Pricing optimization of product line with value-added services considering consumer choice behavior

ABSTRACT

The global economy is transitioning from product economy to service economy, and the optimization of product line with value-added services is one of the important issues faced by enterprises. Therefore, this paper studies the pricing optimization of product line with value-added services considering consumer choice behavior. First, we apply the multinomial logit (MNL) model to simulate consumer choice behavior, and establish a MNL-based product line pricing optimization model considering value-added services. Then, the optimal pricing strategies for products and services are solved for three different scenarios: single product with service, homogeneous products with services, and heterogeneous products with services. Finally, through numerical experiments, we analyze the changing trends of the optimal pricing, market share and profit of products line with value-added services with some impact factors, such as product quality, service level, consumer sensitivity, and number of products under three different scenarios. We also obtain some managerial implications when an enterprise develops product line with value-added service.

CO EDITOR-IN-CHIEF:

• Kuo, Cheng-Chien

ASSOCIATE EDITOR:

• Wang, Wen-June

KEYWORDS:

• Product line
• value-added service
• consumer choice behavior
• multinomial logit model

Nomenclature

$b_i$	=	quality of product $i$
$c$	=	total cost of products with value-added services
$c_i$	=	fix cost of product $i$
$i$	=	index of each product
$j$	=	index of each consumer
$m$	=	number of potential consumers
$n$	=	number of products
$\mathbf{p}$	=	price vector
$p_i$	=	price of product $i$
$\mathbf{q}$	=	choice probability vector
$q_i$	=	probability of a consumer choosing product $i$
$q_0$	=	probability of a consumer choosing outside product
$R$	=	expected revenue
$s_i$	=	service level of product $i$
$u_{ij}$	=	utility obtained by consumer $j$ from purchasing product $i$
$v_{ij}$	=	deterministic utility obtained by consumer $j$ from purchasing product $i$
${\alpha }_{ij}$	=	sensitivity degree of consumer $j$ to the quality of product $i$
${\beta }_{ij}$	=	sensitivity degree of consumer $j$ to the price of product $i$
${\gamma }_{ij}$	=	sensitivity degree of consumer $j$ to the service level of product $i$
${\epsilon }_{ij}$	=	random utility obtained by consumer $j$ from purchasing product $i$
$\pi$	=	profit

Acknowledgments

This work was supported by the National Natural Science Foundation of China [grant number 72001071]; National Social Science Foundation of China [grant number 22FGLB083]; Philosophy and Social Sciences Planning Project of Henan Province in China [grant number 2022BJJ031]; the Humanities and Social Sciences Research Program of the Ministry of Education of China [grant number 21YJA630061]; Program for Science &Technology Innovation Talents in Universities of Henan Province (Humanities and Social Sciences) [grant number 2021-CX-004]; Program of Higher Education Philosophy and Social Sciences Innovative Talents of Henan Province [grant number 2024-CXRC-02].

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The work was supported by the National Natural Science Foundation of China [72001071]; Humanities and Social Sciences Research Program of the Ministry of Education of China [21YJA630061]; Philosophy and Social Sciences Planning Project of Henan Province in China [2022BJJ031]; Program of Higher Education Philosophy and Social Sciences Innovative Talents of Henan Province [2024-CXRC-02]; Program for Science &Technology Innovation Talents in Universities of Henan Province (Humanities and Social Sciences) [2021-CX-004].