february 2013 ExcErptEd from
Across the generations
How charitable foundations
t their spending rates
investment outlook • charity IT survey 2013 • audit committee assurance • Public Services Act • FRS 102
analysis Charity Finance February 201344boree
lapd donors
In THE LaST issue of Charity Finance, we prented a ca study, ‘Rubies in the Dust’, showing how we ud statistical learning models to boost direct donor fundraising in a charity with £12m per annum
pork
in voluntary income and a large list of donors.
we outlined veral techniques we tend to u in civil society
organisations to predict and classify information; to solve problems such as cost-benefit optimisation of charitable activity; to rebalance a charity’s investment portfolio; or to t rerve-level ranges. we showed that statistical
learning models, known as support vector machines (Svms), are especially good at predicting and classifying individual items of information. we concluded that the predictive ability of Svms is significantly better than conventional ‘rule-of-thumb’ techniques for predicting whether a prospective donor is likely to give or not.
A regular bonanza?
we suspected that applying Svm techniques on higher-value items might graduate to regular giving or legacy pledges, and could be very valuable indeed.
as luck would have it, we soon got an opportunity to test a regular-income example with another civil society organisation; one who income ba stems largely from tens of millions of pounds in memb
ership income each year, through hundreds of thousands of members.
The benefits of good follow-up process for leavers include
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improving data quality and
understanding membership trends, but there is also a key financial benefit; if you get this process right it pays for itlf many times over through income recovery, by persuading a proportion of the leavers to rejoin.
The key point is to follow up swiftly and efficiently. Simple analysis and evidence from other membership organisations proves that you are far more likely to persuade a leaver to rejoin if you
follow up with them swiftly. If you allow two or three months to pass, the chance of success roughly halves. and the financial benefits of rejoiners are substantial. For this large organisation, hundreds of
thousands of pounds in membership income can be generated from rejoiners every year.
when this is compounded by the number of years the average rejoiner stays, the extra income soon runs to millions of pounds each year.
Ca study two: ‘Rust never sleeps’
Becau a leaver is far more likely to rejoin if pursued promptly, it makes n to pursue leavers with rigour. Tho leavers who do not respond to the basic, correspondence approach, but are subquently
contacted with an enhanced approach (eg with a telephone call), are as likely, in the end, to rejoin as tho who respond to the basic approach. However, there is a significant cost to an enhanced approach; it
Ian Harris and Mary O’Callaghan continue their discussion on the u of predictive analytics to boost membership fundraising.
Rust never sleeps
Without innovation, civil society income just
corrodes away
Propensity of rejoining
Total members
Actual rejoiners *
Actual rejoiner rate
High 192168.33%Medium 11,742491 4.18%Low 16,164318 1.97%
figure 1: Total members by propensity to rejoin
010%20%30%40%50%Length of membership (months)
星期一的英语单词C u m u l a t i v e l a p s e r a t e (%)
First time joiners
Rejoiners
1
5
9
13
1721252933374145
49
53
57
Actual
Forecast
figure 2: Comparison of first-time-joiner and rejoiner lap rates
* This analysis was bad on respons to lapsing letters only
analysis
Charity Finance February 2013
45
lapd donors is not viable to attempt to make telephone contact with all leavers who fail to respond to correspondence.
we thought this problem would lend itlf to statistical-learning predictive analytics; which indeed it did. we trained our Svm, PropheZy, using members who had lapd during 2009, including many who had subquently rejoined through the basic follow-up process.
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Key data included gender; date of birth; duration of membership; and various membership groupings and categories specific to the organisation, which indicated particular interests and the degree of involvement in tho interests.
Predictive data
The data proved highly predictive and we were able to construct three bands showing high, medium and low propensity to rejoin, as indicated in figure 1.
PropheZy is not the only tool available if you want to apply
statistical learning through an Svm. Statistical packages such as SPSS, minitab, SaS, R and matlab all offer Svm capability. Indeed, in simple cas with very few variables, even using Excel’s regression functions can be predictive to some extent. one benefit of our Svm,
PropheZy, is that it can cope with gaps in the data, as long as the ‘gappy variables’ have reasonable amounts of data in them.
In this instance, the gender and duration of membership data was very clean, but the date of birth and some of the ‘member interests’ fields had gaps. PropheZy will ignore a variable completely if the data within it is too spar or not predictive. we found even the ‘gappy member interests variables’ were all predictive, so we ud them, even when they were spar.
Leavers with high and medium propensity to rejoin tend to have been members for a relatively long time, but also tend to be younger than the low propensity leavers. also, in this ca, (much to the delight of the finance staff), the high and medium propensity to rejoin leavers tend to be in more expensive membership categories than the low propensity leavers.
applying simple cost-benefit analysis to the results produced some straightforward conclusions: it is unquestionably worthwhile to undertake enhanced, telephone follow-up on high and medium propensity people, but borderline to go beyond the basic follow-up
with the low propensity people. The workflow diagram in figure 3 illustrates the follow-up process when enhanced by the u of
predictive analytics using PropheZy.
chinked
Lap rates
In this particular membership
organisation, the average duration of membership is about ten years. when we started working on follow-up process to generate rejoiners, we assumed in our initial cost-benefit calculations that rejoiners would be more ‘flaky’ than other members, and that we might get five years’ additional membership on average.
However, once we had three years’ history of follow-ups, we looked at the actual lap rates for rejoiners and learned some interesting things, as illustrated in figure 2.
During the first 14 months or so of joining or rejoining, the lap rates are quite similar, with rejoiners
Rejoiners are far more valuable than we had
first anticipated
fi
gure 3: Enhanced workflow of leaver follow-ups using PropheZy analytics
Update databa
records
Monitor and record responssoccer是什么意思
Enhanced approach: telephone or other means of contact (high and medium propensity only)
The parts of the process take place simultaneously
下午好日语Basic approach: nd respon-eking letter to every leaver
Run leavers through PropheZy
Classify leavers (high, medium or low propensity to rejoin)
Deal with respons
Process queries
Key
Basic process
Enhanced process
Exclude tho who respond to basic approach within three weeks
Ian Harris is a director, and Mary O’Callaghan is a nior consultant, with Z/Yen Group
Headline sponsors Marlow, Buckinghamshirethursday怎么读
KEYNOTE SPEAKERS
sars
Allister Heath, editor, CITY A.M. Dennis Turner, former chief economist, HSBC
CHAIR
Andrew Hind, editor, Charity Finance
Spots, Solves, Acts
Z/Yen Group Limited
90 Basinghall Street, London, EC2V 5AY
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