1. Analysis Greatest hits – the final countdown 29 November 2011 Robbie Buscombe Individual Giving Manager – Projects and Operations Steven White Director of Operations 1

2. The introduction –About Marie Curie Cancer Care –Data Objectives in 2009 –Foundation Audit The final countdown –The top ten analytical techniques The future The music quiz results 2 Agenda

3. Introduction About Marie Curie Cancer Care and where we were in 2009 3

4. About Marie Curie Cancer Care Marie Curie Nurses The charity is best known for its network of over 2,000 Marie Curie Nurses working in the community to provide end-of-life care, totally free for patients in their own homes. Last year we cared for more than 31,000 terminally ill patients in the community and in our nine hospices. There is a Marie Curie Nursing Service available to 96 per cent* of the UK. We mainly care for people with cancer but we also care for people with other life-limiting illnesses such as dementia, Motor Neurone Disease and heart failure. To access a Marie Curie Nurse, patients and/or their carers should speak to their GP or District Nurse. 4

5. About Marie Curie Cancer Care Marie Curie Hospices Marie Curie has nine hospices It is the largest provider of hospice beds outside the NHS Marie Curie Hospices provide care for patients with cancer and other illnesses and provide support for families and carers, all completely free of charge. Research Marie Curie’s pioneering programme of palliative care research is showing how we can better care for cancer patients. The charity has two centres for palliative care research, The Marie Curie Palliative Care Unit in London and The Marie Curie Palliative Care Institute in Liverpool. It also funds seven fundamental scientific research groups which investigate the causes and treatments of cancer. 5

6. About Marie Curie Cancer Care Supporting the Choice to Die at Home Research shows around 65% of people would like to die at home if they had a terminal illness, with a sizeable minority opting for hospice care. However, more than 50% of cancer deaths still occur in hospital, the place people say they would least like to be. Since 2004 Marie Curie Cancer Care has been campaigning for more patients to be able to make the choice to be cared for & die at home. Funding Around 70%of the charity’s income comes from the generous support of thousands of individuals, membership organisations and businesses, with the balance of our funds coming from the NHS. Our services are always free of charge to patients & their families, which means that in 2010-12, we will need to raise more than £140 million. We spend more than £83 million a year on our care and research activities. 6

7. Where Individual Giving was in 2009 Had developed four appeals and Shine On (quarterly supporter newsletter) as core mailing activity Supported regional and other fundraising departments but did not have access to their data Had a good base of skills across the organisation to facilitate growth BUT… Some datasets where not centrally owned (i.e. not on the main database) Used a complicated RFV selection model – mailing segments with very few supporters Had no structured selection model for non-core mailings e.g. pledger acquisition mailing Generally there was a poor understanding of the content/quality of the data and how it could be used There was a lack of skills across the organisation to handle the required change 7

8. Analysis Objectives in 2009 Due to the recent introduction of the new CARE database, and limited analytical capacity of the previous database, little analysis has been undertaken 1.Provide some essential basic reporting for Direct Marketing campaigns to enable tactical decisions to be made 2.Provide a suite of regular reports that can inform (Fundraising Management Team) of the volume, recency, frequency and retention of supporters at a high level 3.To support the implementation of the MCCC strategic plan (2008-11) by providing robust data on which to base long term investment decisions 4.Ultimately this analysis should be the first step in developing audience segmentation to support development of MCCC’s Stewardship Strategy 8

9. 9 I’m worried that the strategic thinkers in this organisation don’t know or aren’t involved in the database architecture/structure. Everything is in the database - we just can’t get any information out quickly… I use Excel. Everyone is so focussed on their own detail and work in their own silos. Not allowed to say that things don’t work well. Defensive culture is too positive. The 4 different teams that communicate with my mum have no idea of her overall enthusiasm and value to Marie Curie. We are the least report focused organisation I’ve come across. Foundation Audit Spring 2009 – some quotes If we have one record for everyone – people will quickly understand the relevance of the base.

10. 10 Example of audit – the number of supporters lapsing has exceeded the level of new supporters during the years 2004 to 2007

11. 11 1. Hygiene 2. Education 5. Tactical Reporting 3. Fundraising Closer to the Database 4. Single Supporter View 6. Delivery Software 7. Strategy Process Legacies Online Audit outcome – where did MCCC want to be ?

12. The Final Countdown Our analysis top ten 12

13. At number 10 – categorisation SQL roadmap – developing analysis summary tables –Single Supporter View(s) –Financial History Merge –Mailing History Merge –Source Code mapping –Reports (LTV, MASt) 13

14. At number 9 – reporting 14

15. At number 8 – knowledge transfer Transfer of skills / Mentoring Examples of training given in 2011 –LTV Insights –Selection briefs –SQL –Updating reporting tools –Advanced Excel –SmartFocus 15 select contact_number, product, transaction_date, fhd.amount, rank() OVER (PARTITION BY contact_number, product order by transaction_date desc) AS r, row_number() OVER (PARTITION BY contact_number, product order by transaction_date desc) AS w, dense_rank() OVER (PARTITION BY contact_number, product order by transaction_date desc) AS d from financial_history fh join financial_history_details fhd on fh.batch_number = fhd.batch_number and fh.transaction_number = fhd.transaction_number order by contact_number, product, transaction_date desc

16. 16 Example of Excel – free tips guide!

