David Dipple is a recognized expert in data modeling who has worked with charities for over 25 years. He discusses the challenges of creating acquisition and retention models, including defining success, measuring long-term impact, and dealing with limited data. Effective models require understanding a supporter's full journey and matching messages to their interests, not just running campaigns. The acquisition and retention processes must work together to maximize supporter potential over their lifetime with an organization.

1. David Dipple

3.  Fellow of Royal Statistical Society
 Worked with Not For Profit and Charity Clients for over
25 years
 Recognised as an expert data modeller
 Trained numerous analysts and fundraisers in the use
of analysis in fundraising
 Worked with charities in UK and mainland Europe

4. An approximate answer to the right question is worth a great deal more
than the precise answer to the wrong question.
-The first golden rule to applied mathematics
The formulation of a problem is often more essential than its solution
which may be merely a matter of mathematical or mental skill.
•A. Einstein

6.  This is an off heard cri de coeur coming from marketers
and fundraisers
 We need to be able to target our supporters for….
◦ …. acquisition
◦ …. retention
◦ …. a legacy ask
◦ …. an upgrade
◦ …. an event
◦ …. something else

7.  We need to build a model to target all of our
supporters
 And need it by next Wednesday
 Then I need to know if it has worked a soon as possible
 What do you mean you need responses to do a
response analysis!

8.  The acquisition manager tends to like prospects who
come from sources where there is a high response rate
 The retention manager tends to want supporters who
are going to be worth lots of money over their
“lifetime” with the organisation
 Are the requirement even compatible?
 Is it the fault of the Acquisition manager if the
Retention manager can’t get the new recruits to do
anything?

10.  One issue of creating models is: how to measure
success?
 The success of acquisition is often based on single
point based statistics such as ROI or CPR/CPC (Cost per
Response/Cost per Contact)
 Consider………

11. Which would you prefer?
A group that cost (A) £37 or (B) £50 to recruit?

12. The 18-35s cost £37 to recruit and the 56+ cost £50 to recruit.
The Year 1 value of the first group was c£50 and the second
group c£85

13.  The highest recruitment costs came from the sources
that gave the highest levels of upgrade/survival
 The lowest survival rates came from sources with the
lowest cost per response

14. Supporters recruited by two different channels –
Which is best?
Recruitment Stats
Recruitment Recruited Total Value Average value ROI
Channel 1 5000 £60,000 £12 1.1
Channel 2 4000 £40,000 £10 0.9

15. Retention Stats
Recruitment Year 0 year+1 year+2 Year+3 Year+4
Channel 1 100% 30% 20% 10% 10%
Channel 2 100% 70% 50% 30% 30%

16. Cumulative Value
Recruitment Year 0 year+1 year+2 Year+3 Year+4 Initial ROI 5 Year ROI
Channel 1 £60,000 £78,000 £90,000 £96,000 £102,000 1.1 1.87
Channel 2 £40,000 £68,000 £88,000 £100,000 £112,000 0.9 2.52
(Based on Broadsheet and Tabloid Data)

17. In the first example there was a
lot of cancelled regular gifts and
so how long do you need to wait
to see if the campaign/groups are
going to be successful?
In this example 80% of the people
who were going to cancel had
done so by 6 months from
recruitment.

18.  So some of our main measures of success from
determining if an acquisition model has succeeded are
flawed - if not downright misleading!
 If measuring success is an issue - we also have to
define who we want to target!

19.  One of the biggest issues with Acquisition models is data, or
should I say the lack of it.
 This means that Acquisition targeting can be a bit of a scatter
gun approach based mostly of channel
◦ “These channels have worked before and so are likely to work again.”
 Within the mass marketing channels there can be a certain
amount of targeting:
◦ List names
◦ Press Titles
◦ Geography & Geo-Demographics
◦ Etc.
 With lifestyle databases more individual data can be obtained
but this type of data can tend to be more expensive

20.  Geo-demographic data does tend to work well for cold
targeting, but don’t necessarily expect to get a person
who is the same as the profile description.
Academic Centres, Students and Young Acorn Description
Professionals
Personicx Retired - Low income - Aged in the City
Description Suburbs
Are they both right? Or wrong? Or what?

21. Your (and Their) Audience
3rd World &
Humanity
Overseas
Environment
Disability
Cancer &
Nature Health Medical
Research
Wildlife
Animal
Welfare
You are tend to be fishing in the same pond
with other charities in the same sector

23.  Retention should be easier than Acquisition as the
population is now defined rather than an amorphous
mass
 But it is not quite that easy…..
 ….. which bit of the population and whereabouts in the
timeline of the supporter journey does it exist?

