Name
Abstract | ||
|---|---|---|
In this paper we explore and analyse the heterogeneity existent within a seemingly homogenous sample of online consumer behaviours in terms of their demographic profile. The data from a sample of 371 survey respondents is clustered using various distance functions and a clustering algorithm. In doing so, the respondents are clustered based on their response profiles to online behaviour questions rather than their demographic characteristics or brand preferences. Through our results we highlight that high levels of heterogeneity amongst consumers within the same cluster exists in terms of the 'types' of brand categories they engage with through social media. This finding has implications for marketing strategies and consumer behaviour analysis as it highlights the importance of investigating consumer's behavioural profiles in the online brand setting. Our method also provides an empirical guide to examining respondents' heterogeneity in terms of response profiles rather than 'traditional' segmentation strategies based on basic demographic information or brand categories. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/BDCloud.2014.23 | Big Data and Cloud Computing |
Keywords | Field | DocType |
consumer behaviour,graph theory,marketing data processing,brand categories,clustering algorithm,consumer behaviour analysis,demographic information,distance functions,marketing strategies,online consumer behaviour data,proximity graph approach,social media,combinatorial optimisation,data clustering,graph partitioning,online consumer behaviour,social media | Data mining,Social media,Demographic profile,Computer science,Consumer behaviour,Segmentation,Robustness (computer science),Correlation,Cluster analysis,Graph partition | Conference |
Citations | PageRank | References |
0 | 0.34 | 5 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| De Vries, N.J. | 1 | 0 | 1.01 |
| Ahmed Shamsul Arefin | 2 | 4 | 2.17 |
| Pablo Moscato | 3 | 334 | 37.27 |