not for profit marketing Dipple/Sauvé-Rodd

7. Modelling Examples Warm modelling – Legacy – Committed giving – Raffles – Upgrade – High Value supporters – Attrition – Reactivation Cold modelling – Postal sector – Cold lists – MMP (modelled market potential) 7 Modeling not for profit marketing Dipple/Sauvé-Rodd

8. Challenges Modelling often seen as a cost rather than an investment Fundraisers often more interested in the creative side of campaigning rather than the data aspect Data and information 8 Modeling not for profit marketing Dipple/Sauvé-Rodd

9. Data Challenges Data mostly comes from a marketing data base Data is often lacking in demographic and attitudinal data Time based information often lacking Data structures not designed with analysis and modelling in mind Data is heavily skewed Data often siloed – not a single supporter view Lots of rules of thumb present 9 Modeling not for profit marketing Dipple/Sauvé-Rodd

10. Legacy Modelling Why legacy modelling? £5bn Legacy marketing currently worth approx £2bn – set to rise to over £5bn by the middle of the century Between 40-60% of legacies left by people who have no (known) relationship with charity in question 10 Modeling not for profit marketing Dipple/Sauvé-Rodd

11. Challenges Data a mix of categorical, ordinal and continuous Low number of target audience Data not present on large number of legators Data missing for key factors such age/date of birth A large number of prospects who have not had time to build up relationship with organisation Time based data can cause issues 11 Modeling not for profit marketing Dipple/Sauvé-Rodd

12. Processes Read data in Create single supporter view using aggregates Recode “missing” data so that the whole of the target supporter base can be used Recode, band and label data Create a selection variable so that a balanced model can be created Run model (many times) Examine confusion matrix Take “best” score and then produce ntiles Output results and produce a gains report 12 Modeling not for profit marketing Dipple/Sauvé-Rodd

13. EXIT TO SPSS Legacy modelling 13 Modeling not for profit marketing Dipple/Sauvé-Rodd

14. Legacy Model Gains Report Legacy and Legacy Pledge Model Gains Report Ntile(20)LegatorsPledgersLegacy(%) Pledge(%)Random(%)Legacy (Cum%)Pledge (Cum%)Random (Cum%) 12481140948.1%58.7%5.0%48.1%58.7%5.0% 2113134121.9%14.2%5.0%70.0%72.9%10.0% 351720310.0%8.5%5.0%80.0%81.3%15.0% 42801155.4%4.8%5.0%85.4%86.1%20.0% 5149552.9%2.3%5.0%88.3%88.4%25.0% 6110382.1%1.6%5.0%90.5%90.0%30.0% 779411.5%1.7%5.0%92.0%91.7%35.0% 890341.7%1.4%5.0%93.7%93.1%40.0% 960211.2%0.9%5.0%94.9%94.0%45.0% 1067161.3%0.7%5.0%96.2%94.6%50.0% 1138280.7%1.2%5.0%96.9%95.8%55.0% 1233160.6%0.7%5.0%97.6%96.5%60.0% 1353141.0%0.6%5.0%98.6%97.0%65.0% 1432230.6%1.0%5.0%99.2%98.0%70.0% 158120.2%0.5%5.0%99.4%98.5%75.0% 164200.1%0.8%5.0%99.5%99.3%80.0% 171090.2%0.4%5.0%99.7% 85.0% 181130.2%0.1%5.0%99.9%99.8%90.0% 19740.1%0.2%5.0%100.0% 95.0% 20000.0% 5.0%100.0% Total51602402100% 14 Modeling not for profit marketing Dipple/Sauvé-Rodd

15. Legacy Model Gains Chart 15 Modeling not for profit marketing Dipple/Sauvé-Rodd

16. Does it make a difference? “ In terms of ROI, this has undoubtedly been the best legacy marketing campaign that Barnardo’s have run. Income is estimated at almost £12.5 million. This compares with estimated income of £5m in Jan 08 and £10.9m in Jan 07” Client quote 16 Modeling not for profit marketing Dipple/Sauvé-Rodd

17. Attrition Understanding giving patterns is vital to being able to predict future behaviour and value Attrition analysis can be used to understand this behaviour by channel of recruitment, demographics etc so that future investment can be properly targeted 17 Modeling not for profit marketing Dipple/Sauvé-Rodd

18. Attrition Processes Read data in Create activity flags Create single supporter view of transactional data Merge with supporter information Create attrition curves 18 Modeling not for profit marketing Dipple/Sauvé-Rodd

19. EXIT TO SPSS Donor attrition 19 Modeling not for profit marketing Dipple/Sauvé-Rodd

20. Activity by Age Age Bandact_2005act_2006act_2007act_2008act_200920052006200720082009 Unknown211978304735056723591100.00%39.18%34.67%26.76%16.94% Under 305554321315871060726100.00%57.85%28.57%19.09%13.07% 31 to 4549643455206415871221100.00%69.60%41.58%31.97%24.60% 46 to 5514991136763626527100.00%75.78%50.90%41.76%35.16% 56 to 65715576429351294100.00%80.56%60.00%49.09%41.12% 66 to 75319262198175151100.00%82.13%62.07%54.86%47.34% 75 plus197117937663100.00%59.39%47.21%38.58%31.98% Total34445170631248495476573100.00%49.54%36.24%27.72%19.08% 20 Modeling not for profit marketing Dipple/Sauvé-Rodd

21. Why is it important? By understanding attrition and activity the organisation can calculate more accurate lifetimes and expected income values at the time of recruitment 21 Modeling not for profit marketing Dipple/Sauvé-Rodd

22. Conclusions Propensity models can create a huge difference for targeting prospects But big wins can also be made with more basic analytical techniques The key challenge is to educate the analyst about what the results are to be used for and the fundraiser what the analysis and data can do for them 22 Modeling not for profit marketing Dipple/Sauvé-Rodd

23. Any questions? David Dipple ddipple@Tangibledata.co.uk John Sauvé-Rodd johnrodd@aol.com End 23 Modeling not for profit marketing Dipple/Sauvé-Rodd