Derek

1
AICUM August 10 2001

• Derek
• McIntyre

2
Northern Inland

• Community based credit union
• 4 Branches
• 130m assets
• 23,000 members
• 50 full time staff

3
Reasons

• Better targeting
• Niche Marketing
• Commodity Products
• Retention more profitable than acquisition
• Understand our members better

4
An Evolution

• Integrate IT and Marketing skills
• Sustainable competitive advantage
• Difficult to emulate
• Covertly conducted

5
How?

• It was an evolutionit took time
• Data sourced from FCS
• Data downloaded into spreadsheets
• Data files linked using RDBMS
• Data came to life

6
Understanding

• Understanding the data allowed us to see the
  potential of the system
• Importance of archiving data
• Data date stamped in data warehouse

7
Evolution

• Data manipulation was still not automated
• Importance of system automation
• Download data, scrub it, date stamp and archive
  it.

8
Evolution

• Adhoc manually constructed marketing campaigns
• More than just direct marketing
• Develop stronger relationships with members

9
Problems

• Dirty data
• Duplicate memberships
• Need to identify households and economic
  relationships

10
Evolution

• Why?
• To fully understand the relationship with our
  members

11
Evolution

• How do we use the information?
• Segment into groups
• Determine member value
• Monthly snapshot of member holdings and behaviour
• Decile 1 Most valuable
• Decile 10 Low value

12
Decile Value
13
Improving member value

• Retention Strategy Decile 1
• Improve Member Value Deciles 2,3 4.
• Decile 2 14.83
• Decile 3 19.03
• Decile 4 25.95
• Decile 10 12.7

14
Database Marketing

• Early application issues
• Members targeted at product level only
• Same members contacted about the same products

15
Database Marketing

• To contact a broader group of members
• Improve financial well being of members
• Develop stronger member relationships

16
Database Marketing

• Identify members who have potential
• Improve member satisfaction and retention
• Selectively focus our marketing activities

17
Database Marketing

• Campaign Management
• Data warehouse holds detailed information
• Manual construction of campaigns tedious and
  labour intensive

18
Database Marketing

• Automated campaign system
• Identifies changes in data
• Automatically builds mailing lists

19
Database Marketing

• Campaigns are triggered by changes in data
• Product related activity
• Life events
• Life stages
• Niche marketing system
• Uses opportunities to provide members with
  relevant and timely information

20
Database Marketing

• Contact material (smart letter) is automatically
  generated
• Populated with text and graphics dependant on
  member characteristics

21
Database Marketing

• Campaign example
• Loan Prospects
• Age
• Last product mailed
• Credit History
• Loan Balances
• Duplicated members
• Previous loans

22
Database Marketing

• Automatic insertion of graphics, photos and text
• - Dependent on data variables such as age,
  gender, address etc.
• Dynamic environment
• - Need to continually explore new ideas
  and areas.

23
Database Marketing

• Monitoring
• Use control groups to compare marketing
  effectiveness
• Archive all target and control group data
• Track member value, product holdings, campaign
  costs

24
Database Marketing

• Contact Management
• Manages contact frequency
• Not less than 4 months any contact
• Not less than 12 months for products

25
Database Marketing

• Identify changes in member behaviour
• Help members to reduce fees
• Find help members who have potential

26
Database Marketing

• Future Initiatives
• Building a knowledge base
• Use predictive data mining in next 12 months

27
Where to from here?

• Determining which contact material impacts on the
  member relationship most
• New high volume colour printer
• Personlised Newsletters
• Customised email Newsletters
• Integration into other areas with CRM staff.

28
Conclusion

• Majority of the IT hurdles have been overcome in
  regard to finding, extracting and storing the
  data.
• Real challenges still lay in the marketing area
  and the majority of the learning is yet to come
  over the next 24 months.

29

• Would we do it all again.?
• .YES!
• THANKYOU