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Segmentation analysis of mobile phone users based on frequency of feature use

Segmentation analysis of mobile phone users based on frequency of feature use

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Segmentation analysis of mobile phone users based on frequency of feature use

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  1. Segmentation analysis of mobile phone users based on frequency of feature use Amaleya Goneos-Malka Arien Strasheim Anské Grobler 30-08-2012 2012 World Marketing Congress ~ Cultural Perspectives

  2. Introduction • Mobile phone penetration in South Africa: 80% of population • High penetration illustrates the importance of mobile technology and its acceptance • The widespread popularity of mobile phones is reason to consider the mobile medium as a viable marketing communication platform • Effective campaigns tapping into specific mobile phone features requires knowledge of: • Specific features users have access to • Which features people actually utilise • How frequently the features are used

  3. Purpose of study • To establish segmentation profiles according to mobile phone features used and frequency of use • To conceptualise and develop measures of mobile importance and social transformation within a postmodern perspective • To compare segments on attitudinal and behavioural factors concerning the importance of mobile phones and how social transformation is effected within the context of a postmodern environment

  4. Definitions Mobile importance • Mobile importance is viewed as the degree of dependency individuals attach to mobile phones as a behavioural outcome Social transformation • Social transformation is a behavioural outcome arising from a postmodern environment, which is enabled by mobile devices and other technological advances in society

  5. Background : Segmentation, a marketing strategy • Segmentation based on frequency of mobile features used by consumers, allows marketers to utilise this medium to communicate with multiple market segments more effectively. • The importance that individuals attach to their phones may have implications for the features they are using, and this may result in differential success of the medium for marketing communication • Frequency of different features used may also lead to different behavioural outcomes, in terms of how the medium contributes to the social transformation of users, suggesting different needs within each segment, each presenting unique marketing opportunities

  6. Background : Mobile phones and marketing communication • Mobile phones have progressed from voice only mobile telecommunication to smart mobile media devices possessing multi-functional capabilities • Mobile phones permit their owners to choose what content or functions these individuals wish to access at their convenience (Groening 2010) • The various mobile phone functions present marketing communication opportunities for practitioners to connect with their target audiences

  7. Cluster analysis: a method for segmentation research Prior research has used cluster analysis: • To produce patterns of mobile service usage (Sohn and Kim 2008) • To segment mobile phone users according to their preferences to use certain mobile phone functions (Head and Ziolkowski, 2010) • To assess consideration factors involved in the purchase of mobile phones (Kimiloğlu, Nasir and Nasir 2010) • To identify mobile Internet adopters (Okazaki, 2006) • To associate different consumer lifestyles with payment of various mobile services (Zhu, Wang, Yan and Wu 2009)

  8. Segmentation: by mobile phone features • The proposed profiles segment mobile phone users according to the different mobile phone functions they use and the frequency of usage • This provides an indication of suitable mobile applications for practitioners to communicate with their various target segments

  9. Methodology • Typical features of phones were listed and usage frequency of each feature was measured • New scale items were developed for the introduced constructs, mobile importance and social transformation • A convenience sample comprising of full-time registered students at the University of Pretoria was used. A total of 333 completed usable questionnaires were obtained • K-means non-hierarchical clustering method was used to obtain clusters from the data • Exploratory factoring analysis (PAF) was applied to explore the dimensionalities of the two newly designed measures, capturing mobile importance and social transformation

  10. Results and discussion The cluster analysis produced four clusters, namely Connectors, Conventionalists, Technoisseurs and Mobilarti. Figures in bold are larger than the overall means, whilst figures in italics are less than the overall mean

  11. Segmentation results 28 % 33% 19% 20%

  12. Segments • Connectors (28%) primarily use mobile phones for communicating and organising - using messaging, social media and email functions on daily basis • Conventionalists(33%) are inclined to restrict their use of mobile phone features to talking and texting, and thus are regarded as technology laggards • Technoisseurs (19%) frequently use a variety of different mobile phone features and listen to or download music more than any other cluster in the group • Mobilarti (20%)make the most extensive use of several mobile phone features through their mobile handsets, despite recording the lowest percentage of smartphone ownership

