Int. J. Nonprofi.t Volunt. Sect. Mark. 1O: 43-52 (2005)Published online in Wiley InterScience(www.interscience.wiley.com). DOI: 1 0. I 002/nvsm. 5

Pledger modelling: Help theAged case studyKaren Cole,l Rachel Dinglel and Raiesh Bhayani2t Talking Numbers, UKt Help tbe Aged., uK

o The recruitment of pledgers (as a proryfor potential legators) to cbaritable organisations

plays a uital role in tbeir continued success, and as apercentage of allfundraising income

generated it can represent substantial proportions. Hou)euer, of all the 'donation asks'

made of supporters, askingfor a legacy is tbe most dfficult. Tberefore, it is important that

tbe target audience sbould be as utell researcbed and bigltly targeted as possible.

o Help tbe Aged bad reacbed tbe stage ubere decisions need to be made about its futuremarketing in order to protect longer-term income. Tbe findings of tbis legacy targeting

project utere to feed into communication progralnmes, direct marketing, and tbe ouerall

le g acy m ar k e ting strategJ/.o The key objectiue u)as to identifu tlJe best prospects to mail a legaqt ask to, across tbe

supporter database, utitlt tlte likeliltood tbat tbey are going to pledge as a result.

o It uas found tbat ultilst tailored data analtsis comes at a price, tbe auerage ualue of a

legacy justifies the cost of using sophisticated targeting tools. Houteuer, because of tbe

pledge-tr>l.egaqt time lapse, tbere utill ahaays be issues uitb measuring any long-term

return on inuestment (ROI). Nonetbeless, pledgers baue to be taken on tbeir utordfor tbe

purpose of testing (and subsequent rollouts). Pledge data sbould be tested and tbe

outcomes sltould inform legaqt marketing. Houteuer, as mentioned aboue, pledgers

necessarily need tobe taken ontbeir utord and tberefore,formulatingmodekbased on tbe

type and/or ualue of pledges is not recommended.

Copyrigbt A 2OO5 Jobn lYiley & Sons, Ltd.

Background.

The recruitment of pledgers (as a proxy forpotential legators) to charitable organizationsplays a vital role in their continued success, andas a percentage of all voluntary income raised,legacies can represent a substantial proportion.However, of all the 'donation asks' made ofsupporters, asking for a legacy is the mostdifficult. According to Richard Radcliffe (flill,

Correspondence to: Nigel Magson, Talking Numbers,Iarrcure House , MarketPlace, CirencesterGL7 2NW, UK.E-mail: nmagson@talkingnumbers.com

2OO2)'legacy income is likely to go down aswomen in their 80s who are leaving legaciesnow tend to be asset ignorant. . . the nextgeneration will be more aware of the value oftheir estate and may only leave a fixed sum tocharity'.

It is important that the target audienceshould be as well researched and highlytargeted as possible. According to the I-egacyPromotion Campaign website (2OOZ)'65% oftbe population are regular cbarity suppor-ters. Recent researcb bas sltoutn tbat donorsbaue no objection to leauing cbaritablelegacies - tbey simply neuer get around to it.'

Copyright O 2O05 John Viley & Sons, Ltd. Int.J. NonprofitVolunt. Sect. Mark., February 2005

44 K. Cole et al.

25,000,000

20,000,000

15,000,000

10,000,000

5,000,000

Figure l. kgacy income by financial year received.

Ifelp tbe Aged's legacy actiuity

Help the Aged's legacy income was S,12.8mil-lion in zOOL/Oz, (see Figure 1.) which repre-sents a significant part of its total voluntaryincome of 3,75 million. Legacy marketing activ-ities include four direct marketing campaignsper annum. In addition, Help the Aged offers a'Will Advice Service' which, although indepen-dent, does often lead to a pledge for the charify.

