DCC - 'Win Me Back' - Jarrod Matchett, CPA

1. Win Me Back

2. On a personal note….

6. About Cerebral Palsy Alliance
- One of the largest
disability service
providers in NSW/ACT
providing therapy and
equipment to over 6,000
clients per year
- Largest independent
funder of research into
CP in the word with over
$30M funded since
2007.

7. About Cerebral Palsy
CP is the most common childhood physical disability
40 thousand people are living with CP in Australia
17 million people worldwide

8. What I will cover today
Cash reactivation
RG reactivation
Propensity Modelling and
Data Science
What’s next?

9. Some context…
CPA has:
- 38,000 Active RG’s –
primarily F2F acquired
- 100,000+ Lapsed RG’s
- 30,000 active cash donors
– primarily premium
acquisition acquired
- 100,000+ lapsed cash
donors

10. Cash Reactivation
Start with clean data - clean
data helps you
make better decisions
What you can do:
• Install QAS or equivalent as
part of your CRM
• Use 3rd party providers to
clean your data

11. Cash Reactivation
RTS Management
Don’t just mark donors as RTS – call them as
many donor use it as a way of managing
their mail
Turn what can be an unpleasant experience
for the donor into a great form of donor care.
You will be able to save 15%-20% of
donors you contact
Keep mailing RTS records – 1%-2% will
respond

12. Cash Reactivation
Data Selection - cast your net
wider
Be prepared to accept a lower
ROI for deeply lapsed segments.
Not all segments need to make a
profit on the mailing.
Remember – the additional cost
to mail more donors is minimal.

13. RG Reactivation
For RG reactivation, being across your
data is critical
• Stringently monitor post reactivation
metrics
• Minimise the blind spots in your reporting
Not all RG’s are worth reactivating….

14. What you need to know before reactivating RG’s
• What was the cancellation reason? Donor
or client cancelled?
• How many times have they been asked to
or reactivated before?
• How many successful donations have they
made?
• What was the channel of conversion?
• What was their payment method?

15. Decision Tree Example
• Analysis like this can
be outsourced
• Will help you uncover
what factors lead to
good/poor reactivation
prospects
CanxReasonGroup = Donor
channel2 = F2F
PreviousPayType = Direct Debit
RGsPreReact < 9.5
RGsPreReact < 3.5
PreviousRefusals >= 0.5
DeclineNo >= 0.5
RGsPreReact < 34
DeclineNo >= 0.5
PreviousRefusals >= 0.5
RGsPreReact < 16
RGsPreReact >= 14
RGsPreReact < 12
RGsPreReact >= 16
yes no
1
2
4
5
10 11
3
6
12
13
26
27
54 55
7
14
28
29
58
59
118
236
237
474 475
119
238 239 15
CanxReasonGroup = Donor
channel2 = F2F
PreviousPayType = Direct Debit
RGsPreReact < 9.5
RGsPreReact < 3.5
PreviousRefusals >= 0.5
DeclineNo >= 0.5
RGsPreReact < 34
DeclineNo >= 0.5
PreviousRefusals >= 0.5
RGsPreReact < 16
RGsPreReact >= 14
RGsPreReact < 12
RGsPreReact >= 16
1.1
n=26e+3 100%
0.71
n=15e+3 56%
0.61
n=12e+3 45%
1.1
n=3145 12%
0.41
n=658 3%
1.3
n=2487 9%
1.7
n=11e+3 44%
1.2
n=8985 34%
0.99
n=6501 25%
1.9
n=2484 9%
0.28
n=384 1%
2.1
n=2100 8%
0.92
n=205 1%
2.3
n=1895 7%
3.2
n=2426 9%
3.1
n=2357 9%
1.2
n=339 1%
3.4
n=2018 8%
1.5
n=153 1%
3.5
n=1865 7%
3.1
n=992 4%
2.5
n=307 1%
3.4
n=685 3%
2.9
n=531 2%
5.2
n=154 1%
4
n=873 3%
3.8
n=777 3%
5.9
n=96 0%
8.7
n=69 0%
yes no
1
2
4
5
10 11
3
6
12
13
26
27
54 55
7
14
28
29
58
59
118
236
237
474 475
119
238 239 15
React

16. Propensity Modelling and Data Science
- Propensity modelling
scores each record
based on their
propensity to carry out
the desired action
- Standard RFV still plays
a significant role, but
there may be other
donors worth
communicating with

17. Data that helps propensity modelling
• What is their connection to the cause?
• Do they view and click through on emails?
• Have they participated in an event?
• Have they provided free form feedback in
the past?
• Have they contacted the charity to update
their details?

18. What’s next?
- Taking a longer term
approach to reactivation -
using a multi-channel
approach to re-engage
deeply lapsed donors
before reactivating them
• Use propensity scoring
based on their interactions
and engagement
• Create a pool of donors with
a high propensity to convert

19. Wrap Up/Summary
CASH
• Clean data is key
• Contact your RTS records
• Cast your net wider

20. Wrap Up/Summary
RG
• Data analysis is key
• Not all RG’s are worth reactivating
• Don’t let your phone agencies push you
into widening your selection criteria
without the data to support it

21. Wrap Up/Summary
Propensity Scoring and Data Science
• Can help find pockets of donors worth
communicating with
• RFV is still king, but data science may
help with larger, more mature programs

22. Any questions?