The impact of online purchase behaviour on customer lifetime value
| Authors | |
|---|---|
| Year of publication | 2024 |
| Type | Article in Periodical |
| Magazine / Source | JOURNAL OF MARKETING ANALYTICS |
| MU Faculty or unit | |
| Citation | |
| web | https://doi.org/10.1057/s41270-024-00328-9 |
| Doi | http://dx.doi.org/10.1057/s41270-024-00328-9 |
| Keywords | E-shop; Customer lifetime value; Loyalty; Customer targeting; Multilevel models |
| Description | This paper investigates customer lifetime value (CLV) in e-shops, particularly those operated by small on-platform evolving financially independent online resellers (SOEFIOR) e-shops. The aim is to identify factors predicting CLV and assess their associations with CLV. Given the nested structure of the data, where transactions by customers are clustered within e-shops, a multilevel model is employed as the analytical framework. While classical linear regression assumes independence of observations within a sample, our dataset operates across three hierarchical levels: transaction level (I), customer level (II), and e-shop level (III). This hierarchical structure challenges the validity of inferences drawn from linear regression models, as transactions by one customer are not independent, and customers within a single e-shop may exhibit interdependencies. Therefore, a multilevel model is utilised to appropriately address the dependence among transactions within this nested data structure. The analysis reveals that the “number of transactions” exhibits the strongest positive association with CLV, followed by “days to transaction” and “session duration”. Furthermore, we discovered that “direct access” exhibits a positive association with CLV compared to access through Google campaigns, whereas access through Facebook campaigns demonstrates a negative association with CLV when compared to Google campaigns. Additionally, using the e-shop on mobile and landing on the product details page both show negative associations with CLV compared to desktop usage and landing on the e-shop’s home page, respectively. Our research identifies several variables that are associated with CLV in e-shops. This enables e-shop managers to effectively target and engage customers through marketing activities, thereby maximising revenues, financial performance, and customer CLV. |