  13. Factor loadings: Mobile importance

  14. Mobile Importance Factors • Mobile addiction: isindicative of de-differentiation, through the reversal roles between subject and object as inferred by the status respondents confer to their mobile phones • Empowered choice: is indicative of respondents’ right to choose media under conditions of abundant choice and therefore suggests of tolerance of diversity • Convenient interconnection:is suggestive of hyperreality and de-differentiation, arising from the blurring of boundaries - mobile phones overcoming boundaries between physical and virtual media Postmodern behavioural outcomes in response to opportunities in the environment enabled by mobile phones

  15. Mobile importance: Factor mean scores by usage segments

  16. Mobile importance: Factor mean scores by usage segments • Technoisseursobtained the highest mean scores for Convenient interconnection andEmpowered choice. This segment predominantly own smartphones; the technological capabilities of their devices facilitate access to and use of a range of media applications and social media platforms • Mobilarti had the highest mean score for Mobile addiction, illustrating their high dependency on mobile phones. • Connectorshad slightly higher mean scores than the Mobilarti on Convenient interconnection andEmpowered choice; which supports the assumption that this group uses mobile devices for connectivity, and have the choice to choose which mobile phone features or what media content they wish to engage with through their mobile phones. • Conventionalists consistently recorded lower scores for each dimension than all other groups, indicating their comparatively lower dependence on mobile devices, despite the fact that their mobile devices are mostly smartphones.

  17. Factor loadings: Social transformation

  18. Social Transformation • Hyperreal cult: is indicative of the outcome hyperreality, positioning social media networks as communication platforms, bridging physical and virtual worlds • Hyperreal escapism: dovetails with hyperreality, suggesting escapism • Interactive collaboration:is suggestive of collaborative marketing • Dissolved boundaries: represents de-differentiation. In this instance it pertains to the fact that using social media networks dissolves boundaries of time, space and place Outcomes of postmodern behaviour are manifested through respondents’ use of social media The outcomes are significantly different over clusters

  19. Social transformation: Factor mean scores by usage segments

  20. Social transformation: Factor mean scores by usage segments • Shows a similar pattern for the factors of social transformation to the factors of mobile importance with the Mobilarti and Technoisseurs obtaining the highest mean scores followed by Connectors and finally Conventionalists for each of the four factors. • The lack of mobile phone features coupled to financial constraints may limit Mobilarti’s perspective of social media networks as platforms to overcome boundaries of time, space and place to cultivate global connectivity • Mobilarti had the second highest score forDissolved boundaries. This result may be due to a lower incidence of smartphones and possibly fewer financial resources. The lack of mobile phone features coupled to financial constraints may limit Mobilarti’ perspective of social media networks to cultivate global connectivity

  21. Managerial Implications • High use of multiple mobile phone features underlies the value of mobile media for marketing communication • Different segments exhibit different usage profiles of mobile phone features • Understanding segments mobile phone usage patterns may enable marketers, mobile phone manufacturers, advertisers, software application developers, and mobile network operators to be more effective in the mobile marketing communication and mobile commerce domains • Improve future development of features and applications • Overcome limiting factors: user ability and handset functionality

  22. Conclusion • Use of mobile phone functions and the nature of use are suggestive of the degree to which mobile phone media are integrated into respondents’ lives • Segmentation by mobile phone features used and extent of usage improves understanding of the market’s mobile phone usage patterns and therefore suggests which mobile phone features marketers should make use of in communication campaigns directed at different market segments • Mobile importance and social transformation relate to postmodern traits. Mobile importance recognises mobile phones as important enablers of postmodern behaviour, and social transformationcharacterises outcomes of postmodern behaviour

  23. Suggestions for further research • Continued advancements in mobile technology will lead to the development of new features and applications that were not previously available. Therefore consumer access to these choices may, in the future, impact on the compositions of the cluster profiles developed in this study. In light of this it is suggested that longitudinal studies be undertaken to detect changes and track trends. • The sample used in this research limits the generalisability of the study to the larger Generation Y population. It is recommended that future research should include younger as well as employed members of Generation Y to allow for the generalisability of the results. Alternatively, studies into other generations could be conducted to determine the categories of mobile phone usage segments within these generations.