User profiles automatically assign target groups
Target-Group definition as the first step in marketing automation
Double Income No Kids, Big 5 target groups, millennials and more
The target group mapper automatically sorts your customer data into target groups based on intelligently selected user profiles. The target group portfolio contains already defined target groups with complete algorithms for profiling. The finished target group definition contains both general target groups, for example according to age, income or DINK – Double Income No Kids, as well as industry-specific target groups. For example for the travel industry, insurance companies or energy suppliers. Of course, you can also define the target group manually. With the target group generator, which is part of the target group matcher, you can freely create your own target groups. The target group mapper is a web-based application in our cloud that you can test free of charge or use productively immediately.
Advantages of the Target Group Matcher
- With the target group generator you always have your target groups immediately and clearly at hand
- Complete transparency as to which customer data is used for which target groups
- All target group types are already stored
- Customers are automatically divided into one or more target groups
- Possibility of defining your own target groups
- Automatic assignment of customers to self-defined target groups
- Save self-defined target group definitions
- Transfer of target groups into third-party campaign management systems
- export of target groups to CRM systems, but also to Excel
- increase in sales figures
- increase in productivity
Save a lot of time and effort when creating target group clusters. Optimise your target groups after each campaign, whether for newsletter marketing, banner advertising or display advertising – for programmatic advertising as well as for manual campaign management.
Start your target group management with a customized
Automatically define target groups, – the first Automatically define target groups, – the first
Step towards marketing automation.
What target groups does the target group matcher handle?
All relevant target groups are stored in the target group mapper. The target groups are divided into generally valid and industry-oriented customer target groups.
The general target groups are divided into
However, also in the consumer psychological Big 5 personality target groups
- Open-mindedness for experiences (open-mindedness)
- Conscientiousness (Perfectionism)
- Extraversion (sociability)
- Compatibility (willingness to cooperate, empathy)
- Neuroticism (emotional instability and vulnerability)
The target groups are subordinated to the general target groups according to income, attitude to life and generations and their proven characteristics.
Target groups according to income, age or other characteristics
The target group mapper allows you to filter your customer data into different income groups. However, you can also add other sorting characteristics to the income, such as age, interests or lifestyle.
Filter settings can thus be made to identify, for example, subsequent target groups within your customers:
- Buppie, Tuppie, …Xuppie. Tuppi = turkish young urban professionals. These young career-conscious people live in cities and have cultural affinities. By “X “uppie we mean different cultural affinities which are mainly present in Germany, Austria and Switzerland (e.g. Italian, Spanish, Turkish, African-American, Polish or Arab cultural affinities).
- Skipp‘ies = school kids with income and purchase power. These are students with income and purchasing power
- Rumpie = rural upwardly-mmobile professionals. This target group consists of young people from rural areas who are rising and career-oriented
- woopie = well-off older people. These are financially secure seniors with purchasing power
- Yuppie = young urban professional. These are young, career-oriented urban people without cultural affinities
- Yuspie = young urban single professionals. This important target group is willing to spend, has an academic degree and works in high-income positions
- WOOF = well off older folks. These are wealthy seniors whose income is still slightly higher than that of the Woopies.
- SOHO = small office home office. These target groups are often micro-entrepreneurs or freelancers, often freelance people in the IT sector, e.g. system administrators, software developers, etc.. Increasingly, however, other freelance occupations are also included in this target group
- DINK = double income no kids. These are childless married couples in whom both spouses are employed
- LOHAS = lifestyle of health and sustainability. People within this target group cultivate lifestyles. For example, especially focused on nutrition, sports or environmental awareness. There are special consumer types
- LOVOS = lifestyle of voluntary simplicity. LOVOS “consumers” prefer a life oriented towards minimalism. It is a counter-movement to the consumption-fixed affluent society. By not consuming, this target group wants to free itself from everyday constraints in order to lead a self-determined life
Target groups according to generation
- The target group mapper contains the generation Z, which was born after 1999
- Even the millennials are depicted in the target group generator. 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 the generation target groups into Digital Natives and Digital Immigrants are possible. 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. It belongs, like the Boomers, to the Digital Immigrants.
