| 释义 |
- Calculation Methods
- World Demographic Profile 2017
- Demographic Profiles of the 3 Most Populated Countries in the World
- See also
- References
{{multiple issues|{{Refimprove|date=January 2015}}{{original research|date=April 2013}}
}}Demographic profiling is a tool used 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 using 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 used 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). Calculation Methods Demographic data that makes up the profile is collected through multiple ways such as censuses, surveys, records, and registries in order to keep track of things such as population, births, deaths, relationship status, and more. The Census is the most important tool when it comes to tracking this data. The United States Census was first introduced in 1790 and has been taken every 10 years since under Constitutional law [https://www.census.gov]. While the questions in the U.S. Census vary each decade, the aim is to find more about the residence within its borders and their unique characteristics from marital status, age, sex, race, education status, employment status, and location. Even though the U.S. Census is the most relied on tool for collecting this information it still has its flaws such as overcount and undercount which has caused controversy in previous years [https://www.census.gov/prod/2001pubs/censr01-2.pdf]. World Demographic Profile 2017 |
| World Population | 7,405,107,650 (July 2017) | | 10 Most Populated Countries (In Millions) | China: 1379.3 India: 1281.93 United States: 326.6 Indonesia: 260.58 Brazil: 207.35 Pakistan: 204.92 Nigeria: 190.63 Bangladesh: 157.83 Russia: 142.26 Japan: 126.45 | | Age Structure | 0-14 years: 25.44% (male 963,981,944/female 898,974,458) 15-24 years: 16.16% (male 611,311,930/female 572,229,547) 25-54 years: 41.12% (male 1,522,999,578/female 1,488,011,505) 55-64 years: 8.6% (male 307,262,939/female 322,668,546) 65 years and over: 8.68% (male 283,540,918/female 352,206,092) | | Dependency Ratio | total dependency ratio: 52.5 youth dependency ratio: 39.9 elderly dependency ratio: 12.6 potential support ratio: 7.9 | | Median Age | total: 30.4 years male: 29.6 years female: 31.1 years | | Birth Rate | 4.3 births every second | | Death Rate | 1.8 deaths every second | | Maternal Mortality | 216 deaths/100,000 live births | | Sex Ratio | at birth: 1.03 male(s)/female 0-14 years: 1.07 male(s)/female 15-24 years: 1.07 male(s)/female 25-54 years: 1.02 male(s)/female 55-64 years: 0.95 male(s)/female 65 years and over: 0.81 male(s)/female total population: 1.02 male(s)/female | | Life Expectancy | total population: 69 years male: 67 years female: 71.1 years | | Total Fertility Rate | 2.42 children born/woman | | Languages | Mandarin Chinese: 12.2% Spanish: 5.8% English: 4.6% Arabic: 3.6% Hindi: 3.6% Portuguese: 2.8% Bengali: 2.6% Russian: 2.3% Japanese: 1.7% - Percentages for "first language" speakers only; the six UN languages - Arabic, Chinese (Mandarin), English, French, Russian, and Spanish which are mother tongues for approximately half of the world's population. They are also the official languages in over half the countries in the world with more than a million first-language speakers.
- There is an estimated 7,000 languages spoken in the world and about 80% of the languages are spoken by >100,000 people and approximately 130 of those languages are spoken by >10 people.
- There is an estimated amount of 2,300 languages spoken in Asia, 2,140 in Africa, 1,310 in the Pacific, 1,060 in the Americas, and 290 in Europe.
