Target groups2019-09-02T17:21:15+01:00
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Finding the Right Target Groups with Data Analytics

Target group-based marketing is essential. So which target groups are right for your service? And which target groups are right for your products? With Google Analytics, for example, it is possible to identify specific target groups. The target groups of Google, however, are very generalistic. And they are also strongly oriented towards end customers/consumers. Most companies are therefore using their own data analytics methods to find the right target groups. But finding the right target groups is not that easy. In particular, because some target groups change quickly, or because the target groups interact with each other and can overlap as well.

Data Analytics for target-group definition is complex

Why is it difficult to identify and define target groups? There are several reasons for this:

  • The target groups are in constant interplay with each other
  • People are in several target groups at the same time with different intensity
  • People leave target groups again, e.g. through media influence
  • The parameters on the basis of which people are assigned to different target groups change or new parameters are added
  • The allocation of people to target groups is culture and market dependent
  • Some of the parameters used to define target groups are time critical and dependant on time
  • The collection of data that can be used to identify people as target groups is made harder by data protection laws
  • Some target groups are only relevant for specific products or services
  • For price-sensitive target groups, further factors must be taken into account – see Dynamic pricing

Basically, the definition of the goal is based on the human being, their environment and the ensuing (financial) possibilities. A primary subdivision is achieved with these general terms:


  • Sociographic
  • Socio-economic
  • Demographic

Psychographic target groups – the most important parameters for data analytics

During childhood, the following personality parameters are already formed, which in turn are assigned to target groups (Big 5 target groups):

  • Open-mindedness (openness for (new) experience)
  • Conscientiousness (Perfectionism)
  • Extraversion (sociability)
  • Compatibleness (willingness to cooperate, empathy)
  • Neuroticism (emotional instability and vulnerability)
Big5 Target groups by Data Analytics

Data Analytics to assign customers by personality target groups

These psychographic Big 5 target groups, which are formed in childhood, do not change for most people. They remain in effect for the rest of their lives. Companies can work with these Big 5 target groups for a long time. In the field of data analytics, knowledge of those parameters that can be used to assign customers to these target groups is particularly valuable.

Sociographic and socio-economic target groups – characteristics for data analytics

These two target groups are often directly related. Sociography describes the constraints exerted on people or societies that lead to defined actions (empirical social research by sociologist Émile Durkheim).

Socioeconomic target groups which marketing departments in companies frequently work with

Marketing in companies differentiates the following target group definitions:

  • Buppie, Tuppie, …Xuppie. Tuppi = turkish young urban professionals. These young and career conscious individuals live in cities and have cultural affinities. By “X “uppie we mean different cultural affinities that are mostly present in Germany, Austria and Switzerland (e.g. Italian, Spanish, Turkish, Afro-American, Polish or Arab cultural affinities).
  • Skipp‘ies = school kids with income and purchase power. These are students with an income and a purchasing power
  • Rumpie = rural upwardly-mmobile professionals. This target group consists of aspiring and career-oriented young individuals from rural areas
  • Woopie = well-off older people. These are financially safe elderly persons with a high spending power
  • Yuppie = young urban professionals. These are young, career-oriented city dwellers without cultural affinities
  • Yuspie = young urban single professionals. This important target group is eager to spend, has an academic degree and works in high income positions
  • WOOF = well off older folks. These are wealthy seniors with incomes slightly higher than those of the Woopies.
  • SOHO = small office home office. These target groups are often micro-entrepreneurs or freelancers, often freelance IT professionals, e.g. system administrators, software developers, etc. Increasingly, there are other freelance professions that are also grouped under this target group
  • DINK = double income no kids. These are childless married couples where both spouses are job holders
  • LOHAS = lifestyle of health and sustainability. People in this target group are cultivating different lifestyles. Particularly focused, for example, on nutrition, sports or environmental awareness. These are special consumer types
  • LOVOS = lifestyle of voluntary simplicity. LOVOS = “Consumers” prefer a life aimed at minimalism. It is a counter-movement to the consumer-fixed affluent society. By renouncing consumption, this target group wants to free itself from everyday constraints in order to lead a self-determined life

Sociographic and socioeconomic target group parameters from scientific perspective

The target groups listed above, with whom marketing likes to work so much, are important for some products or services, but they are rarely fully relevant. But there are many other parameters for the definition of socio-economic target groups. Once the data is available, all the parameters for defining socio-economic target groups should be considered with varying emphasis. The target group definition parameters are generally weighted differently for each product and service (HighPots has developed a special weighting editor with a machine learning module).

