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
9. Forget complex relationships – simplicity is your friend
Analysis follows the 80/20 rule
◦ 80% of the analysis can be done in 20% of the time.
◦ The last 20% takes 80% of the time
10.
11. Binary Clustering: Charity Sector
Humanity
3rd Word &
Overseas
Environment
Disability
Cancer &
Nature Health Medical
Research
Wildlife
Animal
Welfare
13. Traditionally many legacy campaign have been designed and
devised around a message they are not shaped around supporters
needs and requirements
To fully tap the legacy potential of the base a more supporter lead
strategy would match supporter interests and propensity to legacy
message
14. Method
◦ Mail
◦ Phone
◦ Event
◦ Online
The halo effect
15.
16. Behavioural
Recency, Frequency,
Value, Forms of help.
Segmentation
Demographic Attitudinal
Lifestage, Age, Gender Questionnaires,
Geodems Interests & Beliefs
17. Payment Type Interests
Amount Lifestyle
Date Cause
Name
Address
Gender
LTVs Donor & Age
RFVs Demographic Income
Scores Details
Media codes
Responses
Method
19. Geo-Dems are great for cold and certain aspects of
warm targeting
For small population analysis they tend to be less
useful
◦ For one model that I created by using a geo-dem it added
0.5% to the power of the model
Take care with including or excluding people based on
their geo-dem coding
20. Academic Centres, Students and Young Acorn Description
Professionals
Personicx Retired - Low income - Aged in the City
Description Suburbs
21. People tend to be interested in people
◦ But why are they interested?
◦ What aspects of your cause excites them?
◦ What motivates them to give you money?
22. What data do we currently have?
◦ What is its quality
What data would we like to have?
◦ What barriers are there to getting it?
23.
24. But what type of model?
◦ Legacy
◦ Pledger
◦ Legacy & Pledger
◦ Residuary/Pecuniary
The past determines the future
◦ Lifetime Model
◦ Time Limited Model
◦ Something Else
25. SPSS
Excel
FastStats
MapInfo & MapPoint
My own software
27. Type of Data
◦ Number of Relationships
◦ Supporter Lifetime
◦ Number of Gifts
◦ Age of Supporter
◦ Gift Aider
Time is not our friend!
28. Beware of False Relationships
Gender Response Age Response
Male 8% Young 12%
Female 10% Old 12%
Population
Response: 10%
Gender: Male Gender: Female
Response: 8% Response: 12%
Age: Young Age: Old Age: Young Age: Old
Response: 15% Response: 5% Response: 10% Response: 16%
29. c
Clas sification Table
Predicted
a b
Selected Cas es Unselected Cas es
Legator Percentage Legator Percentage
Obs erved 0 1 Correc t 0 1 Correc t
Step 1 Legator 0 776 134 85.3 908940 153597 85.5
1 173 725 80.7 83 272 76.6
Overall Perc entage 83.0 85.5
a. Selected c as es sel_var EQ 1
b. Unselected c ases sel_v ar NE 1
c. The cut value is .500
Multiple ways of understanding if a
model has worked. Most of the output
can be ignored by non statisticians
and the key – The key is finding what
needs to be communicated to
marketers and in what form. used to
determine power.
30.
31. Selected
High Score Supporters
Even with a small
population outcome
models – test down
the model to reduce
the Tom Smith effect.
Model Score
32. Building legacy models has so far been carried out by
building statistical propensity models. These need
previous results to determine what will happen.
But if there are no previous results you can’t build a
model or can you?
33. The factors that increase propensity to make a pledge
or leave a legacy are fairly well know – as we saw
earlier
Create binary flags for each of the data items given
earlier and then add them all up. The higher the result,
the more likely to make a pledge (and it works).
34. Analysis of a legacy campaign tends to be point based,
That is how many responded to being contacted
To truly understand the effect of legacy campaigning
the relationship over time needs to be examined,
including the effect on non legacy messages – that is
the full supporter journey
35.
36. Message 1 Message 2 Message 3 Message 4
Single model that
determines both
who should be No Contact
Model
contacted and with (at this point…)
what message.
Warehouse
37. 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 - There is no one 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
Define the question and the answer will be much easier –
remember a model is not a panacea
