Mobile Phone and Web Sites

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KEL451

MOHANBIR SAWHNEY

Ontela PicDeck (B):
Customer Segmentation, Targeting, and Positioning

The Quantitative Customer Segmentation Study
Although Joe Levy felt that the customer persona provided a good starting point to begin thinking about PicDeck’s segmentation, he believed that he needed quantitative data to get a more precise understanding of the relative attractiveness of different customer segments. This would require data on end users regarding their preferences and behaviors related to mobile devices and imaging. He therefore hired a market research firm to conduct a quantitative customer segmentation study.

The firm conducted a national survey of 2,000 respondents who were selected from lists of mobile telephone customers aged 15 and older. The questions in the survey ranged from respondents’ current mobile phone behavior and camera experience to general technology aptitude and price sensitivity. The survey questions are listed in Exhibit 1. The research firm used a random sampling approach to ensure that the respondent sample accurately represented nationwide wireless subscribers.

Analysis of Customer Survey Data
Identifying Segments—Cluster Analysis of Preference Data
The research firm performed a cluster analysis on the responses. A cluster analysis is a statistical technique that identifies groups of customers who have similar survey response patterns. The number of clusters is determined in part by how different the response patterns are. If all survey respondents tend to give the same responses, then only one cluster is identified. For this survey, six clusters of respondents were identified, each with a different response pattern. Details of each cluster are provided in Exhibit 2, which lists the mean responses to each question for each cluster.

©2009 by the Kellogg School of Management, Northwestern University. This case was prepared by Patrick Dupree, Christine Hsu, Ryan Metzger, Fuminari Obuchi, Arun Sundaram, and Kari Wilson under the supervision of Professor Mohanbir Sawhney. It was revised by Professor Kent Grayson. Cases are developed solely as the basis for class discussion. Cases are not intended to serve as endorsements, sources of primary data, or illustrations of effective or ineffective management. To order copies or request permission to reproduce materials, call 800-545-7685 (or 617-783-7600 outside the United States or Canada) or e-mail custserv@hbsp.harvard.edu. No part of this publication may be reproduced, stored in a retrieval system, used in a spreadsheet, or transmitted in any form or by any means—electronic, mechanical, photocopying, recording, or otherwise—without the permission of the Kellogg School of Management.

ONTELA PICDECK (B)

KEL451

Profiling Segments—Demographic and Media Choice Information In addition to the preference data, the research firm also asked the same respondents a series of questions related to demographics and media habits. The demographics and media habit questions are shown in Exhibit 3, and the profiles of the six clusters are reported in Exhibit 4.

Assignment Questions
1. Based only on the cluster analysis data, which preference-related variables are most useful for segmentation identification and evaluation? Which variables are least useful? 2. Keeping in mind what you know about each cluster before you look at Exhibit 3 and Exhibit 4, create descriptive profiles for the customer segment represented by each cluster. Label each segment with a title that best describes that cluster. To what extent does this new information reinforce or challenge your previous assumptions about the segments in this market?

3. Now, use the profiling information in Exhibit 4 to create a revised profile for each cluster. Is this profile different from what you “guessed” based only on the preference data? 4. Which segment(s) would you recommend as a target for PicDeck? Explain the logic behind your choice.

5. Develop a positioning statement...
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