The Help the Aged pledger commitmentprogression is as follows:

Enquirers .---+ Considerers .---= Pledgersc=:> Legators

Objectiue

Help the Aged had reached the stage wheredecisions needed to be made about its futuremarketing in order to protect longer-termincome. The findings of this project were tofeed into communication pfogfammes, directmarketing and the overall legacy marketingstrategy. The key proiect objective was toidentify the best prospects to mail a 'legacy ask'to, across the supporter database, with thelikelihood that they are going to pledge as aresult. Secondary objectives wefe as follows:

o Descriptive statistics of legator and pledgerdata

o Centralizing datao Comparative sociodemographic profiles of

supporters through to legators

I

I I I

I I I I

1992/93 1993/94 1994/95 1995/96 1996/97 1997/98 1998/99 1999/00 2000tO1 2001tO2 2002103

Financial year

o Timing of 'legacy ask' and other recommen-dations for implementation.

Metbodol.ogjt and rfltionale

Analysis steps

As with any investigative project, thefe afe anumber of analysis steps to go thfough beforeany sort of modelling can be achieved. Thesemain proiect steps are as follows:

r Describe data- summary statisticso Profile datar Reportingo Modellingo Reporting

The first step in this case involved under-standing the transition - and differences -

between the stages of legator and pledger com-mitment by producing summary statistics on allavailable data. This also allowed for an evalua-tion of the existing strategy by examining anychanges shown by the sufirmary statistics bet-ween groups or over time.

Following this analysis the next stage was toprofile the available data. Profiling is a techni-que that compares gfoups of interest to somesort of base population, for example, the lifestages of legators compared to the GB popula-tion as a whole. Although profiling obviouslyprovides insight in its own right, it is anessential precursof to modelling because itfinds the variables that provide the most

Copyright o 2005John Wiley & Sons, Ltd. Int. J. Nonprofit Volunt. Sect. Mark., February 2OO5

Pledger modelling 45

discrimination befween groups. Talking Num-bers profiled by common database variables, aswell as by some of the external data variablesdescribed later.

Modelling

The final stages of analysis revolved aroundmodelling the data in order to predict futurebehaviour. For this project we had a largedatabase but with very few existing pledgers,making the modelling more difficult. Thepotential of modelling legators was examined(see CTIAID sectionbelow) but the final modelswere built for pledges. A pledge is the desiredfesponse and, as such, is a behaviour themodels should predict. The data on pledges didnot allow the models to be tailored to the typeof pledge (pecuniary or residuary) or the valueof pledge.

It should be noted that even a small gain indirect mail should be cost-effective since ave-rage legacies (depending on type) are up to anaverage of S25,000 (residuary).

The project deliverables were two scoresacross the supporter database, one for cashdonors (including committed givers who havealso given a cash gift) and one for all committedgivers. The scores were based on models indi-cating propensity to become a pledger. Sup-pofters can then be targeted appropriatelybased on variables alreadv known about themon the database.

Talking Numbers used a combination ofmodelling techniques, namely CHAID and logi-stic regression, to produce the models. Ifithinproduction of a regression model many modelswere tested (the dataset size allowed a testsample) in orderto pickthe most discriminatorymodel to be written back to the supporter base.

Talking Numbers also allowed for an inter-active meeting between analysts and Help theAged in-between the profiling and modellingstages. This allowed the preliminary results tobe discussed and any extfa, and important,insight to be provided by the parties actuallyinvolved in collecting and using the data. Thismeeting can be an invaluable contribution toan analysis proiect.

CHAID Method

Chi-square Automatic Interaction Detection(CHAID) is a form of cluster analysis. This iswhere the data set is initially thought of as onelarge cluster and then broken up into a numberof clusters containing related data, aftet exam-ining all interactions. CFIAID can be used tofind the most significant discriminating factorsfor a given dichotomous variable e.g. 1/O oryes/no. CHAID works best with dichotomousvariables.

Uses

CHAID is used extensively in marketing. One ofthe uses of CHAID is to discoverwhichvariablesare best used to describe respondents/non-respondents. Thus segmentation models can bebuilt up for communication purposes.