17. At number 7 – Response Uplift 17 RUM modelling looks at populations of the supporter base who respond with different elasticities

18. At number 6 – Lifetime value 18 Presented to Chief Executive Updated in-house Essential metrics and understanding of performance But historical and dependent on costs Introducing Projected LTV …

19. From Basics To Bottom Line 10 to 6 are prerequisites before we can truly influence the bottom line … what makes the biggest difference are 5 to 1 19

20. At number 5 – Projected ROI 20 Identifies key drivers of long term ROI: – Volume – Cost per acquisition for regular givers – Average Direct debit value – Attrition Rates – Suppression rates & Gift Aid penetration Feeds these assumptions into a cash flow model: – plus other assumptions around upgrade, reactivation, cross sell, loyalty costs, financial discount rates etc. but we can start simple! Projects cash flow over 5 year period and shows: – Return on Investment – Breakeven period – Net financial contribution (income – expenditure) Can be used to compare channels and understand sensitivities to key drivers. Many assumptions can be quantified using LTV outputs

21. At number 5 – Projected ROI cont. 21 Shows possible H2H scenario (high churn, high average value). Net income of £2.7m over 5 years and ROI of 2.15 –If Yr1 attrition reduced to 30% then income rises to 3.7m (£1m gain) –If Yr1 attrition rises to 55% then income falls to 1.3m (and 1.6 ROI with breakeven delayed to year 3)

22. At number 4 – Retention 22

23. “The retention tool is fabulous. It allows me to track and monitor no show & attrition rates across all agency & in-house campaigns. This insight into which streams have the best retention & their best cost per acquisition in the long term is consequently a great help for budgeting & strategy.” “The tool identifies “hotspots” during the donor journey by showing attrition at months 3,6 and 12. This will prove extremely useful when reviewing methods, contents & timings of supporter communications. Monitoring other supporter information such as average gift, age, wealth & other sonar profiles will allow a greater insight into targeting regular givers” Yr1 Attrition has increased over recent years: from c.10% pre-2005 To c.25% in recent years 2010 in particular is showing signs of early high attrition Trending towards 20%-30% driven by high proportion of F2F Predictors 2008 Yr1 attrition low at 15% overall: Channel clearly an important factor Sonar and age also shown to be predictive At number 4 – Retention cont.

24. At number 3 – prediction models … “There seems to be little science applied from a segmentation or data modelling standpoint when campaigns are being developed” Data IQ Autumn 2011 article on Closing the Fundraising Analysis Gap 24 8 out of 10 charities who expressed a preference

25. –To simplify the SmartFocus selections and time taken to produce the segments - reduce the number of cells (52 segments for XMS 2008 wave 1) The XMS 2009 selection had 5 segments! –To consider targeting other sub-populations of the database not previously “understood” from a data viewpoint, as likely candidates to respond. Identified ‘good prospects’ within committed givers and regional supporters –To improve ROI - Analysis of the last 5 XMS mailings had shown: 11,745 supporters have responded to all 5 mailings! 23,650 supporters continually being mailed XMS and never responded. Equates to £10k per annum potential saving! Employment of prediction model has been one factor in helping DM maintain good results. A £200k net contribution gain Vs Budget –To transfer knowledge back to MCCC Provision of scoring code Develop the audience selection briefs 25 Campaign Prediction - Background and objectives

26. 26 Net Income from Great Daffodil Appeal Mailing has grown substantially in line with the use of the Wood for Trees Scoring Model Model not used Model used Great Daffodil Campaign – Marie Curie’s most heard of campaign

27. AWA Warm XMAS WARM CNA WARM (CASH ONLY) GDA WARM Pre-model Post-model Strength of model visible across all four major campaigns

28. At number 2 – Legacies A word on the legacy pledger model … 28

29. And this weeks number 1 – duplicates! 29

30. 30 Garbage In = Garbage Out

31. So pop pickers …a recap on our top ten 10.Single Supporter View / Categorisation 9.Measurement 8.Transfer Knowledge 7.Response Uplift Testing 6.Lifetime Value 5.Projected ROI 4.Retention 3.Prediction Modelling 2.Legacies – any science! 1.Data Integrity 10 to 6 are the foundation, 5 to 1 are more tangible and impact bottom line! 31

32. What next for analysis 32 Introduction

33. More on … –Projected ROI –Legacies –Simple segmentation + communications overlay Reviewing the effectiveness of communications 33 What next ?

34. 34 Step 1 – MACRO Simple Segmentation Step 2 – Communication Propensity Modelling (MICRO) Step 4 – Targeting Step 3 – Establish some rules On retention… communications

35. Continue to use –Lifetime Value (historic) –Committed Retention –Projected ROI (Future LTV) 35 On acquisition …

36. Answers to our music quiz 36 And finally…

37. Europe – The Final Countdown 10.The Foundations – Build Me Up Buttercup 9.The Housemartins – Build 8.Supertramp – School 7.Pink Floyd – Money 6.Foo Fighters – All My Life 5.Kaiser Chiefs – I Predict A Riot 4.The Faces – Stay With Me 3.ABBA – Knowing Me Knowing You 2.Wham – Wake Me Up Before You Go-Go 1.Garbage – The World Is Not Enough 37 The artists and titles are …

38. Questions? Thank you… 38