data modelling in not for profit marketing case studies & discussion david dipple & john...

Data modelling in not for profit marketing

Case studies & discussion

David Dipple &John Sauvé-Rodd

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

2Modeling not for profit marketing Dipple/Sauvé-Rodd

Modeling not for profit marketing Dipple/Sauvé-Rodd 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.

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


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


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


Modelling Examples• Warm modelling– Legacy– Committed giving – Raffles– Upgrade– High Value supporters– Attrition– Reactivation

• Cold modelling– Postal sector– Cold lists– MMP (modelled market potential)


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


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


Legacy Modelling

• Why legacy modelling?

• Legacy marketing currently worth approx £2bn – set to rise to over £5bn £5bn by the middle of the century

• Between 40-60% of legacies left by people who have no (known) relationship with charity in question


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


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


EXIT TO SPSSLegacy modelling


Legacy Model Gains ReportLegacy and Legacy Pledge Model Gains Report

Ntile(20) Legators Pledgers Legacy(%) Pledge(%) Random(%) Legacy (Cum%) Pledge (Cum%) Random (Cum%)1 2481 1409 48.1% 58.7% 5.0% 48.1% 58.7% 5.0%2 1131 341 21.9% 14.2% 5.0% 70.0% 72.9% 10.0%3 517 203 10.0% 8.5% 5.0% 80.0% 81.3% 15.0%4 280 115 5.4% 4.8% 5.0% 85.4% 86.1% 20.0%5 149 55 2.9% 2.3% 5.0% 88.3% 88.4% 25.0%6 110 38 2.1% 1.6% 5.0% 90.5% 90.0% 30.0%7 79 41 1.5% 1.7% 5.0% 92.0% 91.7% 35.0%8 90 34 1.7% 1.4% 5.0% 93.7% 93.1% 40.0%9 60 21 1.2% 0.9% 5.0% 94.9% 94.0% 45.0%

10 67 16 1.3% 0.7% 5.0% 96.2% 94.6% 50.0%11 38 28 0.7% 1.2% 5.0% 96.9% 95.8% 55.0%12 33 16 0.6% 0.7% 5.0% 97.6% 96.5% 60.0%13 53 14 1.0% 0.6% 5.0% 98.6% 97.0% 65.0%14 32 23 0.6% 1.0% 5.0% 99.2% 98.0% 70.0%15 8 12 0.2% 0.5% 5.0% 99.4% 98.5% 75.0%16 4 20 0.1% 0.8% 5.0% 99.5% 99.3% 80.0%17 10 9 0.2% 0.4% 5.0% 99.7% 99.7% 85.0%18 11 3 0.2% 0.1% 5.0% 99.9% 99.8% 90.0%19 7 4 0.1% 0.2% 5.0% 100.0% 100.0% 95.0%20 0 0 0.0% 0.0% 5.0% 100.0% 100.0% 100.0%

Total 5160 2402 100% 100% 100%


Legacy Model Gains Chart


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


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


Attrition Processes

• Read data in• Create activity flags• Create single supporter view of transactional

data• Merge with supporter information• Create attrition curves


EXIT TO SPSSDonor attrition


Activity by AgeAge Band act_2005 act_2006 act_2007 act_2008 act_2009 2005 2006 2007 2008 2009

Unknown 21197 8304 7350 5672 3591 100.00% 39.18% 34.67% 26.76% 16.94%

Under 30 5554 3213 1587 1060 726 100.00% 57.85% 28.57% 19.09% 13.07%

31 to 45 4964 3455 2064 1587 1221 100.00% 69.60% 41.58% 31.97% 24.60%

46 to 55 1499 1136 763 626 527 100.00% 75.78% 50.90% 41.76% 35.16%

56 to 65 715 576 429 351 294 100.00% 80.56% 60.00% 49.09% 41.12%

66 to 75 319 262 198 175 151 100.00% 82.13% 62.07% 54.86% 47.34%

75 plus 197 117 93 76 63 100.00% 59.39% 47.21% 38.58% 31.98%

Total 34445 17063 12484 9547 6573 100.00% 49.54% 36.24% 27.72% 19.08%


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


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


Any questions?

David [email protected]

John Sauvé[email protected]

End


mailto:[email protected]

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