Output example

Figure 2 shows atree diagram, or dendogrlm,produced using CIIAID. In Figure 2 R is theresponse fate. It can be seen that the responserate for AB males is over 80%, whereas theresponse rate of DE females is only 17%.Consequently, AB males are deemed a betterprospect than DE females.

Logistic regression

Regression techniques are a class of statisticalmethods in which one dependent variable -

the response - is related to one or moreindependent variables - the predictive orexplanatory factors. A regression model isone in which the response variable is linearlyrelated to each explanatoryvariable, i.e. wherethere is a straight-line relationship between theresponse and each explanatory variable there islinear regression. For example, children'sweight is often related to their height whereweight increases as height increases, this is astraightline relationship and linear regression.Simple linear regression is where there is only asingle explanatory variable and multiple regres-sion is where there is more than one explana-tory variable. Logistic regression is applied as atechnique when the response variable is of a

Copyright ar 2O05 John Viley & Sons, Ltd. Int. J. Nonprof.t Volunt. Sect. Mark., February 2005

46 K. Cole et al.

Figure 2. CHAID tree response analysis.

binary type, that is, yes,/no, has responded/hasnot responded etc.

Available data

Data were provided by Help the Aged fromtheir donor and legacy databases. Many differ-ent fields were extracted in order to allowmaximum interrogation: cash and committedgiving including Adopt a Granny databasedonors, legators, pledgers, considerers andenquirers.

Iri addition, Talking Numbers investigatedexternal data to allow cold modelling and addvalue to the existing donor information.

MONICA (CACI)

MONICA is CACI's age classification based onsophisticated analysis of millions of first namestaken from lifestyle questionnaires. Monicahelps to identiry the age of people on a databaseby looking at the likely age profile of their firstname, it also allows new donors to be targetedby matching a distinct age prof,le.

PRIZM (Acxiom)

The PRIZM postcode lifestyle-based segmenta-tion and targeting tool assigns GB consumersinto one of 6O unique clusters. The data hasbeen aggregated up to full postcode level. ltscore data sources include:

o Lifestyle census - information derived fromthe merger of three companies: Claritas,

NDL and CMT, that includes over 40Ovariables, including income, age, householdcomposition and lifestyle

o Company directors - supplied by Dun andBradstreet

o Share ownership - a list of public andpivatized shareholders from NDL's 'Active

lnvestor File' databaseo Behaviourbank - CMT's database contain-

ing details of financial products, shoppinghabits, holiday preferences, appliance own-ership and property types

o Unemployment rates - from the Depart-ment of Employment

o Birth and death rates - from the Office ofNational Statistics

o Flat, farm and house names - from thePostcode Address File @AFl

o Census data-from the Office of NationalStatistics.

Every GB postcode was assigned a five-char-acter code based on the key marketing driversof life stage and income. The first and secondcharacters indicate the following life stages.

PA: Young no children (starting out, youngsingles and childless couples)

PB: Families (nursery and school-agechildren)

PC: Empty nesters (and older singles)PD: Retired seniors.

Each of the four life stages was then brokendown into four income indicators:

Copyright o 2005John Viley & Sons, Ltd. Int. J. Nonprofit Volunt. Sect. Mark., February 20O5

Pledger rnodelling 4 7

1. Most affluent households2. Mid-high affluent households3. Mid-low affluent households4. Least affluent households.

Then a unique cluster code dffierentiatedbetween households that had the same lifestage and income band in tems of lifestyle, forexample PA31 1 : Young Conservationists:

o Young marriedr Own four-wheel driveo Likely to have club cardso Support national causeso Support animal welfare charities.

StreetValue (CACI)

StreetValue provides Lnavetagevalue for everypostcode, the number of properties and pre-dominant type of property pertaining to apostcode area. StreetValue also shows if theproperties in a particular area are mainly own-occupied or rented. These data combinedeventually allowed two models to beproduced:

o Cash database donors (active and lapsed)o Committed giving database donors (active

and lapsed).

