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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

Dr Marcus Brownlow marcus@analytikk.com

Dr Inna Kolyshkina inna@analytikk.com

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