Consumer behaviour explains the attitudes, intentions and preferences of the consumer prior to their buying which has been closely monitored and exploited by the e-commerce businesses (Akkucuk & Esmaeili, 2016). Online buying behaviour is influenced by multiple factors which depend on consumer needs and their immediate necessities. The difference in online purchase decision on the basis of economic status
Role of age in online buying behaviour
Age is a crucial demographic aspect that impacts online buying behaviour, as the purchase decisions change with age (Gurmu & Etana, 2014). People of younger age tend to spend more on lifestyle, fun, fashion, while elderly people spend on most health-related expenses. Rajyalakshmi (2015) found that older shoppers are more concerned about the country of origin of a product or service before buying. Younger shoppers likely take an online purchase decision due to better computer skills and more experience in using the internet. On the other hand, older shoppers perceive a higher degree of risk in making online purchases (Morris and Venkatesh, 2000). Age not only influences buying behaviour but also plays a crucial part in the development of marketing strategies and market segmentation for a firm.
Amazon and Netflix target a specific age group
One of the most notable examples of online companies using age to segment their buyers is Amazon. It is targeting customers in the age group 35-42 years old with high income for products such as apparel. By offering a flat two-day shipping policy at a reasonable yearly fee of USD 99, Amazon is set to surpass Walmart as the top online apparel selling the company by 2010 (Thomas, 2018).
Another noteworthy example is that of online streaming giant Netflix’s revenues quickly
Role of income in online buying behaviour
Income plays a vital role that influences online buying behaviour (Ryscavage, 2015). Low-income individuals approach online shopping cautiously and suspiciously due to lower tolerance for a financial loss as compared to a high-income individual (Gunes, 2018). Middle-class people make purchase decisions on the basis of their needs and not wants of luxury brands or designer clothes (Lautiainen, 2015). The medium for the marketing of luxury goods is unique compared to basic commodities and necessities. Luxury goods are mostly promoted on niche magazines or web portals and sensory branding, whereas basic commodities are mass promoted on TV and newspaper ads, hoardings (Hammer, 2011).
Louis Vuitton’s strategy to tap the Chinese high-income online consumers
Louis Vuitton, one of the biggest luxury fashion houses in the world, recently changed its marketing strategy in China. Although it offered the online buying option to buyers since 1999, customers were traditionally required to pick up the product physically from the store so that they can experience a different service. However, recently it launched an e-commerce website in 2018 to tap the rapidly expanding online consumer base in China. This led to a 14% rise in its revenues (Zheng, 2018).
Role of marital status in online buying behaviour
Marital status of a consumer explains an individual’s association with a significant other (Han et al., 2014). Examples of marital status are single, divorced, married and widowed. Research on the association between marital status and online buying behaviour has been inconclusive. While Kim and Kim (2004) found that the marital status and number of children in the household is positively related to online shopping, Doolin et al (2005) suggest that there is no relation. Few of the biggest examples of e-commerce companies targeting their market on the basis of their marital status are online match-making websites like Shaadi.com in India. The website targets exclusively single and divorcees.
Gender-based marketing strategy
Gender differences influence buying decisions as per their varied needs and want, and lifestyle (Oakley, 2016). Buying behaviour differences are significant between men and women. Women perceive a higher risk of online purchases than men. On the other hand, shopping orientation is also different for men and women. Women’s online buying behaviour is led by social interactions and is mostly recreational. Men are more convenience-oriented and do not give importance to face-to-face interaction (Swaminathan et al, 1999). Moreover, online shoppers are more likely to be men than women due to their computer skills (Zhou et al, 2007). Shopping formats also differ when it comes to gender. Women are more likely than men to participate in online bids (Fallows, 2005). Finally, women report a higher degree of dissatisfaction with their online purchases than men (Doolin et al., 2005).
Several examples exist of e-commerce businesses targeting customers on the basis of their gender. For instance, Walmart, Amazon and other apparel retail website target ads specific to their gender. Another noteworthy example is that of Australian Women’s Travel, an e-commerce website that caters specially women looking for guided tours around the world.
Location based marketing strategy
Based on rural and urban location, marketing strategies are being idealised (Andrews & Hampel, 2015). Consumers from the urban market are exposed to a different set of goods, newly launched fast moving products or services. People who live in rural areas with less number of shops are more likely to purchase online in order to save time in travelling shops (Ren and Kwan, 2009).
Zomato, the global restaurant search engine, introduced context-based advertisements in 2014 on their website as well as mobile app, where restaurant suggestions are given to customers based on their location. The website enabled this through the use of GPS, wherein the live location of the users are recorded regularly. Since then, the company has seen a rapid rise in its revenues, touching Rs. 650 crores in 2018 (Srinivasan, 2018).
Demographic segmentation play an important role in marketing strategies
It is important for marketers to understand how different demographic factors influence online buying behaviour and can be exploited. The sole focus of e-commerce companies is to ensure customer satisfaction through quality products or services, reasonable pricing and enhanced pre and post-sale services. Based on demographic factors such as income, age, gender and location, online businesses are able to categorize their products or services more efficiently and are able to set their target market. With a focus on demographic factors, market segmentation would be strictly on point and cost-effective.