| | Religions | Christian: 31.4% Muslim: 23.2% Hindu: 15% Buddhist: 7.1% folk religions: 5.9% Jewish: 0.2% other: 0.8% unaffiliated: 16.4% |
Source: CIA World Factbook [3] Demographic Profiles of the 3 Most Populated Countries in the World | The United States | 2017 |
|---|
| Population | 326,625,791 | | Age Structure | 0-14 years: 18.73% (male 31,255,995 per female 29,919,938) 15-24 years: 13.27% (male 22,213,952 per female 21,137,826) 25-54 years: 39.45% (male 64,528,673 per female 64,334,499) 55-64 years: 12.91% (male 20,357,880 per female 21,821,976) 65 years and over: 15.63% (male 22,678,235 per female 28,376,817) | | Dependency Ratios | total dependency ratio: 51.2 youth dependency ratio: 29 elderly dependency ratio: 22.1 potential support ratio: 4.5 | | Population Growth Rate | 0.81% | | Birth Rate | 12.5 births per 1,000 people | | Death Rate | 8.2 deaths per 1,000 people | | Net Migration | 3.9 migrant(s) per 1,000 people | | Sex Ratio | 0-14 years: 1.04 male(s) per female 15-24 years: 1.05 male(s) per female 25-54 years: 1 male(s) per female 55-64 years: 0.93 male(s) per female 65 years and over: 0.79 male(s) per female total population: 0.97 male(s) per female | | Infant Mortality | total: 5.8 deaths per 1,000 live births male: 6.3 deaths per 1,000 live births female: 5.3 deaths per 1,000 live births | | Ethnic Groups | White: 72.4% Black: 12.6% Asian: 4.8% Amerindian and Alaska native: 0.9% native Hawaiian and other Pacific Islander: 0.2% Other: 6.2% 'Two or more races: 2.9% | | Maternal Mortality | 14 deaths per 100,000 live births | | Education Expenditure | 4.9% of GDP | | Languages | English: 79% Spanish: 13% other Indo-European: 3.7% Asian and Pacific island: 3.4% Other: 1% | | Religions | Protestant: 46.5% Roman Catholic: 20.8% Jewish: 1.9% Mormon: 1.6% other Christian: 0.9% Muslim: 0.9% Jehovah's Witness: 0.8% Buddhist: 0.7% Hindu: 0.7%: other: 1.8% unaffiliated: 22.8% don't know/refused: 0.6% | | Total Fertility Rate | 1.87 children born per woman | | Life Expectancy at Birth | total population: 80 years male: 77.7 years female: 82.2 years |
Source: CIA World Factbook [4] | China | 2017 |
|---|
| Population | 1,384,688,986 | | Age Structure | 0-14 years: 17.15% (male 127,484,177/female 109,113,241) 15-24 years: 12.78% (male 94,215,607 per female 82,050,623) 25-54 years: 48.51% (male 341,466,438 per female 327,661,460) 55-64 years: 10.75% (male 74,771,050 per female 73,441,177) 65 years and over: 10.81% (male 71,103,029 per female 77,995,969) | | Dependency Ratio | total dependency ratio: 37.7% youth dependency ratio: 24.3% elderly dependency ratio: 13.3% potential support ratio: 7.5% | | Population Growth | 0.41% | | Death Rate | 7.8 deaths per 1,000 people | | Birth Rate | 12.3 births per 1,000 people | | Sex Ratio | at birth: 1.15 male(s) per female 0-14 years: 1.17 male(s) per female 15-24 years: 1.14 male(s) per female 25-54 years: 1.04 male(s) per female 55-64 years: 1.02 male(s) per female 65 years and over: 0.92 male(s) per female total population: 1.06 male(s) per female | | Maternal Mortality | 27 deaths per 100,000 live births | | Infant Mortality | total: 12 deaths per 1,000 live births male: 12.3 deaths per 1,000 live births female: 11.7 deaths per 1,000 live births | | Life Expectancy | Average: 75.7 years male: 73.6 years female: 78 years | | Total Fertility Rate | 1.6 children born per woman | | Ethnic Groups | Han Chinese: 91.6% Zhuang: 1.3%, other: 7.1% (Hui, Manchu, Uighur, Miao, Yi, Tujia, Tibetan, Mongol, etc.) | | Religions | Buddhist: 18.2% Christian: 5.1% Muslim: 1.8% folk religion: 21.9% Hindu: < 0.1% Jewish: < 0.1% other: 0.7% (Daoist or Taoist) unaffiliated: 52.2% | | Languages | - Standard Chinese or Mandarin- Yue (Cantonese)- Wu (Shanghainese)- Minbei (Fuzhou)- Minnan (Hokkien-Taiwanese)- Xiang- Gan | | Literacy | total population: 96.4% male: 98.2% female: 94.5% |
Source: CIA World Factbook [5] | India | 2017 |
|---|