    The following parameters for the definition of socio-economic and sociographic target groups have to be considered:

  • Gender
  • age
  • family status, family structure
  • Nationality, Migration and Migration Background
  • Regional Affiliation
  • Religious affiliation
  • Household size
  • Children in the household
  • Education
    • School Education (CASMIN Classification – Comparative Analysis of Social Mobility in Industrial Nations)
      • 3b Higher education degree
      • 3a University of applied sciences degree
      • 2c_voc_technical college entrance qualification/Abitur and vocational training
      • 2c_gen A-levels (Fachhochschulreife/Abitur) without vocational training
      • 2a Intermediate school leaving certificate and vocational training
      • 2b Intermediate school leaving certificate without vocational training
      • 1c lower secondary school leaving certificate and vocational training
      • 1b lower secondary school leaving certificate without vocational training
      • 1a no termination
    • Highest Vocational Education and Training Degree
  • Job
    • Employment status, occupational activity
    • Income
    • Professional position
    • Professional prestigiousness
      • ISIC (UNO classification, structuring by economic sectors and branches of industry, relevant for international corporations)
        • A – Agriculture, forestry and fishing (Agriculture, forestry and fishery)
        • B – Mining and quarrying
        • C – Manufacturing
        • D – Electricity, gas, steam and air conditioning supply
        • E – Water supply; sewerage, waste management and remediation activities
        • F – Construction
        • G – Wholesale and retail trade; repair of motor vehicles and motorcycles
        • H – Transport and storage
        • I – Accommodation and food service activities
        • J – Information and communication
        • K – Financial and insurance activities
        • L – Real estate activities
        • M – Professional, scientific and technical activities
        • N – Administrative and support service activities
        • O – Public administration and defence
        • P – Education
        • Q – Human health and social work activities
        • R – Arts, entertainment and recreation
        • S – Other service activities
        • T – Private households as employers; undifferentiated goods- and services-producing activities of households for own use
        • U – Activities of extraterritorial organisations and bodies
      • ISCED (UNESCO Classification – International Standard Classification of Education, relevant for international corporations))
        • 0 – Early childhood Education (02 Pre-primary education)
        • 1 – Primary education
        • 2 – Lower secondary education
        • 3 – Upper secondary education
        • 4 – Post-secondary non-tertiary education
        • 5 – Short-cycle tertiary education
        • 6 – Bachelor or equivalent
        • 7 – Master or equivalent
        • 8 – Doctoral or equivalent
      • KldB (classification of occupations for companies operating in Germany)
        • occupational areas
        • Main occupation groups
        • occupational groups
        • Professional subgroups
        • Vocational categories
  • Social stratum, Social class, Human capital
    • Economic Capital
    • Capitalists
    • Petty bourgeoisie
    • Wage-dependent middle class
    • working class
      • of which productive workers
      • of which non-productive workers
      • of which unemployed
    • Cultural capital
    • Social capital
    • Symbolic capital
  • Social status, socioeconomic status
    • formal education and school-leaving qualifications
    • Training and studies
    • Work and income
    • Possession of cultural property (often captured through book ownership)
    • cultural practice: visits to theatres and museums
    • Residence and ownership
    • Liquidity and creditworthiness
  • Health
  • Media competence

Target groups according to generations

The subdivision of target groups according to generations is very popular. It is indeed a persistent topic in the media, in particular concerning “baby boomers” and “millennials”.

  • The Generation Z Born after 1999
  • The Millennials were born between 1980 and 1999 and are also referred to as Generation Y (short for Gen Y) or Generation Me
  • The Generation X, born between 1966 and 1980, and the
  • Boomers born before 1965 round off the generation classification in the target group mapper

Subdivisions of generational target groups into Digital Natives and Digital Immigrants are also popular. The Digital Natives, also known as Generation Media or Generation Internet, were born after 1980. The Digital Natives therefore include the Millennials as well as Generation Z. Generation X no longer belongs to the digital natives; it is also called generation C64. Like the Boomers, they belong to the group of digital immigrants.