- Akkucuk, U. & Esmaeili, J.., 2016. The Impact of Brands on Consumer Buying Behavior. International Journal of Research in Business and Social Science, 5(4), pp.1-16, DOI: 10.20525/ijrbs.v5i4.551.
- Andrews, D.F. & Hampel, F.R.., 2015. Robust estimates of location: Survey and advances. 1st ed. NY: Princeton University Press, ISBN-13: 978-0691619767.
- Doolin, B., Dillon, S., Thompson, F. and Corner, J.L. (2005), ‘‘Perceived risk, the internet shopping experience and online purchasing behavior: a New Zealand perspective’’, Journal of Global Information Management, Vol. 13 No. 2, pp. 66-88
- Fallows, D. (2005), ‘‘How men and women use the internet’’, Pew Internet and American Life Project, available at www.pewinternet.org/media/Files/Reports/2005/PIP_Women_and_Men_online.pdf
- Gurmu, E. & Etana, D.., 2014. Age at first marriage and first birth interval in Ethiopia: analysis of the roles of social and demographic factors. African Population Studies, 28(3), pp.1332-1344, doi: 10.11564/28-3-625.
- Gunes, C. (2018). Factors Behind Consumer Behavior of Online Customers. Price2Spy. [online]. available at http://www.price2spy.com/blog/consumer-behavior-of-online-customers/ Last accessed 26 Feb 2019.
- Han, K.T. et al., 2014. Is marital status associated with quality of life? Health and quality of life outcomes, 12(1), pp.109, doi: 10.1186/s12955-014-0109-0.
- Kim, E.Y. and Kim, Y.K. (2004), ‘‘Predicting online purchase intentions for clothing products’’, European Journal of Marketing, Vol. 38 No. 7, pp. 883-897.
- Lautiainen, T. (2015). Factors affecting consumers’ buying decision in the selection of a coffee brand. Saimaa University of Applied Sciences, Lappeenranta.
- McCoy, J. (2017). How Netflix Is Dominating With Their Brand Content Strategy. [online]. available at https://expresswriters.com/netflixs-brand-content-strategy/ Last accessed 26th Feb 2019.
- Morris, M.G. and Venkatesh, V. (2000), “Age differences in technology adoption decisions: implications for a changing work force”, Personnel Psychology, Vol. 53 No. 2, pp. 375-403.
- Oakley, A.., 2016. Sex, gender and society. 1st ed. London: Routledge, ISBN: 9781472435620.
- Rainwater, L.., 2017. Family design: Marital sexuality, family size, and contraception. 1st ed. NY: Routledge, ISBN-13: 978-1138523401.
- Rajyalakshmi, N. (2015). Factors influencing online shopping behavior of urban consumers in India. International Journal of Online Marketing, 5,(1), 38–50
- Ryscavage, P.., 2015. Income Inequality in America: An Analysis of Trends: An Analysis of Trends. 1st ed. NY: Routledge, ISBN-10: 0765602342.
- Srinivasan, S. (2018). Zomato hits
gold withfood delivery, annual revenue run rate at Rs 650 cr. Economic Times. Available at https://economictimes.indiatimes.com/small-biz/startups/newsbuzz/zomato-ceo-claims-100-million-in-annualised-revenue-run-rate-40-growth-in-last-2-months/articleshow/63414145.cms Last accessed on 26 Feb 2019.
- Swaminathan, V., Lepkowska-White, E. and Rao, B.P. (1999), ‘‘Browsers or buyers in cyberspace? An investigation of factors influencing electronic exchange’’, Journal of Computer-Mediated Communication, Vol. 5 No. 2,
- Thomas, L (2018). Amazon’s 100 million Prime members will help it become the No. 1 apparel retailer in the US. CNBC.
Available athttps://www.cnbc.com/2018/04/19/amazon-to-be-the-no-1-apparel-retailer-in-the-us-morgan-stanley.html Last accessed on 26th Feb 2019.
- Uppal, N., Mishra, S.K. & Vohra, N.., 2014. Prior related work experience and job performance: Role of personality. International Journal of Selection and Assessment, 22(1), pp.39-51, doi: 10.1111/ijsa.12055.
- Zheng, R. (2018). How Louis Vuitton Upped Its Digital Game in China. Jing Daily. Available at https://jingdaily.com/louis-vuitton-digital-china/ Last accessed 26th Feb 2019.
- Zhou, L., Dai, L. and Zhang, D. (2007), ‘‘Online shopping acceptance model – a critical survey of consumer factors in online shopping’’, Journal of Electronic Consumer Research, Vol. 8 No. 1, pp. 41-62
Latest posts by Jaideep Bhattacharjee (see all)
- Motivators and demotivators of online financial products - June 26, 2019
- Psychological theories of entrepreneurship - March 14, 2019
- Factors influencing consumer buying decisions - March 11, 2019