Localizing brand name in a logographic language could be challenging in marketing. In this paper, the authors investigated the relationship between brand name types and consumer demands for logographic language using data from China’s automobile market during 2008 - 2012. They focused on four categorization of brand names: alphanumeric, phonetic, phonosemantic, or semantic and they also considered the product’s country of origin and market segment. They examined the impact of brand names at the vehicle model level to control for the unobserved heterogeneity in higher levels. The dataset they used is market-level sales data from R.L. Polk & Company, which contains “monthly unit sales and characteristics of all new vehicle models sold in China at the vintage and model level (e.g., 2005 vintage Toyota Camry) from January 2008 until December 2012” (Fang, et al. 2018. pp1). After data cleaning, there’s 270 vehicle models and 10,607 observations of monthly sales at the vintage–model level”(Fang, et al. 2018. pp1).
The authors used a discrete choice model for estimation of consumer utility. Name refers to the brand name type, γ is the taste paramer, p_it represents the price of vehicle j at time t, and x_it represents other observed characteristics of vehicle j at time t. They also included some fixed-effects such as the vehicle type fixed effect, an import indicator and time fixed effect to control for unobserved heterogeneity. In addition, to control for the potential endogeneity caused by the correlation between some unobserved vehicle characteristics and price variable and brand name indicators, they also adopt an instrumental variable.
To get further insights, they also investigate “heterogeneity in the impacts of brand name types on veicle demand based on COO and targeted market segment”(Fang, et al. 2018. pp2).
The empirical results shows that generally consumers preferred vehicle models with semantic names, and the least preferred type is phonosemantic names. At the mean while, consumers had similar attitudes towards alphanumeric and phonetic brand names. For the heterogeneity in the impact of brand name types, the result shows that consumers preferred vehicle models with semantic brand names and were generally indifferent to the other three types for domestic brands, while alphanumeric names are preferred when it comes to foreign brands. When comparing entry-level versus high-end models, consumers preferred semantic brand names for entry-level brand models while they preferred alphanumeric or phonetic brand names for high-end models.
The extended question I would like to do further research on is: how would the slogan of a brand influence consumer demands for a product?
An effective slogan appearing in advertisements could stick in customers’ minds and help create a bond with potential customers. The research of the relationship between brand name and consumer demand provides important insights in the modelling part for this potential further research. However, as slogan is mainly composed of a couple of words, we can’t directly use the four categories used in the brand name research to categorize the words. My basic idea is to use NLP techniques to extract features from the slogans and have a better categorizations. I would also consider compare the effect on the parent brand level.
Reference:
Wu, Fang & Sun, Qi & Grewal, Rajdeep & Li, Shanjun. (2018). "Brand Name Types and Consumer Demand: Evidence from China's Automobile Market". Journal of Marketing Research, Dec 2018, Vol: 56 issue: 1, p: 158-175.
Localizing brand name in a logographic language could be challenging in marketing. In this paper, the authors investigated the relationship between brand name types and consumer demands for logographic language using data from China’s automobile market during 2008 - 2012. They focused on four categorization of brand names: alphanumeric, phonetic, phonosemantic, or semantic and they also considered the product’s country of origin and market segment. They examined the impact of brand names at the vehicle model level to control for the unobserved heterogeneity in higher levels. The dataset they used is market-level sales data from R.L. Polk & Company, which contains “monthly unit sales and characteristics of all new vehicle models sold in China at the vintage and model level (e.g., 2005 vintage Toyota Camry) from January 2008 until December 2012” (Fang, et al. 2018. pp1). After data cleaning, there’s 270 vehicle models and 10,607 observations of monthly sales at the vintage–model level”(Fang, et al. 2018. pp1).
The authors used a discrete choice model for estimation of consumer utility. Name refers to the brand name type, γ is the taste paramer, p_it represents the price of vehicle j at time t, and x_it represents other observed characteristics of vehicle j at time t. They also included some fixed-effects such as the vehicle type fixed effect, an import indicator and time fixed effect to control for unobserved heterogeneity. In addition, to control for the potential endogeneity caused by the correlation between some unobserved vehicle characteristics and price variable and brand name indicators, they also adopt an instrumental variable. To get further insights, they also investigate “heterogeneity in the impacts of brand name types on veicle demand based on COO and targeted market segment”(Fang, et al. 2018. pp2).
The empirical results shows that generally consumers preferred vehicle models with semantic names, and the least preferred type is phonosemantic names. At the mean while, consumers had similar attitudes towards alphanumeric and phonetic brand names. For the heterogeneity in the impact of brand name types, the result shows that consumers preferred vehicle models with semantic brand names and were generally indifferent to the other three types for domestic brands, while alphanumeric names are preferred when it comes to foreign brands. When comparing entry-level versus high-end models, consumers preferred semantic brand names for entry-level brand models while they preferred alphanumeric or phonetic brand names for high-end models.
The extended question I would like to do further research on is: how would the slogan of a brand influence consumer demands for a product? An effective slogan appearing in advertisements could stick in customers’ minds and help create a bond with potential customers. The research of the relationship between brand name and consumer demand provides important insights in the modelling part for this potential further research. However, as slogan is mainly composed of a couple of words, we can’t directly use the four categories used in the brand name research to categorize the words. My basic idea is to use NLP techniques to extract features from the slogans and have a better categorizations. I would also consider compare the effect on the parent brand level.
Reference: Wu, Fang & Sun, Qi & Grewal, Rajdeep & Li, Shanjun. (2018). "Brand Name Types and Consumer Demand: Evidence from China's Automobile Market". Journal of Marketing Research, Dec 2018, Vol: 56 issue: 1, p: 158-175.