24.  Typical strong descriptors
◦ Number of Relationships
◦ Supporter Lifetime
◦ Number of Gifts
◦ Age of Supporter
◦ Gift Aider

25. Our Legacy Prospects
Or
Sally is in her 50s and gave her first Bob, a single man in his thirties,
gift 3 months ago after her has been a supporter for 10 years.
children left home. He is a committed giver and has
donated a number of cash gifts.

26. Young Supporter: Middle Age: Mature Supporter:
Chief concern Likely to have more Most likely to have
getting them to relationships than multiple
give again. Little the young supporter relationships but
known about and a greater depth also been asked to
person. of data known about do everything.
supporter. Most known about
this supporter.
Time with Organisation

27. Potential of Action
Time with Organisation

28. Potential of Action Extent of Data
Time with Organisation

29.  New to organisation with low known data density - often
restricted to: source of recruitment, amount (prompted) and
form of help (cash, cog, event, lottery etc.)
 One of main challenges is to get them give again
 This group is the one that is most likely to need additional
information about you and you about them
 Should be considered as part of the acquisition process until
they become mature

30. Time to second action
100%
90%
80%
70%
60%
Cum(%)
50%
40%
30%
20%
10%
0%
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24
Months

31.  These have been supporting for some considerable time
 They are likely to have been asked to do most things and
convert to different forms of help (and upgrade).
 The known data density for this group has the potential to be
high, however for vey long standing supporters (especially
lapsed) the data density can be very low due to lack of data
collection historically.
 Lots of data but likely to have been asked multiple times.

32.  This supporter was recruited a long time ago. But…
◦ Are you still using the same recruitment methods?
◦ Is the profile of your supporters the same now as it was then?
◦ Do you have different propositions or products now?
◦ There could be lots of data but, what is the quality and
completeness of the data?
◦ Has any of the data been overwritten in the time that the
supporter has been with you?

33. Potential of Action Extent of Data
Future Potential
Time with Organisation

34.  These supporters have been with you with some time (2-5
years)
 Will have given again and/or support using an additional form
of help
 They have a medium density of known data due to multiple
gifts and more likely to have appended data (age etc.) and
have possibly been asked to fill out a questionnaire
 Multiple responses can be used to determine areas of interest
 More data would be nice but there is sufficient to start
building models

35.  This can often be considered the “sweet spot”
 Supporters have been with the organisation to have data
connected with them
 They are likely to have been contacted at least once with most
offers:
◦ Conversion to other form of help
◦ Upgrades
◦ Legacy (hopefully)
 So, a good population to test models and hypotheses on and
close to the “new kids on the block”

37. The retention models then
use the colours to form the
database picture
Your acquisition models add
the colours to use in your
database

38. But the final result is not fixed and
can be altered by the fundraising
artist to reflect changes in attitudes
and requirements.

39.  Acquisition and retention should not be seen as
separate entities: they are heavily interlinked
 The acquisition brings in the prospects that are going
to be used during development and retention
 It is a good idea to understand the potential for
particular groups early on, using segmentation and
some basic forecasting so that the correct retention
strategy can be used
 Retention strategies/journeys should not be set in
stone and beak points should be considered based on
actual supporter behaviour

40. Combine the acquisition population and basic information known at or about recruitment
with survival and value to get a forecast of likely value over time. This can be carried out for
both regular giving and cash supporters. Usually a one and three year forecast will give you
valuable insight of the likely performance (here the client wanted 7 years!).

41.  Traditionally most fundraising campaigns have been
designed and devised around a message: they are not
shaped around supporters’ needs and requirements
 To fully tap the fundraising potential of the base a more
supporter-led strategy would match supporter interests and
propensity to fundraising message

43. Message 1 Message 2 Message 3 Message 4
Produce models
that determine
both who should be No Contact
Model
contacted and with (at this point…)
what message.
Using clustering,
segmentation and models
Warehouse
that go beyond the basic
yes/no results

44.  Acquisition and retention models need to work together in order to
get the most out the supporters.
 Create a supporter-led rather than a campaign-led marketing approach
 The biggest barrier to producing efficient models is lack of data –
especially demographic and attitudinal data
 Understand what the data is saying and then use an appropriate model
and population - there is no single perfect solution
 There is no certainty in modelling – models are built from past
behaviour and if you change what you are doing it can take a while for
the data to catch up
 Examine the whole supporter journey to understand the full
relationship and don’t rely on point based statistics
 Define the question and the answer will be much easier – remember a
model is not a panacea

45. david.dipple@adroitinsight.com