Find.ings

Summary statistics

The data exploration stage highlighted theissues with small ovedaps across the donor and

legator databases. The different types ofdonor,howevef, were still compared. Also discoveredat this stage was the short time span of theenquirer to legator cycle. This meant thatthere were few donors found in the mid-cyclegfoups, such as considerers. Also, completelife cycles were available for only a fewindividuals. However, each piece of theenquirer, considerer, pledger and legatorcycle was examined in its own right andsufirmary information produced. For example,Figure 3 shows this informationforthe legatorcycle.

Profiles

Legators and pledgers have a very similar lifestage profile to each other. They are older thanthe other donor groups. Other cash supporters(those who have not made anylegacy contact)show a younger age profile, similar to con-siderers and enquirers, while committed giversare different again and areby far the youngestgroup. The affluence profiles are different,however. The legator profile most closelymatches that of the other cash supporters andcommitted givers (those who have not madeany legacy contact). In terms of affluence,pledgers are similar to considerers andenquirers comprise the least affluent group.Pledgers are more likely to be aged 50-59(MONICA) and retired seniors GRIZND. Theresults from the StreetValue analysis wereinteresting but did not show enough differ-entiation to use in a model,

Financial year

Figure 3. Number of legators by financial year of notification date

1992/93 1993/94 1994/95 1995/96 1996/97 1997/98 1998/99 '1999/00 2000101 2001t02 2002t03

Copyright to 2005John Wiley & Sons, Ltd. Int. J. Nonprofit Volunt. Sect. Mark., February 2OO5

48 K. CoIe et al.

Pledger profiles are interesting in terms oftheir cash-giving history. Nearly half of allpledgers (who are also cash supporters) arein the top value band. The average value of cashgift is a very important factor in the pledgercash model. Pledgers are the group with thehighest frequency profile and frequency ofcash gift also appears in the final pledger cashmodel.

Geograpbical patterns

The geographical spread of the number oflegators was examined and this showed adistinct Southern bias to the pattem, withScotland in particular providing very smallnumbers of legators. A geographic analysis ofthe average legacy values was also undertaken.This time, a pattern emerged whereby theaverage legacy values rose in correlation tothe geographical affluence of each Lrea,particularly in the South East. Additionally, anumberof interesting hot spotswere identffiedincluding the 'retirement' areas of Torquay andBrighton.

CHAID

After running tables and comparing profiles,CHAID was used as an exploratory techniqueto investigate the relative importance ofdifferentiating factors and how those factorscombine to provide the most differentiation(and hence prediction). CHAID explorationwas carried out looking at prediction of bothpledgers and legators; rnd avalTable variablesincluded cash-giving history, legacy contactmade and external data such as MONICA andPRIZM. As there is a clear relationship betweenthe life rycle groups, i.e. legators are likely tohave been pledgers, etc., it was decided tomodel pledgers and not a combination ofgfoups.

After the profiles were studied, it alsobecame apparent that two models would beneeded, one for cash donors and one forcommitted givers. These two groups werequite different in profile and it was expectedthat they would behave very differently. There-

fore one model for both groups would havebeen inappropriate.

Modelling: cash donors

The same modelling approach was used forboth cash donors and committed givers. Theprofiling results were studied and variables thatmight become predicted factors were high-lighted. Then both CHAID and regressiontechniques were used to refine this list untilthe best predictors in combination wereselected for the final models, and their respec-tive levels of importance were allocated.

This methodology produced the followingfactors in the final cash donor model:

o Other relationships/donor status/enquiriesmade

o MONICA agebando Cash-giving history, value and frequency.

The 'gains' achieved by the model are evidentas half of all pledgers were found in the first(righest scoring) 3O% of cash supporters, asshown in Figure 4andTable 1.

Modelling: coflrmitted givers

The final model for committed givers includedthe following factors:

o Length of relationshipo Other relationshipso PRIZM, both life stage and affluenceo MONICA.

Over half of all pledgers were found in the top25% of committed giving supporters as shownin Figure 5 andTable2.

