How do you determine a target group’s typical age category?
Define your target group’s age category with clear boundaries and evidence. Separate age, life stage and career stage, with a practical numerical example.
‘Our target group is aged 35–44 and entering its first management role.’ That sounds specific, but it contains two separate claims. One concerns age; the other concerns a person's career. Each needs its own evidence. The Typical age category field shows which ages occur in a target group. Add life stage and career stage when they help describe the choice context, supported by their own data. This makes the field useful without turning a birth year into an explanation of behaviour.
What is a typical age category?
A typical age category is an explicitly bounded age group that you describe as characteristic of the researched target group using a named data source. Explain what ‘typical’ means: for example, the most common category within a predefined classification.
State the reference group, measurement date, counts and missing data. Keep an observed age distribution separate from life stage, career stage and reasons for choosing. Without data, the age category is unknown or a hypothesis awaiting investigation.
How do age, life stage and career stage differ?
| Concept | Meaning in this profile | Example of a separate measure |
|---|---|---|
| Chronological age | Age in completed years at a stated time | Age on the date of participation |
| Age category | Agreed interval of ages | 35 through 44 years |
| Life stage | Relevant life situation or transition | Living independently for the first time |
| Career stage | Relevant position or transition in working life | Managing a team for the first time |
| Role tenure | Time spent in the current role | Eight months in the current position |
| Decision authority | Mandate for a particular decision | Approval up to a documented budget |
These are practical definitions for the target group profile. Life stage does not mean a universal sequence that everyone follows at the same age. Ask about the actual situation relevant to the study. A first management role is also different from recently joining an employer: a new employee may already have extensive management experience.
Where does this approach come from?
We use Typical age category as a descriptive profile field, rather than the name of a single scientific scale with a fixed scoring model. Its background comes from several research traditions.
Elder describes life course theory as addressing development throughout life and changing historical contexts. This provides background for distinguishing age from life circumstances. Elder, 1998
Dolnicar, Grün and Leisch distinguish variables used to form segments from additional characteristics used to describe them. Age can therefore be a selection criterion or a subsequent description. Dolnicar et al., 2018
The completion guidelines and examples below are Neurofactor's practical application. These publications do not establish a particular age distribution for your target group.
Is age a criterion or a description?
Before analysing the data, specify the purpose age will serve.
- Selection criterion: the study deliberately includes only people aged 25–54, for example. Its findings then say nothing about people outside that selection.
- Descriptive characteristic: the target group is defined using other characteristics, after which you describe participants' ages.
- Comparison variable: you investigate whether measured choices or associations differ between age categories.
Age spread is not a segment definition
A broad age range is not a reason to automatically split an associative target group. Conversely, equal age does not demonstrate equal association patterns. The segment definition determines membership; the age distribution then describes one aspect of the group.
How should you document an age distribution?
- 1Name the reference group. Are these interviewed users, current customers or people who approve purchases?
- 2Record the measurement date. Age changes. Write, for example, ‘age at participation, September 2026’.
- 3Choose an appropriate classification. Use clear, non-overlapping boundaries. For completed years, 25–34 and 35–44 are adjacent intervals. Specify how other ages are handled.
- 4Report counts and denominators. Show how many people provided an age and how many records lack that information.
- 5Define ‘typical’. Identify the largest category within the chosen classification, or report several categories if one label would poorly describe the distribution.
- 6Add context separately. Measure life circumstances, experience or career transitions if you want to make claims about them.
Mean, median and classification
A mean is not an age category. A median is the midpoint of the ordered ages, not evidence that many participants are exactly that age. The largest category also depends on the chosen boundaries: a twenty-year interval is difficult to compare directly with a five-year interval. Keep the classification with the result.
Numerical example: the largest category is not a majority
Fictional example; not a Neurofactor research finding. A study includes 50 participants. Age is known for 48. In this example, reported ages range from 25 through 64.
| Age at participation | Count | Share of all 50 participants |
|---|---|---|
| 25–34 years | 12 | 24% |
| 35–44 years | 18 | 36% |
| 45–54 years | 12 | 24% |
| 55–64 years | 6 | 12% |
| Age unknown | 2 | 4% |
| Total | 50 | 100% |
Ages 35–44 form the largest individual category in this classification. However, that category contains only 36% of all participants. Among participants with a known age, its share is 18 out of 48, or 37.5%. Avoid saying ‘most participants are 35–44’ if that suggests a majority.
An accurate profile entry is: ‘Largest category: ages 35–44, 18 of 50 participants (36%); age unknown for 2 participants; measured at participation.’
This table does not allow you to calculate an exact mean age or establish career stage. Ages within the intervals are unavailable, and experience was not measured. The distribution describes these participants, not automatically the entire market.
Why is a generation label not a behavioural explanation?
Rudolph and colleagues question generational explanations in organisational practice and advocate greater attention to development across the lifespan. Rudolph et al., 2021
Our application to this profile is straightforward: do not attribute a purchase motive to someone because they fall under ‘Gen Z’, ‘millennial’ or another label. Measure the motive itself. When using a cohort, document the birth years and the source of the classification.
