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