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Data Science in action: Helping a major National
Charity to increase revenues
Dr Marcus Brownlow
Dr Inna Kolyshkina
Dr Marcus Brownlow is an advanced analytics and data visualisation professional with 20+ years ofacademic, government and industry experience in the management, analysis and communication ofcomplex data. He has been a SAS software user since 1988 and has delivered data-driven insight inscientific research, telecommunications, insurance and other industries.
He likes the stories that numbers can tell, and likes them more when they have a business or otherrelevance. Depending on the context, the stories can be about risk or opportunity, profit or loss, moneyor people, business or society. The important thing is to be able to convey this information as succinctlyas possible.
He is a Fellow of the Governor's Leadership Foundation and holds a PhD from The University ofAdelaide.
Dr Marcus Brownlow
Dr Inna Kolyshkina is a data science and business analytics professional with 20+ years of governmentand industry experience in the management, analysis and communication of complex data. She hasbeen a SAS software user since 1997 and has delivered data-driven consulting projects in the industriessuch as insurance, banking, telecommunications, Government (State and Federal), transport, FMCG,airlines etc.
Inna is a Founding Chair of Institute of Analytics Professionals of Australia, and Adjunct Senior Lecturerin University of South Australia and holds a PhD from The University of Kharkov, Russia.
Dr Inna Kolyshkina
Bequest modelling for a large
national charity
Background
● A large national charity maintained a register of all its
donors. Some of the donors would leave money in
their Will (bequest).
● The charity tried to identify such donors and ensure
that they get reached by the right amount and channel
of marketing activity.
Client issues
The organisation faced the following challenges:
● Difficulties in identifying donors who might become bequestors and who are the best target
for marketing to
● Risk of donor attrition caused by the donors being over marketed to or receiving wrong
marketing messages
● Limited call centre resources and hence the need to allocate the resources better in order to
achieve “more with less”
● Donor data was of limited use as it was kept in a difficult to work with sources
The client needed to improve their marketing ROI and minimise donor attrition by
optimising their targeting, marketing activities, marketing spend and resource allocation
Client Issues
Project outcomes
Benefits to the client
1. We segmented their donor base on likelihood of bequest.
This allowed the organisation to
● optimise their marketing activities and spend by targeting the right donors
instead of approaching everyone on the list
● optimise resource allocation so only the donors with high propensity to
respond were approached by phone calls and visits
● optimise their donor acquisition strategy
2. We recommended important data improvements such as data storage,
data enrichment that allowed easier insight driving in future
Benefits to the Client
Bequest propensity model More detail on segments quality
How we did itHow we did it
Step 1. Data pre-processing typically in Analytics projects right data pre-processing accounts for 75%
of the project success
● Good data management, but operationally-focussed
● Designed for call centre staffo execute marketing campaigns
o update donor records
● Operational CRM(ish) design, but alsoo donor demographic data
o campaign response
o donation history
o contact history
Data Background
Analytical base table
Matrix containing attributes for each client:
● demographic (age, marital status, suburb …)
● communication (preferences, contact frequency, contact type …)
● engagement (response to various marketing campaigns… )
● donation (frequency, type and timing of donation(s) …)
Data reality
Contacts
Campaigns
Demographics
Donations
All done using data step and Base SAS procedures
Repetitive tasks automated using %macro processing
Structural transformation Stacking: proc transpose
Concatenate and merge: data step
Data profiling proc freq, %macro
Data cleansing and
standardisation
data step
Data transformation and
recoding
data step
Data Pre-processing
Step 2. Analytics modelling
When the data was pre-processed, a combination of predictive
analytics modelling approaches were used to derive insights
from the data by segmenting the donor base into the segments
with similar behaviours and propensity to bequest
Segmentation via Analytics Modeling
