Supporter Attrition within Regular Giving at PDSA Victoria Barham Senior Marketing Analyst

Regular Giving at PDSA  Multiple Regular Giving Products Traditional Regular Giving, Cashcascade and Sponsorship All with different Supporter Profiles  Multiple recruitment channels Direct Mail, Web, Door to Door  Multiple Payment Plans and Frequencies Weekly, Monthly, Quarterly, Annual … Any day of month Annual Value Ranges from £1 to £2,700  Multiple Payment Methods Direct Debit, Standing Order, Cash, Card… 2 =10 million rows of complex commitment payment data room for mis-interpretation the need for a robust, programmable, yet simple technique

Introducing Survival Analysis PDSA use Descriptive Survival Analysis to measure the cancellation of regular giving commitments over time, this produces a survival curve, that can be sliced and diced by any definable criteria. What is Survival Analysis? Allows analysis of time to (recurrent) events, lending itself perfectly to regular giving Why use Survival Analysis? –Can analyse commitments together, irrespective of signing date, date of first payment etc. as all commitments are aligned together, back to time 0 ( date of first payment) –You do not need the same volume in your comparison segments –Consistent methodology that can be applied across multiple regular giving products and produces output understood by non analysts –You don’t need to be a statistician to apply the method to your data –You can automate the calculations allowing the easy refresh of regular reports 3 Descriptive Calculates a survival curve Allows comparison of survival curves Allows prediction of the monthly volume of regular givers expected to attrite Predictive Allows specific identification of which supporters are likely to attrite Allows identification of the key variables influencing survival

Tools for Descriptive Survival Analysis Began with SPSS Survival Analysis Module, but have successfully applied technique into regular reports using FastStats Discoverer, FastStats Excelsior and Microsoft Excel 4 Derive age of Commitment* Date of First Payment Date of Last Payment (or date of analysis if still active) Run frequencies by age of commitment and required categorisation (campaign, product etc) Started commitments Attrited commitments Import FastStats cubes and trees into Excel using FastStats Excelsior Calculate survival curve in excel * We measure this in months rather than number of payments due to the mix of products and payment plans

Survival Analysis…. By Regular Giving Product (This is fictional data!) 5 A misleading statistic that can not be used to compare products as not all commitments began at the same point in time Non Starters are removed before analysis, but need to be considered in further applications Product C is an established product with more history to analyse Product E is a relatively new product Health Warnings : Know your data to make sure you are making fair comparisons… Has Product D poor survival because of the product, or because of the recruitment channel? Would we use this these survival curves to predict survival of newly recruited commitments? (For forecasting, PDSA limit analysis to commitments recruited in last 3 years)

Survival Analysis…. By Demographics (This is fictional data!) 6

Survival Analysis…. Applications Information and Insight –Regular Reports using excelsior update survival analysis with ease, providing up to date information on performance of regular giving campaigns, products, segments –Inform targeting decisions –Has been most useful in understanding the effect of economic climate of regular giving Scenario Planning –If I had £50,000, where is it best spent? –Applying survival curves to assumed campaign performance statistics results in an income stream and break even point Regular Giving Budgeting Process –Applying survival curves to existing commitments and expected acquisition has proven to boost budgeting accuracy and streamline process Predicting Individual Supporters Likelihood to Attrite –PDSA currently use CHAID to identify Supporters at risk of attrition, but are investigating the application of survival analysis 7

Thank You! Victoria Barham Senior Marketing Analyst