The characteristics assigned to these target groups are often incomplete and incorrect. For example, more media and IT competence is attributed to the digital natives. Studies show that this is wrong. The digital immigrants, or the C64 generation, were much more often concerned with the programming, design and functioning of computers. Although the digital natives grow up in the digital world, they are more concerned with the operation of apps than with programming them.

For this reason, the parameters that these target groups supposedly define should be scrutinized.

B2B Target groups – when companies are the clients, which parameters are used for Data Analytics?

Many companies have other companies as their clients. Therefore, marketing and sales departments need to ponder upon other businesses. Wikipedia and other online dictionaries distinguish between B2B target groups (companies) and B2C target groups (consumers). B2B target groups are roughly divided as follows:

  • organisational characteristics (size of enterprise, location of enterprise, market share, etc.)
  • economic characteristics (finances, liquidity, stocks, etc.)
  • Buying behavior of the company (merging buying center, supplier loyalty, time of purchase, etc.)
  • personal characteristics or characteristics of decision-makers in enterprises (information gathering, time pressure, innovative spirit, etc.)

Organizational characteristics for B2B target groups

Some of the organizational characteristics can be easily identified. Company size and Company location can be determined via commercial registers. The market shares are, depending on the industries, more difficult to record.

The activities of the personnel department of a company are another important characteristic for B2B target group identification. Important key figures are:

  • number of new employees
  • Number of employees leaving the company
  • What kind of new employees are being looked for (computer scientists, engineers, scientists, project managers, etc.)

The marketing behaviour of a company is also relevant for the classification of B2B target groups. The following parameters for B2B target groups are relevant for access:

  • Number of publications
  • Number of publications related to a specific product or service
  • Channels used

Economic characteristics of B2B target groups

The economic characteristics of a company are difficult to find out. Of course, companies listed on stock exchanges have to publish their financial data. But an economic area does not only consist of listed companies. The financial data of GmbH are more difficult to determine. In some countries a few financial data of a company are published. In Germany, for example, the assets and liabilities of a company are published. However, this does not make a liquidity analysis possible. The quality of data from companies that evaluate other companies, such as Creditreform, is not very reliable. Measuring the activities of marketing and HR departments is usually a better alternative to assessing liquidity.

How can the purchase behaviour of another company be identified? In order to include the organizational purchase behavior of companies in the B2B target group definition, a great deal of information must be taken into account. Knowledge about the purchasing processes in the companies is important. Participants in the procurement process are divided into the following roles:

  • Users (user): are the members of an organization using the products and services.
  • influencers: like technical personnel and engineers who provide information and thereby influence decisions.
  • buyers: are those with formal responsibility and authority, especially in supplier matters.
  • deciders: are the management and the buyers
  • Informant (gatekeeper): are buyer and secretary. They control the internal flow of information and the influx of new information.

Knowledge about the distribution of power among these roles is very important for suppliers. Without “insiders” it is difficult to obtain this knowledge.

Personal characteristics in the B2B target group definition

  • The personal characteristics are dependent on
  • their roles in the procurement process (see above)
  • the procurement situation (time pressure)
  • the character traits (innovative, willing to take risks, conservative, willing to spend, etc.)
  • the relationship between you as the supplier and the potential customer (sympathy)

It is a complex matter to use data analytics in marketing, especially data analysis for target group allocation

Some assignments of customers to certain target groups rarely change. However, many customer-to-target group assignments are constantly changing. Sometimes even in real time. In addition, the target groups also are changing. New events, such as trends, require the creation of new target groups. Some of these target groups are effective for many years. Other target groups, on the other hand, become obsolete after just one week and may be deleted. Managing the allocation of customers to target groups is difficult. Keeping target groups up to date is just as difficult. The interaction and the dependencies of both areas, on the one side the customer-target group allocation and on the other side the proper and current target group definitions, is very intricate. All the more so as time plays an important role, too. This can no longer be done manually. A real-time data analysis method combined with a machine learning component is a must for these tasks. Our Marketing Data Analysts and our Data Scientists target groups have experience in these areas. Talk to our reference customers and contact us. We are at your disposal by telephone, chat and e-mail. Or simply write us via our Webform.