Demographic Data


Demographic data is information about groups of people according to certain attributes such as age, gender, place of residence, and can include socio-economic factors such as occupation, family status, or income. In web analysis and online marketing, demographic data is used to provide deeper insights into a website’s target audience or in order to create personas. Demographic data is used above all to strategically adapt offers to certain target groups, and can be used as a basis for business analyses and performance reports.

Functions[edit]

Demographic data and interests belong to some of the most important statistics in web analysis, consumer analysis and targeting. In contrast to data collection in the academic field of demographics, the focus of demographic data in marketing is less about data regarding fertility and mortality, but rather about age, gender and interests.

Collection of demographic data[edit]

Demographic data is collected by software solutions such as Google Analytics. Here, a subset of the total number of users is used to extrapolate data for the total number of users. The software collects this data using different protocols that ensure tracking. For instance, geographical and language information is collected in the communication between the server and client. Through the use of cookies or event tracking, more information about the gender and interests is recorded and saved.

The information is acquired from the Google Display Network and participating partners. It therefore makes sense to use this data for advertisements. To be able to collect demographic data, a corresponding tracking code must be integrated or altered accordingly.

In addition, activation in the account is necessary for one to be able to use the functions as reports. Here, it is important to also take notice of eventual changes in the Google Analytics Privacy Policy on the website before activating the new functions.

Apart from the use of data via google products, website operators have the opportunity to use their own data to collect and segment demographic data. This way, online shops can analyze and evaluate their own customer data to optimize advertising campaigns. They do however have to keep to data protection.

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Examples of demographic characteristics[edit]

  • Age: Age is one of the most important demographic factors. It is a good indicator of the user groups that visit a website, as well as the age groups that purchase the most. It provides information about what content on the website is interesting for a particular age group, and where potential can be identified.
  • Gender: Information about the gender shows which parts of a websites or which products are more suitable for men or women. Sorting hits based on gender can be used as a basis to plan campaigns that are targeted to men or women.
  • Interest: Data about users' interests show what visitors to a website are interested in, and they make it possible to draw conclusions on consumer behavior. For instance, if there is affinity to specific product categories from the side of the users, sellers can come up with advertisements that focus on these interests.

Furthermore, this allows for segmentation of user groups e.g., in order to establish a connection between persons aged from 18 to 24 years with certain keywords and interests. This type of segmentation is extremely useful for any remarketing campaigns.

Possible applications[edit]

The possible scenarios for the use of demographic data are complex. Reports related to demographic data could provide answers to the following questions:

  • What groups of users visit the website? Young users have interests that differ from those of older users.
  • Which of these groups provide the most revenue? The most profitable clientele is usually from a certain age group.
  • Where should content be placed in order to increase sales? Relevant content can be tailored to age, gender, and interests.
  • How can advertisements be targeted in a better way? Young female users want to see different types of advertisements compared to older male users.
  • Which factors improve remarketing? Through the segmentation, downstream measures can be tailored to the target group and the corresponding interests.
  • How can e-mail campaigns be more efficient and targeted more directly to certain groups? In this case, newsletters or emails can be sent to particular groups.

Conclusion[edit]

Compared to conventional tracking, demographic data in web analysis provides a much deeper insight into the user behavior. Information about user groups can be used to improve the effectiveness of advertisement campaigns, optimize the website’s offer, and last but not least, boost sales. Although a detailed understanding of the software used is required, this effort pays off in the long term., particularly when demographic data is used for the strategic alignment of the product range, advertisement measures, and remarketing. At the same time, the lawful use of this data should not be disregarded – the data should be anonymous, and the user must be informed about the collection of data as well as the use of cookies. At the same time, users must have the opportunity to conflict data collection.

Web Links[edit]