Demographic Profile
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Demographic Profile

Demographic profiling is a tool utilized by marketers so that they may be as efficient as possible with advertising products or services and identifying any possible gaps in their marketing strategy.[1] Demographic profiling can even be referred to as a euphemism for corporate spying (Hudson, J. 2002). By targeting certain groups who are more likely to be interested in what is being sold, a company can efficiently expend advertising resources so that they may garner the maximum number of sales (Arnott, D., & FitzGerald, M. 1996). This is a more direct tactic than simply advertising on the basis that anyone is a potential consumer of a product; while this may be true, it does not capitalise on the increased returns that more specific marketing will bring (Jothi, A. L. 2015). Traditional demographic profiling has been centered around gathering information on large groups of people in order to identify common trends (GfK. 2016). Trends such as, but not limited to: changes in total population and changes in the composition of the population over a period of time. These trends could promote change in services to a certain portion of the population, in people such as: children, elderly, and the working age population.[1] They can be identified through surveys, in-store purchase information, census data, and so on (Arnott, D., & FitzGerald, M. 1996). New ways are also in the works of collecting and utilizing information for Demographic Profiling. Approaches such as target-sampling, quota-sampling, and even door-to-door screening.[2]

An effective means of compiling a comprehensive demographic profile is the panacea of marketing efforts. To know a person's name, ethnicity, gender, address, what they buy, where they buy it, how they pay, etc., is a powerful insight into how to best sell them a product (GfK. 2016). The development of this profiling is the goal of many businesses around the world, who are pouring huge amounts of money into researching it. A recent discovery that has drastically changed the way we construct demographic profiles, is metadata (Needel, S. 2013). This is the digital footprint left behind of everyone who uses online services. The more extensive a user's usage, the more extensive the information available on them and their interests. Companies such as Google and Facebook make enormous profits through the generation and processing of metadata, which can then be utilised by companies wishing to streamline their advertising to those best suited to seeing it (targeted advertising). This is what controls the ads on a user's news feed, or websites they visit (Needel, S. 2013), and means that for example, an avid mountain biker, is more likely to come across ads aiming towards that interest. For another example, for young girls who often visit online shopping stores, when on a social media account such as Facebook, the pop-up ads are more likely to concern recent stores they've visited or stores similar to. Metadata includes information such as the amount of time spent on a website, what websites a user frequently visits, where/what they clicked and how many times, what they've purchased, whom they have talked to, and what they have purchased. It is so pervasive that most of what people do online contributes to the information being held about them by businesses, and will directly affect what is advertised and shown to them when using an online browser and what mediums this is done through (GfK. 2016).

The gathering of metadata has proven to be a controversial topic, with large numbers of people around the world expressing discomfort at the idea of their personal information is being used to generate a virtual profile of themselves for businesses to take advantage of (Needel, S. 2013). This leads to businesses needing to progress with caution in this field, and not go too far with how they use this information. To avoid future legislation being enacted that would seek to limit the collection of metadata, companies must act ethically and have people's privacy in mind when they target people for advertising (Needel, S. 2013). An example of how this could become an issue is presented by Vastenavondt, J., & Vos, K., & Ewing, T., & Wood, O. (2013), who propose the idea of a virtual reality shopping programme. Within this programme, the shopper is greeted by a virtual attendant who knows them by name and suggests an array of suitable clothing options based on their past purchases. The shopper is delighted by the seamless nature of this shopping experience, until it come time to make a purchase. When buying the items the shopper has picked out, they opt to use their credit card. They are then asked by the virtual attendee if they are sure they would like to use that option, as their credit history suggests that cash would be a wiser option and that they wouldn't want to default on their payments as they have in the past. This highlights the need for discretion in the extent to which information is gathered, and how it is applied (Vastenavondt, J., & Vos, K., & Ewing, T., & Wood, O. 2013).

See also


  1. ^ a b "Lesson 3: Creating a Demographic Profile -- MEASURE Evaluation". Retrieved . 
  2. ^ Treiman, Donald J.; Lu, Yao; Qi, Yaqiang (2012-01-01). "New Approaches to Demographic Data Collection". Chinese sociological review. 44 (3): 56. ISSN 2162-0555. PMC 3704565 Freely accessible. PMID 23844330. 
  • Arnott, D., & FitzGerald, M. (1996). Understanding demographic effects on marketing communications in services. International Journal of Service Industry Management, 7(3), 31-45. Retrieved from
  • GfK. (2016). Tech Trends 2016: Understanding the driving forces behind the connected consumer. Retrieved from
  • Hudson, John. "Ubiquity: Demographic profiling." Acm - an acm publication. N.p., 22 Nov. 2002. Web. 29 Mar. 2017.Jothi, A. L. (2015). A study on influence of demographic factors on customers' preference towards cosmetic products. Sumedha Journal of Management,4(4), 39-48. Retrieved from
  • Needel, S. (2013). Why Big Data is a Small Idea: And why you shouldn't worry so much. ESOMAR: Congress, Istanbul. Retrieved from'metadata')+AND+(demographic+OR+marketin
  • g)&CID=A100226&PUB=ESOMAR
  • Vastenavondt, J., & Vos, K., & Ewing, T., & Wood, O. (2013). Feel Nothing, Do Nothing: Unlocking the emotional secret of online spending. ESOMAR: Congress, Istanbul. Retrieved from
  • "Lesson 3: Creating a Demographic Profile." Lesson 3: Creating a Demographic Profile - MEASURE Evaluation. N.p., 11 Dec. 2015. Web. 29 Mar. 2017. ~~~
  • Treiman, Donald J., Yao Lu, and Yaqiang Qi. "New Approaches to Demographic Data Collection." Chinese Sociological Review. U.S. National Library of Medicine, 2012. Web. 29 Mar. 2017.

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