| Population | 1,281,935,911 | | Age Structure | 0-14 years: 27.34% (male 186,087,665 per female 164,398,204) 15-24 years: 17.9% (male 121,879,786 per female 107,583,437) 25-54 years: 41.08% (male 271,744,709/female 254,834,569) 55-64 years: 7.45% (male 47,846,122 per female 47,632,532) 65 years and over: 6.24% (male 37,837,801 per female 42,091,086) | | Dependency Ratio | total dependency ratio: 52.2% youth dependency ratio: 43.6% elderly dependency ratio: 8.6% potential support ratio: 11.7% | | Population Growth | 1.17% | | Birth Rate | 19 births per 1,000 people | | Death Rate | 7.3 deaths per 1,000 people | | Sex Ratio | at birth: 1.12 male(s) per female 0-14 years: 1.13 male(s) per female 15-24 years: 1.13 male(s)/female 25-54 years: 1.06 male(s)/female 55-64 years: 1.01 male(s)/female 65 years and over: 0.9 male(s)/female total population: 1.08 male(s)/female | | Infant Mortality | total: 39.1 deaths per 1,000 live births male: 38 deaths per 1,000 live births female: 40.4 deaths per 1,000 live births | | Life Expectancy | total population: 68.8 years male: 67.6 years female: 70.1 years | | Total Fertility Rate | 2.43 children born per woman | | Maternal Mortality | 174 deaths per 100,000 live births | | Ethnic Groups | Indo-Aryan: 72% Dravidian: 25% Other: 3% | | Religions | Hindu: 79.8% Muslim: 14.2% Christian: 2.3% Sikh: 1.7% other and unspecified: 2% | | Languages | Hindi: 41% Bengali: 8.1% Telugu: 7.2% Marathi: 7% Tamil: 5.9% Urdu: 5% Gujarati: 4.5% Kannada: 3.7% Malayalam: 3.2% Oriya: 3.2% Punjabi: 2.8% Assamese: 1.3% Maithili: 1.2% other: 5.9% | | Literacy | total population: 71.2% male: 81.3% female: 60.6% |
Source: CIA World Factbook [6] See also- Demography
- Elderly
- Geodemography
- Market segmentation
- Mass marketing
- Niche market
- Population profiling
- Precision marketing
- Psychographic
- Psychological development
- Target market
- Sex Ratio
- Population pyramid
- Demographics of India
- Demographics of China
References1. ^1 {{Cite web|url=https://www.measureevaluation.org/resources/training/online-courses-and-resources/non-certificate-courses-and-mini-tutorials/population-analysis-for-planners/lesson-3|title=Lesson 3: Creating a Demographic Profile — MEASURE Evaluation|website=www.measureevaluation.org|language=en-us|access-date=2017-03-29}}
2. ^{{Cite journal|last=Treiman|first=Donald J.|last2=Lu|first2=Yao|last3=Qi|first3=Yaqiang|date=2012-01-01|title=New Approaches to Demographic Data Collection|journal=Chinese sociological review|volume=44|issue=3|pages=56|issn=2162-0555|pmc=3704565|pmid=23844330}}
3. ^https://www.cia.gov/library/publications/the-world-factbook/
4. ^https://www.cia.gov/library/publications/the-world-factbook/
5. ^https://www.cia.gov/library/publications/the-world-factbook/
6. ^https://www.cia.gov/library/publications/the-world-factbook/
- 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 http://search.proquest.com/docview/233640609
- GfK. (2016). Tech Trends 2016: Understanding the driving forces behind the connected consumer. Retrieved from
- http://www.warc.com/Content/ContentViewer.aspx?MasterContentRef=798d01f2-8167-472c-9f21-9f80c2cd0e43&q=(%22metadata%22)+AND+(demographic+OR+marketing)&CID=A106693&PUB=GFK
- 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 http://search.proquest.com/docview/1776777815
- Needel, S. (2013). Why Big Data is a Small Idea: And why you shouldn't worry so much. ESOMAR: Congress, Istanbul. Retrieved from http://www.warc.com/Content/ContentViewer.aspx?MasterContentRef=d6a6c104-3a25-46e3-9bcc-f3526be49f9d&q=(%22metadata%22)+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
- http://www.warc.com/Content/ContentViewer.aspx?MasterContentRef=91f96347-8a91-4eda-a3f5-e0871f918109&q=(%22metadata%22)+AND+(demographic+OR+marketing)&CID=A100206&PUB=ESOMAR
- "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.
{{DEFAULTSORT:Demographic Profile}} 2 : Demographics|Market segmentation |