Implementatiom

Writeback

Each score effectively ranked the indMdualsaccording to their propensity to pledge. Thesetwo scores were appended to about 5OO,OOOrecords on the donor database at Help the

Copyright O 2005 John rlfliley & Sons, Ltd. Int. l. Nonprofit Volunt. Sect. Mark., February 2005

Pledger modelling 49

60%

oE 50o/oE

40%

No/o

no/o

10%

0%

Figure 4. Cashpledger model gains chart.

Aged. Talking Numbers worked with Helpthe Aged to plan mailing volumes using themodel - bearing in mind that certain donorsand donor groups would be excluded frommailings due to suppressions or othef activity.The aim for the next year was to test the modelswithin the direct marketing prografirme, com-bined with selections based on edsting donorsegments.

Table 1. Cash pledger model

Spring 2003

The February 2OO3 legacy campaign wasmailed to 28,000 cash donors, split into eightequal mailing segments according to standardRFV criteria and pledger propensity score. Tworandom 3,500 samples, labelled Band 1 andBand 2, were taken for each of the RFVsegments - Band 1 comprised those donors

NTILE* Pledgers Model (%) Random (%o) Model cumulative (%) Random cumulative (%)

I23456789

10l ll 2r3r41 5r6t 718r920Total

216216206r93159101r62r49r55r451 1 0777r56631 3867

692,t82

9.909.909.448.857.294.637.426.837.ro6.655.O43 .531 2 q

2.572.490.60o.37o.27o.323 .16

100.oo

5.005.005.005.005.O05.005.005.OO5.OO5.005.005.O05.005.005.005.O05.005.005.OO5.00

100.00

9.9019.8029.2438.08+>. t /50.oo57.4264.2571.3678.0083.O486.5789.8392.3995.2895.8896.2496.5296.84

100.oo

5.00to.o015.0020.0025.OO30.0035.0040.oo45.OO50.0055.0060.0065.OO70.oo75.OO80.o085.0090.0095.00

100.00

.An NTILE is an equal sized group of ranked d^ta, in this case the data is split into 2O equal sized groups.

Copyright o 2O05 John I(iley & Sons, Lrd. Int. J. Nonprofit Volunt. Sect. Mark., February 2005

50 K. Cole et al.

E oov"

8. mv"op +ou

450/o 55%

Contact

Figure 5. Committed giving pledger model gains chart.

with a pledger score in the bottom 5O% of themodel, and Band 2 comprised those with ascore of the next best z0%.lt had been decidedpreviously that those with a score in the top

30% should not be tested in this the firstcampaign, but 'saved' for later campaigns.

Summer 2OO3

To further test the effectiveness of the pledgermodels for targeting potential legators, therewere two campaigns in Summer 2003. Help the

Table2. Committed giving pledger model

Aged mailed donors with a high propensity topledge and those within the same database seg-ments with a low propensity to pledge (5O/5O

split). Significant pledger results woe found for

both models as can be seen in Tables 3 and4.

Moving forward

2OO3 in summaty

The results for 2o03looked promising in termsof the effectiveness of the pledger models. It

NTILE* Pledgers Model (%") Random (%) Model cumulative (%) Random cumulative (%) Index

I2J

4)6789

l0l lt 27 3141 5r617l8r920Total

10362\ )2738372 l22r92ar6187

t tl 2r39072

504

20.4412.30ro.325.36/ . >+7.344 . r74.37) . / /5 .563 . r75 . > /r .392 .La2.382.58r .790.00r .39o.40

100.oo

5.OO5.005.OO5.OO5.OO5.OO5.005.005.005.00) .UU

5.005.O05.O05.O05.O05.005.005.005.00

100.oo

20.4432.7443.0648.4155.9563.2967.467r.8375.608 1 . 1 584.3387.9049.299r.4793.8596.4398.2r98.2r99.60

100.00

5.OO10.0015.0020.0025.OO30.0035.0040.oo4r.oo50.0055.O060.0065.O070.oo75.OO80.o085.0090.0095.00

l00.oo

4.o91 )'7

2.872.422.242 . t rr .931.801.68r .62r .53r.46r.371.31r .25t . 2 lr . 1 61.091.051.00

*An NTILE is an equal sized group of ranked datt, rn this case the data is split into 20 equal sized groups.

