The impact of online purchase behaviour on customer lifetime
value

KVÍČALA, Daniel, Maria KRÁLOVÁ and Petr SUCHÁNEK. The impact of online purchase behaviour on customer lifetime value. JOURNAL OF MARKETING ANALYTICS. ENGLAND: PALGRAVE MACMILLAN LTD, 2024, vol. 1, No 1, p. 0-18, 18 pp. ISSN 2050-3318. Available from: https://dx.doi.org/10.1057/s41270-024-00328-9.

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TY  - JOUR
ID  - 2416218
AU  - Kvíčala, Daniel - Králová, Maria - Suchánek, Petr
PY  - 2024
TI  - The impact of online purchase behaviour on customer lifetime value
JF  - JOURNAL OF MARKETING ANALYTICS
VL  - 1
IS  - 1
SP  - 0-18
EP  - 0-18
PB  - PALGRAVE MACMILLAN LTD
SN  - 20503318
KW  - E-shop
KW  - Customer lifetime value
KW  - Loyalty
KW  - Customer targeting
KW  - Multilevel models
UR  - https://doi.org/10.1057/s41270-024-00328-9
N2  - 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.
ER  -

Basic information
Original name	The impact of online purchase behaviour on customer lifetime value
Authors	KVÍČALA, Daniel, Maria KRÁLOVÁ and Petr SUCHÁNEK.
Edition	JOURNAL OF MARKETING ANALYTICS, ENGLAND, PALGRAVE MACMILLAN LTD, 2024, 2050-3318.

Other information
Original language	English
Type of outcome	Article in a journal
Field of Study	50204 Business and management
Country of publisher	United Kingdom of Great Britain and Northern Ireland
Confidentiality degree	is not subject to a state or trade secret
WWW	URL
Impact factor	Impact factor: 3.000 in 2022
Organization unit	Faculty of Economics and Administration
Doi	http://dx.doi.org/10.1057/s41270-024-00328-9
UT WoS	001248296400002
Keywords in English	E-shop; Customer lifetime value; Loyalty; Customer targeting; Multilevel models
Tags	International impact, Reviewed
Changed by	Changed by: doc. Mgr. Maria Králová, Ph.D., učo 18650. Changed: 1/7/2024 23:14.

Abstract

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.

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The impact of online purchase behaviour on customer lifetime value

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