Functional principle of the target group matcher
Below is a simplified example of how the Target Group Matcher works. The procedure for analyzing the “email address” data record is similar for all other data sources. In this way, all customer records are examined and analysed.
Identification and analysis of data – Procedure
The information coming from applications is divided into elements and attributes and evaluated. The assignment to target groups is based on the type, number and evaluation of the elements and attributes.
Example: An email address is available as a customer data record from a newsletter system. The email address is firstname.lastname@example.org
This email address would now provide information about the elements (facts) first name, last name, type of separator, type of provider and top-level domain DE.
These elements would give attributes (derivatives).
- first name = German, probability of occurrence: 80%
- first name = female, probability of occurrence: 90%
- last name = Turkish, probability of occurrence: 70%
- surname = widely used, probability of occurrence: 80%
- separator is a dot = widely used (dots are the most popular separators, followed by hyphens, underscores and numbers (e.g. date of birth or license plates), likelihood of occurrence: 60%
- Email-Provider = German, probability of occurrence: 90% (United Internet, to which the GMX domain belongs, is also popular in Austria and Switzerland)
- Email-Provider = highest number of e-mail address registrations between 1990 and 2001
- Email-Provider = high distribution in Germany
- top-level domain (TLD) = .de (Germany)
Target group determination
Using the elements and attributes described above, the Target Group Matcher could define the following target group affiliations (probabilities of occurrence not listed in this example):
- It’s a woman
- with German citizenship
- a residence in the Federal Republic of Germany
- married to a Turkish man
- therefore cultural affinity to Turkey
- celebrates Turkish festivals (e.g. sugar festival) or has knowledge thereof
- has knowledge of several religions (Christianity and Islam)
- in the age group 32 to 40 years – Generation Y (conclusion age: first name widespread (especially in the years 1983 to 1995 in relation to the number of newborns), separators widespread, surname widespread; the chance to register widespread first names and surnames with popular email providers is very low; the e-mail address must have been registered in the early days of GMX – probably in the 1990s. It can be assumed that the lady was at least 16 years old at the time of email address registration at GMX)
- Resistance (conclusion: no change in email address since inception)
- low data protection sensitivity (conclusion: open handling of first names and surnames towards the outside world)
- Personalization desired (Salutation with name)
- Travels to Turkey once a year probably
- Mother (conclusion: German women who are married to men from Mediterranean regions are more than 80% likely to be mothers)
- Income: below average to average
All target group characteristics have different valuations or probabilities of occurrence, which you can change at any time.
A target group can only be defined by one characteristic (e.g. income) or by several characteristics simultaneously (e.g. income, cultural affinity, age). Target group characteristics can be linked both AND and OR.
All data sources are divided into elements and attributes. The data sources often have different elements and attributes. For example, the profile of a website visitor (“what did the prospective customer see when, how often and for how long, which device did he use”, etc.) has different elements and attributes than a CRM/booking system (“what did the customer buy when last”).
The target group mapper has already integrated the elements and attributes of most data sources.
What data sources can be linked to the target group matcher?
Interfaces of numerous data sources to the target group matcher already exist. Your internal systems, which contain customer data, can thus be easily docked.
- For example, CRM systems such as Microsoft Dynamics CRM, Sage CRM, CAS CRM, or CRM applications from SalesForce.
- But also email systems, for example MS Exchange, Tobit or Lotus Notes, contain important customer data (especially email addresses that are already sufficient for a rough target group allocation – see MailStone.
- Complaint management, support or ticket systems for processing customer inquiries
- Profiles from web tracking systems (e.g. eTracker)
- Revables management systems
- Booking systems
- Midoffice and backoffice systems from Midoco
- CSV sheets from Excel or Open Office
If your application is not included, we will be happy to develop an interface for you.
We guarantee the highest data protection standards for all our corporate customers.
All data is treated in accordance with Eurpean or the even more restrictive Swiss data protection regulations.
The data is stored in Germany.
- You can delete the data and evaluation results at any time
- If you test our product, the data will only be cached into a volatile memory and will not be stored on hard disks or databases in our data center.
- The cache is deleted automatically after 24h.
- We do not pass on your data to other companies or private individuals.
- We do not use your data for internal advertising purposes.