Copyright o 2OO5 John Wiley & Sons, Ltd. Int. t. Nonprofi.t Volunt. Sect. Mark., February 2005

Pledger rnodelling 51

Segment

Table 3. July 2003 (committed giving model) o Telemarketing channel as tested in 2OO3may be included in the modelling.

There will be two models again as thebehaviourof cash donors and committedgiversare clearly different. The inclusion of high-value donors and others excluded previouslymay increase overall response rates. 2003'snew creative model will again be used, afterresponse rates and research showed itseffectiveness.

2004 actiuity

August 2OO4 committed givers mailingSeptember 2OO4 cash givers mailing (total

70,ooo)September 2004 outbound follow-up call tomailed committed givers.

Conclu.sions

The Legacy Promotion Campaign (2OO2) states:'Over the past 12 years the percentage of thosewills going to probate containing a legacy tocharity has remained constant at arcund l3%.This is despite all the resources that charitieshave put into legacy marketing, indicating thatthey only succeeded in competing for a share ofthe same wills. There is evidence that whilstthe number of people making wills is increas-ing, the proportion of those that include acharitable legacy is in decline'.

This makes careful targeting through directcommunication even more critical. Targetingwill help ensure charities' legacy income ismaintained or even increased. Targetingfurther ensufes cost-efficiencies since carefulselection means the most likely prospects arecommunicated to with a proposition thatthey're more likely to accept.

\Thilst tailoreddataanalysis comes at a price,the average value of a legacy iustifies the cost ofusing sophisticated targeting tools. However,because of the pledge-tolegacy time lapse,there will always be issues with measuringany long-terrn return on investment (ROD.Nonetheless, pledgers have to be taken ontheir word for the purpose of testing (and

Score Low High LowNo. mailed 7,5OO 7,5oO 3,500Pledger response 19 56 9Response rate O.25% O.75% 0.26%

HiCh2,444

) 4

0.94o/n

CG: committed givers segment.AAG:'Adopt A Granny' committed givers make a S,l2 permonth (approximately) regular gift.

Table 4. August 2OO3 (cash model)

Segment Active Activea

O

ScoreNo. mailedPledger responseResponse rate

Low6,oo2

8o.13%

should be noted that there were other factorsthat would have influenced the results. Forexample, Help the Aged tested a new creativepack alongside the cash pledger model. As partof ongoing analysis and testing of the modelsthere was a need to re-run the models on theentire donor database, including new donorsand high value donors who were excludedpreviously, to enable bettertargeting of donorsn2OO4. A full post-campaign meta-analysis for2OO3 has also been suggested so that thenumber of responses across the groups couldbe come mofe statistically robust. A meta-analysis is a statistical way of combining morethan one set of test results together in order togive the analyst latger numbers of responderson which to base the analvsis.

Neut model

o Z0D3post-campaign meta-analysiso New strategy for 2OO4 targeting, including

new channels (telemarketing)o Examine the profiles and update the learning

irrz003 for new pledgers

HiCh7,OO3

25O.36o/o

High7,OO3

26o.37%

copyright o 2005 John \ffiley & sons, Ltd. Int.l. Nonprofit Volunt. Sect. Mark., February 2005

52 K. Cole et al.

subsequent rollouts). Pledge data should betested and the outcomes should inform legacymarketing. However, as mentioned above,pledgers necessarily need to be taken on theirword and therefore, formulating models basedon the type and/or value of pledges is notrecommended.

References

Hill N. 2OO2. Q&A: legacies. Tbe Guardian,October lL,2OO2.

Legacy Promotion Campaign Website. 2002. www.

legacypromotioncampaign.org.uk [May 2OO4].

copyright @ 2005 John l(iley & Sons, Ltd Int. I. Nonprortt Volunt. Sect. Mark., February 2OO5