Distinguish age, birth cohort and measurement period in your research reasoning. A difference between younger and older participants at one point in time does not, by itself, establish its cause. Experiences, roles and circumstances may also differ. The profile should preserve that uncertainty instead of presenting an observation as a cause.
How does this apply to an associative target group?
Neurofactor uses associative target group as a working definition for a bounded group of people whose similar associations guide decisions within a specific choice context.
Age can help describe such a group. To investigate whether it also helps define its boundaries, compare measured association patterns and their relationship with choices. Consider counts and variation within age categories. Simply counting how many participants with a particular association belong to the largest age group is insufficient.
A 29-year-old and a 52-year-old might both be managing a team for the first time. Whether they share associations when choosing training is a research question. Their ages do not answer it. This is an illustration, not an observed finding.
How do you complete the field?
With data: ‘Largest age category within [classification]: [interval], [count] of [total] ([percentage]); [count] unknown; [reference group, source and measurement date].’
With separate contextual data: ‘Age: [supported distribution]. Career stage: [separately measured situation and data basis].’ When combining characteristics, check whether both were established for the same people. Two separate group percentages do not establish that the same people have both characteristics.
Without data: ‘Age distribution unknown; not yet collected.’ If an estimate is needed for study planning, label it as a hypothesis with a question to investigate.
With a broad or mixed distribution: ‘No clear dominant category; see distribution.’ Describe several relevant groups if one compact age band conceals important differences. Do not force a number into a field simply to fill it.
Which mistakes should you avoid?
- Entering an age band because it seems to match a job title.
- Treating ‘mean age 40’ and ‘usually aged 35–44’ as equivalent statements.
- Calling the largest category a majority when its share is below 50%.
- Presenting a selected sample as the distribution of all potential customers.
- Inferring purchasing power, digital skills, decision authority or BIS/BAS scores from age.
- Reusing an old profile entry without checking its source, measurement date and target group boundaries.
Updating for a new study wave
For a new study wave, update the distribution using the new data. Do not simply shift the old age band by the number of years elapsed: the group's composition may also change.
Age describes; context needs its own evidence
A useful age category has clear boundaries, a named reference group and a traceable data basis. Explain what ‘typical’ means and make missing data visible. Support life stage, career stage and decision motives separately. This helps age contribute to understanding the target group without explaining more than the study shows.
About the sources
For Elder and Rudolph et al., the abstracts and publisher metadata were consulted; for Dolnicar et al., the relevant methods chapter. The references support the stated theoretical and methodological points. The specific profile definition, age boundaries, measurement guidance and examples are our applications, not a scientifically validated standard for this profile.
Sources checked on 22 September 2026. Examples and percentages are fictional. Completion guidelines are Neurofactor practical applications.
Discuss your target group question
Would you like to understand which characteristics and associations help define your target group? Discuss with Neurofactor how to develop that research question.
Key terms
- Typical age category
- An explicitly bounded age group described as characteristic of the researched target group using a named data source, with the meaning of typical specified.
- Life stage
- A life situation or transition relevant to the choice context, established separately from chronological age.
- Career stage
- A position or transition in working life relevant to the choice context, established separately from age and role tenure.
Frequently asked questions
What does typical age category mean in a target group profile?
It is an explicitly bounded age group used to describe the researched target group. State what typical means, such as the largest category within a named classification, and provide the data basis.
Are age and life stage the same?
No. Age counts elapsed years; life stage describes a relevant situation or transition. Claims about independent living, parenthood or other life circumstances require separate data.
Which age boundaries should I use?
Choose boundaries that fit the research question and document them before interpreting results. Use non-overlapping intervals and retain the same classification when comparing comparable measurements.
Is the largest age category always a majority?
No. In the fictional example, ages 35–44 form the largest category at 36% of participants. An absolute majority requires more than 50% of the stated reference group.
Can different ages belong to the same associative target group?
Yes. Under Neurofactor's working definition, they can if similar associations guide decisions in the same choice context. Age alone neither confirms nor disproves that similarity.
What should I enter when age data are missing?
Write ‘unknown’ or ‘not collected’. Keep any estimate separately as a hypothesis. Do not estimate a precise age distribution from job titles, photographs or broad generational images.
Sources
- 1.Elder, G. H., Jr. (1998). The Life Course as Developmental Theory. Child Development, 69(1), 1–12. DOI: 10.1111/j.1467-8624.1998.tb06128.x. - Child Development (1998)
- 2.Dolnicar, S., Grün, B. & Leisch, F. (2018). Step 7: Describing Segments. In Market Segmentation Analysis, 199–236. Springer. DOI: 10.1007/978-981-10-8818-6_9. - Springer Singapore (2018)
- 3.Rudolph, C. W., Rauvola, R. S., Costanza, D. P. & Zacher, H. (2021; online 2020). Generations and Generational Differences. Journal of Business and Psychology, 36, 945–967. DOI: 10.1007/s10869-020-09715-2. - Journal of Business and Psychology (2021)
Related topics
Reviewed by: Martijn den Otter · Last reviewed: 9/22/2026
Martijn den Otter
Oprichter van Neurofactor. Expert in neuromarketing en consumentenpsychologie.
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