A Framework to Understand Customer Data Quality in CRM Systems for Financial Services Firms

CADMEF IMC Academic Roundtable: May 10-11, 2012DePaul University, Chicago

Debra ZahayAssociate Professor of Interactive Marketing

Northern Illinois University

James Peltier Professor of Marketing

University of Wisconsin, WhitewaterMarketing Department

College of Business and Economics

Anjala S. KrishenAssistant Professor

Department of Marketing, Lee Business SchoolUniversity of Nevada, Las Vegas

Agenda

• Background/Motivation• Prior Work• Method• Results• Managerial Implications

Primary Research Streams 1999-2012

Customer Information

Management for Competitive

Advantage

Data Quality, Personalization

and CRM

Customer Information Use in New

Product Development

With co-authors: Domagalski, Fredericks, Griffin, Handfield, Krishen, Lehmann, Mason, Payton, Peterson, Peltier, Schibrowsky, Shavitt, Schultz, Scovotti, Thorbjornsen, White

Managerial View of the Learning Organization

1. Generate

2. Remember

3. Disseminate

4. Interpret

Learning Activities

Competitive Advantage

Use

Move

Store

Get

5

Base Contact Data

Transactional History

Promotional History

DescriptiveData

Models/Scores

• Successful marketing databases contain many “views” of customers and prospects...

Name, address, phoneE-mailPreferred communication. ChannelJoin/First Purchase DateSource

Detailed purchase or donor historyCustomer service interactions

Times mailed/solicitedResponse to promotions

Consumer or business demographicsSelf-Reported or overlaidLifestyle, hobbies, interestsPersonal Dates -- birthday, anniversary, etc.

Predictive model scoresRFM ScoresModel segmentation scoresLifetime Value Scores

Practitioners Perceive the Value of Information as Hierarchical

Source: The Allant Group

Learning Organization Theory Suggests Similar Hierarchy for Interactive Strategy

Development

Source: Roberts and Zahay 2012

Research Method & Analysis

• Qualitative Study• Pre-Test• Final Survey• Factor analysis to refine variables• Regression analysis to determine

relationship between use of customer data types and CRM Data Quality

Survey Background• Data Collection:

– 525 mailed– Three waves, one mail wave, one including $2 bill and one

telephone follow up wave– 32 % response rate

• 170 Executives in Financial Services– 50% primarily b2b and 40% b2c, rest other trade relationships– 50% had retail relationships, 27% relied on outside sales– 10% online sales– Executives had typically twenty years of experience

• 166 useable surveys• Response: Percent of Time Data Collected

PersonalizationCustomer

Touchpoint

Psycho-Demographic

Transactional/RFM Data

Customer Contact Information

Proposed CRM Data Type Hierarchy: Hypotheses are that use of these data types are positively related to CRM Data Quality, in this order

CRM Data Quality

What is CRM Data Quality?Overall, Data is of high quality when it reflects perceived reality

In a customer context, we measured managers’ perception of:

• Overall quality of the customer contact system• Overall Quality of Data• Overall quality of the CRM system• 5-Point Scale• 5=Strongly Agree• 1=Strongly Disagree

Personalization=.39

Transactional/RFM =.32Psycho-Demographic =.30

Customer Contact Information = .24Customer Touchpoint =.13

Offers and Communications

Customer Info and Collection Points

Hypotheses Supported in General, Transactional, Contact Data More Important in Relation to Customer Data Quality, Touchpoint Data Less So

CRM Data Quality

Hypothesized vs. Actual Relationships Suggest Shift in Management Focus

Personalization

Customer Touchpoint

Psycho-Demographic

Transactional/RFM Data

Customer Contact Information

Personalization=.39

Transactional/RFM =.32

Psycho-Demographic =.30

Customer Contact Information = .24

Customer Touchpoint =.13

Contacts and QuestionsDebra ZahayNorthern Illinois University815-753-6215zahay@niu.edu James PeltierUniversity of Wisconsin, Whitewater262-472-5474peltierj@uww.edu

Anjala S. KrishenUniversity of Nevada, Las Vegas540-588-3961anjala.krishen@unlv.edu

“Building the foundation for customer data quality in CRM Systems for financial services firms,” Journal of Database Marketing andStrategy Management, Volume 19, Number 1, pages 5-16

Peltier, J.W., Zahay, D.L. and Lehmann, D.L. (2012 Forthcoming), "Organizational Learning and CRM Success: A Model for Linking Organizational Practices, Customer Data Quality, and Performance," Journal of Interactive Marketing.