The Art and Science of Interpreting Market Research Evidence

by Smith, D. V. L.; Fletcher, J. H.

• ISBN13: 9780470844243
• ISBN10: 0470844248
• Edition: 1st
• Format: Hardcover
• Copyright: 2004-04-16
• Publisher: WILEY

Summary

The Art and Science of Interpreting Market Research Evidence offers a complete account of the way today's researchers interpret evidence and apply it to decision making. David Smith and Jonathan Fletcher show how to assess your current deciphering processes, and present an innovative framework integrating quantitative and qualitative approaches for analysing complex data-sets. With its holistic approach to interpretation and its 10-step process for making it work in practice, this book will equip you with a deep understanding of data analysis and ultimately improve your judgment to produce better business decisions.

Author Biography

David Smith is Chairman of the Incepta Marketing Intelligence Group, a leading strategic marketing intelligence organization, and a Professor at the University of Hertfordshire Business School. He is a Fellow and former Chairman of the Market Research Society (MRS), and also a Silver Medal holder of the MRS. He has received Best Paper Awards for papers presented at Market Research Society and ESOMAR events. He is a member of the British Psychological Society; the Chartered Institute of Marketing; and the Institute of Management Consultants. His doctorate in Organizational Psychology is from Bareback College, University of London.

Jonathan Fletcher is a Director of DVL Smith Ltd, one of the strategic research businesses within the Incepta Marketing Intelligence Group. He holds a research degree in philosophy from the University of Cambridge. He has given a number of papers at various industry conferences, and won the Best Methodological Paper Award at the 1999 ESOMAR Congress.

Table of Contents

Foreword.

Preface.

Acknowledgements.

1. ‘New’ market research.

2. Not a science, but a scientific approach.

3. Data-rich intuitive analysis.

4. Analysing the right problem.

5. Understanding the big information picture.

6. Compensating for imperfect data.

7. Developing the analysis strategy.

8. Organizing the qualitative data.

9. Organizing the quantitative data.

10. Establishing the interpretation boundary.

11. Applying the knowledge filters.

12. Reframing the data.

13. Integrating the evidence and presenting research as a narrative.

14. Facilitating informed decision-making.

15. Developing holistic data analysis.

16. Guide to the supporting training module.

Notes.

References.

Glossary of holistic analysis terms.

Index.