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How Analytics Can Enhance Fundraising

Presentation to

thankQ User Group Forum

Wednesday 4 March 2015

Lawrence Jackson

Professor Ujwal Kayande


John Wanamaker’s famous last words on advertising …

John Wanamaker, US Department Store

Proprietor. 1838-1922

Half the money I spend on advertising is wasted. The trouble is I don't know which half. I spend $2 million on advertising each year and I don’t know if that is half enough or twice too much.


How Obama used micro-segmentation to target swing voters

The problem

Voters do not wear uniforms! Most elections are won by a few percentage points only. How to identify potential swing voters, and target them with the “right” message?

Step 1

Deep needs survey


• Large survey in the US

  • 3,000 respondents

• 140 questions about deep needs, beliefs and values

  • What do you envision for the future? (fear, hopes, safety, etc.)

  • How do you define success? (family, financial security, community respect, business success)

  • Skills and assets you believe you will need to achieve success?

  • “Taking care of the country’s children should be our #1 priority”

  • “Country should do whatever it takes to protect the environment”

  • Etc.

• Which party they voted for in the last elections


Step 2

Need-based segmentation


• Found 5 segments

  • “Extending opportunities to others” (37%)

  • “Working within the community”

  • “Achieving independence”

  • “Focusing on family”

  • “Defending righteousness” (16%)

• Further segmented into 10 “tribes”


Step 3

Focusing on swing voters


• “Extending opportunities to others”

  • Keyword for Democrats. No need to “convince” them

• “Defending righteousness”

  • Pure-blood Republicans. Don’t bother

• Potential swing voters:

  • “Working within the community”

  • “Achieving independence”

  • “Focusing on family”


Step 4

Discriminant analysis


• Bought discriminant data (descriptors) from various commercial database companies

• ChoicePoint

  • Tax records, court rulings, birth and death records

  • Used for checking resumes, loan, credit card, etc.

• Acxiom

  • Shopping and lifestyle data on 200,000,000 Americans

  • Value of their house, magazines subscribed, books bought, etc.

Then used this data to predict segment memberships

• You are more likely to be a hard core Democrat if…

  • You have a cat

  • You eat sushis

  • You have an Apple computer

• You are more likely to be a hard core Republican if…

  • You have a dog

  • You drive a Pontiac

  • You own a gun

  • You go to church


Step 5



Suppose you have a tight race in the eastern district of North Carolina

  • Go to Acxiom

  • Buy addresses of 20,000 people in that region who are likely to belong to the “Working within the community” segment

  • Send a specific direct mail talking about the Democrat promise to help local groups working with the community


  • Check what TV programs the “Focusing on family” segment members watch, and when, in that region (e.g., games, TV shows)

  • Buy TV spots during these programs

  • Advertise plans for children’s education, family welfare…

Did it work?

Of course..…

How scoring can be used in direct marketing fundraising

Key figures

  • A large charity sends a direct mail solicitation to its donors for its Christmas campaign

  • A few key figures:

Scoring model

They used several choice models (scoring)

• Responses:

  • Likelihood of donation

  • Donation amount

• Predictors:

  • Recency

  • Frequency

  • Amount

  • Activity over the years

  • Demographics

• They built a score…

Score = Likelihood of donation x Donation amount

• And ranked all their donors in decreasing order

Managerial question

What would have been the financial results, had we only solicited the top [X]% of our donors? (e.g., the 10% of our donors who received the highest scores, the top-50%, etc.)

Financial results

Impact of Analytics

Depending on the managerial objectives, this charity could:

• Collect more with less

  • + 10,000 €

  • Improve fundraising ratio from 18.4% to 15.4%

• Dramatically improve financial performance

  • Same net margins

  • Improve fundraising ratio from 18.4% to 11.3%

  • Improve ROI almost twofold

  • Save 80,000 € in costs

Case Study Lifetime Value of the Donor

Key findings: RBS project

All campaigns showed positive net income over long term, broke even with 12-36 months of initial mailing and were NPV positive.

Further, IRR rates were between 25 % - 63 % and significantly higher than the returns RBS receives from its financial investments.

Return on Investment (ROI) and Internal Rate of Returns (IRR)

Key insights from this analysis

  • Our fundraising was highly profitable but only when viewed / measured over the long term

  • Same campaign clearly worked better than other but all were highly profitable

  • We appeared to be under investing based on the market potential at the time

  • We were leaving “money on the table”

Critical questions for your charity / fundraising program

  • What returns are you currently receiving from your fundraising?

  • What techniques methods are working best / worse for you?

  • Is your charity “leaving money on the table”?

  • How much should you invest in fundraising?

  • What is the optimal amount to invest ?

Closing thoughts ...

“In God we trust. Everyone else, must bring data”
“Without data you are just another person with an opinion”

– J Edward Deming

thankQ / Catalyst Management Fundraising Analytics Program: Jason Haigh

  • Specifically tailored for fundraising context

  • Objective: use metrics and analytics to enhance fundraising performance

  • The thankQ/Catalyst Management solution


Data analysis

Affinity and moves management within ThankQ

  • Bottom line: a substantial improvement in your fundraising

  • If you are interested in finding out more, please contact Jackie Gold:


Lawrence Jackson


Level 15, 9 Hunter St, Sydney

NSW 2029 Australia

Mobile: 0438 602 357

Tel (02) 9262 4933

Fax (02) 9262 1619

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