- Title
- Clustering consumers and cluster-specific behavioural models
- Creator
- De Vries, Natalie J.; Carlson, Jamie; Moscato, Pablo
- Relation
- ARC.FT120100060 http://purl.org/au-research/grants/arc/FT120100060
- Relation
- Business and Consumer Analytics: New Ideas p. 235-267
- Publisher Link
- http://dx.doi.org/10.1007/978-3-030-06222-4_5
- Publisher
- Springer Cham
- Resource Type
- book chapter
- Date
- 2019
- Description
- Social media has almost become ubiquitous in everyday communications and interactions between customers and brands. A novel clustering algorithm, that has shown high scalability in previous applications, is applied to analyse and segment an online consumer behaviour dataset. It is based on the computation of a Minimum-Spanning-Tree and a k-Nearest Neighbour graph (MST-kNN). Cluster-specific consumer behaviours relating to customer engagement are predicted using symbolic regression analysis which, in a commercial setting, would provide the basis for personalized marketing strategies. Five major clusters were found in the dataset of 371 respondents who answered questions from theoretical marketing constructs related to online consumer behaviours. They are labelled as follows: 'Brand Rationalists', 'Passive Socializers', 'Immersers', 'Hedonic Sharers' and 'Active Participators'. For each of these clusters, a linear model of customer engagement was predicted using symbolic regression analysis. These models inform possible personalized marketing strategies after proper segmentation of the customers based on their online consumer behaviour, rather than simple demographic characteristics.
- Subject
- brand; customer engagement; engagement; loyalty behaviour; online customer engagement; customer engagement prediction
- Identifier
- http://hdl.handle.net/1959.13/1447121
- Identifier
- uon:43055
- Identifier
- ISBN:9783030062217
- Language
- eng
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