mining for gold: using data to drive revenue & services

© Gamse, Trochlil, Younger 2002

Mining For GoldMining For Gold

Philippa GamseWes TrochlilJay Younger

Using Data to Drive Revenue and Services

© Gamse, Trochlil, Younger 2002

Enough about us…

• Quick poll

• Expectations / Questions

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“Mayo Clinic Plans Database Of Every Patient’s History, Including Genetic Makeup”

Wall Street Journal3.25.2002

In The News...

© Gamse, Trochlil, Younger 2002

Data Mining Defined:

The extraction of predictive information

from stored data

© Gamse, Trochlil, Younger 2002

Data Mining Can Be...

•Automated or Manual

•Expensive or Reasonable

•Integrated or Stand-alone

•Frustrating or Productive

© Gamse, Trochlil, Younger 2002

Call Center

Staff

MeetingRegistratio

n

RenewalFormsSurveys

Vendor Reports

Product

Sales

E-mail

Web

Education

DATA

© Gamse, Trochlil, Younger 2002

Data Management Strategy

• What should you collect?

• How you can collect it?

• What can you do with it?

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The Technology

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“A tale of two associations”

Centralized System• Easy data access• Analyze NOW• Collaborative• Data drives

marketing• Laser marketing

Multiple Systems• Inaccessible data• “Can’t analyze”• Silos• Guesswork drives

marketing• Shotgun marketing

© Gamse, Trochlil, Younger 2002

OK…so I’m in the right column

• Now what?

1. Acquire a new centralized database2. “Connect” the databases via a key

identifier3. Use the systems in a stand-alone

capacity

© Gamse, Trochlil, Younger 2002

Tough Questions...

• Who’s managing this?

• Where’s it going now?

• Who’s entering it?

• What do you really need?

• How can you use it?

© Gamse, Trochlil, Younger 2002

What To Collect

• What’s your goal?

• What’s the potential (ROI)?

• What will it cost?

• What’s your timeframe?

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What To Collect

• Types of data

– Research Data

– Behavioral Data

– E-Data

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Examples of Research Data

• Demographic Information

• Employment Data

• Industry Research

• Marketing Results

• Financial Metrics

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© Gamse, Trochlil, Younger 2002

“Past performance is no GUARANTEE

of future returns”

BUT BUT it’s a good start it’s a good start

in marketingin marketing

© Gamse, Trochlil, Younger 2002

Examples of Behavioral Data

• What they’re buying from you– meeting attendance, publications, education,

products

• Key dates– renewal, lapse, dates of purchase, registration

• Member program participation– certification, insurance, services

• Feedback

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More Examples…(associate members/supporters)

• Sponsorship history

• Advertising & exhibit sales

• Meeting attendance

• “Drivers”

• Competition

• All inquiries

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Website Statistics

Automated data collection– internal search engine

– web traffic reports

– tracking URL’s

“Market research that cannot lie . . .”

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Internal search engine

• site usability• user needs:

– what’s hot?– what’s not covered?

• program / product / service development ideas

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© Gamse, Trochlil, Younger 2002

© Gamse, Trochlil, Younger 2002

© Gamse, Trochlil, Younger 2002

Website traffic reports

• Approaching this data:– HITS and page views– most / least requested pages– time spent on pages– conversion rates– top exit pages

• “Ask questions . . .”

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E-mail campaigns

• Mine your database for targeting– opt-in / opt-out

• Use tracking URL’s with traffic reports:– click-thru’s– conversions

• Test different wording / timing

© Gamse, Trochlil, Younger 2002

Roadblocks

• Data is decentralized & fragmented

• “Overload”

• Storage

• Tendency to focus on the micro

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Solutions

First: Information Audit

Next: Focus on your objectives

Then: Prioritize and plan

© Gamse, Trochlil, Younger 2002

Information Audit

Should help answer these questions:

• What do we have?

• How can we access it?

• What can we do with it?

• What’s it worth?

AND...

© Gamse, Trochlil, Younger 2002

Information Audit

• What’s missing?

• Where can we get it?

• What’s THAT worth?

• How can we centralize it?

Bottom line: knowing your customers

© Gamse, Trochlil, Younger 2002

What can you do with it?

• Market driven information to:– promote current products & services– develop new initiatives

• Permission Marketing– expectation: choice of communication

• Predictive Marketing – think AMAZON

© Gamse, Trochlil, Younger 2002

What ELSE can you do?

• Illustrate total value – “you saved x this year”

• Inform your growth strategy– market segmentation– joint ventures– marketing mix

© Gamse, Trochlil, Younger 2002

Goal: Boost Membership (IMO)

RESEARCH“vitals”gender

age income

education

BEHAVIORproduct salesparticipationattendance

datesfeedback

YOUR MARKET YOUR MARKET YOUR MESSAGEYOUR MESSAGE

E-DATAsearch engine

trafficemail

click-thruconversions

© Gamse, Trochlil, Younger 2002

NACUBO Case Study

© Gamse, Trochlil, Younger 2002

NACUBO Case Study - Association Profile

• Staff: 42 Budget: ~$12 million• 30 seminars, conferences, and workshops

annually• Over 80 publications• Database holds 25,000 contact names

representing 2,200 colleges and universities• Database is SQL-based and enterprise-wide

(centralized)

© Gamse, Trochlil, Younger 2002

NACUBO Case Study – Survey Background

• Collect primary responsibilities • Collect interests• Collect all key contact information,

like title, email, phone, fax, etc.

© Gamse, Trochlil, Younger 2002

NACUBO Case Study – Details

• Paper survey to 25,000 contacts to collect responsibilities and interests

• First round collected 10,000 responses• Moved to online collection

– Initial cost: <$9,000; ongoing cost is negligible– savings of $10,000 annually over paper survey

© Gamse, Trochlil, Younger 2002

© Gamse, Trochlil, Younger 2002

NACUBO Case Study - Results

• New Business Officer program– 300 personalized letters mailed– 100-120 attendees (40% return)

• GASB program– Now holding 10 workshops per year– Targeted mailings of 5,000-7,000 pieces– 1500 total attendees @ $500 per

© Gamse, Trochlil, Younger 2002

NACUBO Case Study – What they did right

• Moved to online data collection• Collected MORE than they initially

wanted, e.g., email addresses, interests

• Targeted their promotions• Used data to develop new programs• Continue to review the data they’re

collecting

© Gamse, Trochlil, Younger 2002

Q & A

© Gamse, Trochlil, Younger 2002

The keys to success

• It’s worth the time

• Benchmark & innovate yourself

• Technology shouldn’t stop you

• People and culture are the key

• Test…and test again