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T

YOUR DATA GOLD MINE

“IT’S CLOSER THAN YOU THINK”

Page Bullington, MPA

Target Analytics

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• Getting Rid of Silos

• Data Mining Defined

- A closer look at inside information

• External Sources

- Thinking outside the box

• Final Discussion and Questions

AGENDA

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• Data does not belong here…

GETTING RID OF SILOS

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• When it does not allow for institutional knowledge to be shared

• When it prevents consistent data entry

• When it only allows for one dimensional fundraising

• When it results in data being double, triple…entered

• When it becomes “ours” or “theirs” but not both

• When it makes your work harder

• When it prevents moves management

WHEN SEGMENTING IS BAD…

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CAN WE MOVE TO THIS?

Yes…through a

combination of focused

internal and external data

mining.

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DATA MIN ING DE F INE D

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• Data MiningInvestigating and discovering trends within a constituent database using computer or manual search methods. Simple trend analysis.

• Predictive (Statistical) Data ModelingDiscovery of underlying meaningful relationships and patterns from historical and current information within a database; using these findings to predict individual behavior

DEFINITIONS

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• Your data is one of your greatest assets

• The lens through which you view your donors

• Impacts how your donors view your organization

• Like other assets, requires maintenance

• Can be easily mismanaged

• Effects your ability to allocate scarce resources

WHY DATA MINE?

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• Capacity and donor affinity are the keys to transformational giving

• Donor Affinity is the great unknown

• So, what “affinity” data is vital for you to track, code and report on?

• What data is hiding in your database that can be used today?

• What are best practices for querying and eventually analyzing this

data?

THINGS TO THINK ABOUT…

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• Data that goes beyond gift transactions

• Data offered up by the donor – have you been listening?

• Data that is hard coded – storing it all in the “notes” field doesn’t count

• Data that indicates loyalty or affinity for your mission over other

organizations

• Extraordinary behavior – hand-written notes, calls of praise, etc.

• Data that captures donor engagement

DONOR DATA THAT MATTERS

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• Data is frequently messy or missing

• Incomplete data

• “We don’t have any place to enter those fields”

• “We’d never get the users to key it in”

• That information is managed by a different department and stored in

their database

• What’s important to one department may not be important to another

The challenge is using all of this data in a meaningful way…

THE DATA CHALLENGE

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HIDDEN GEMS

•Constituency Codes

•Source of Gift

•Address Coding

•Event Participation

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• Alumni

• Degree

• Major

• Faculty

• Class Year

• Alumni Non-Grad

• Current or Former Parent

• Board Member

• Friend

• Volunteer

• Subscriber

• Employee

• Professor

• Committee Member

CONSTITUENCY CODES

•Ticket buyer - Performance or

Athletics

•Event Participation

•Online Community Membership

•Number of Student Activities

•Number of Alumni Activities

•Number of Reunions Attended

•Marital Status

•Birth Date

•Occupation

•Requests for Information

•Number of Communications

•Quality of Communications

•Portfolio Assignments

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• Fund/Appeal Tracking

• Online Giving

• Mail Response Giving

• Event Giving

• Payroll deduction

• Honor/Memorial giving

• Planned Gifts

• Event or Ticket Sales, Registration Fees

• Individual giving and Household giving

• Foundation giving versus Individual giving

• Corporate giving versus Individual giving

SOURCE OF GIFT

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• Mailing address

- Home Address versus Business Address

- Change offered by donor versus vendor-purchased addresses

• Individual or household solicitation

• Email address

- Change offered by donor versus vendor-purchased addresses

• Phone number

- Change offered by donor versus vendor-purchased phone number

ADDRESS CODING

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• Event attendance versus type of event

- Major giving prospects attended a major giving event

- Does their attendance predict their likelihood to make a major

gift?

- Were they prior donors or new cultivation prospects?

EVENT PARTICIPATION

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• In the midst of a campaign an organization had been

coding that donors were attending philanthropy driven

events. Development office worked to have more types of

events included so additional information was available

about this type of participation by potential donors.

• Looked at combined event attendance as well as other types of

participation

a) Alumni Event

b) Campaign Event

c) Cabinet-Only Events

d) Staff Attended Events – as part of their job

DATA MINING EXAMPLE

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Event Participation

- Donor A – Event data lives outside main database

– Attends an annual event for a $100 ticket price

– Single interaction with your organization each year

- Donor B – Event data lives inside main database

– Attends the same annual event as Donor A

– Participates in Alumni Reunion every year

– Gives over $1,000 level and upgrades giving every year

– Has given 2 telemarketing gifts

– Called the call center twice

Pairing event data up with other donor interactions helps you distinguish average

donors from extraordinary donors

DATA MINING EXAMPLE

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Involve the Entire Team…

• More individuals working on data means more information but

also…more room for error

• Set standards and use fundraising system to maintain them

• May try using specific forms. Can act as a “preview for data”

• Batch entry can help make the process more efficient but should be

tightly controlled

• Develop documentation that guides those placing information in the

system

• Do you have a secondary research database? This type of approach

can allow for qualification while helping to avoid erroneous data in the

main system

DATA INTEGRITY

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Monitor your data quality regularly

• Identify 100 donors randomly, each year, and thoroughly review

their data

- Incorrect data (typos, moved, married, dead)

- Duplicate records

- Missing information

- Correct treatment (clubs, tracks, expire dates)

DATA INTEGRITY

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E X T E RNAL S OURCE S

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SINGLE SCOOP OR LARGE SUNDAE?

You may want both but in most cases there is a best fit.

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• Questions to ask…

- What is our budget?

- What do we need to know (major vs annual capacity)?

- How many records will we screen?

- Will we screen all at once or do we want to do ad hoc

screenings?

- Who will manage and qualify the new data?

- How much time can they devote to the process?

• Make sure to explore the full range of options when thinking

through the resources you will need

EXTERNAL SOURCES – WORKING WITH VENDORS

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•External sources are a great place to look for a combination of free and

cost options

•Remember there are also local sources in addition to better known

options (i.e. Business Journals)

•Do not forget peer review as a great option as well

•Establishing a form that helps guide the specific types of information

that you are looking for can assist

EXTERNAL SOURCES – FREE (FOR THE MOST PART)

OPTIONS

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MAKING IT AL L WORK

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ORGANIZING DATA

Begin with a data dictionary

•Think through common terms for both internal and external data

sources

•Document these terms and be prepared to perform data audits to

monitor quality

•Example: Nurse, Nurse Manager, Personal Care Technical =

Employee

•Can be accomplished through committee but should have a “lead”

from Prospect Research

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ORGANIZING DATA

Think through where data is stored naturally

•i.e. – Most CRMs will support proper salutation information and this

does not need to be duplicated in notes

Think through the export process

•Data will not be meaningful unless we can export or use the

information for queries or reports

•Standardization will help here

•Ask…should it be text field? A number field? Do I want to be able

to order the information?

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QUESTIONS

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CONTACT INFORMATION

Page Bullington, MPA

Resource Manager

______________

Target Analytics, a Blackbaud company

2000 Daniel Island Drive

Charleston, SC 29492

Phone 800. 443.9441, ext. 3996

Cell 843.408-6768

Fax 843.216.6100

page.bullington@blackbaud.com

www.blackbaud.com/targetanalytics