Segmentation is the practice of dividing your audience into groups that share common characteristics, so you can understand how attitudes, behaviours, or needs differ across your market. Rather than relying only on overall averages, segmentation helps reveal patterns that are often hidden in top-line results.
When used well, segmentation can inform product decisions, messaging, positioning, and prioritisation across teams.
What is segmentation, in practice?
A segment is a group of respondents who share one or more defining characteristics. These characteristics can be based on:
Demographics such as age, gender, income, or household type
Geography such as country, region, or market
Behaviour such as usage, purchase, or engagement
Attitudes or needs such as preferences, motivations, or perceptions
Segmentation is most useful when segments are:
clearly defined
large enough to analyse meaningfully
relevant to a real business question
Why segmentation matters in analysis
Analysing results by segment allows you to:
identify differences between groups that are masked by averages
understand who is driving certain results
test assumptions and hypotheses about your audience
tailor decisions to specific customer groups rather than the “average” respondent
For example, an overall score might look stable over time, while a key segment is improving or declining significantly underneath.
Choosing which segments to analyse
Not every possible segment is worth analysing. The most useful segments are driven by intent, not convenience.
Some common starting points include:
Core demographic segments
Age, gender, income, or household type are often useful as a baseline to understand broad differences, especially early in analysis.
Funnel or outcome-based segments
For brand or campaign research, segments based on awareness, consideration, usage, or purchase can be particularly insightful, for example:
aware vs not aware
buyers vs non-buyers
considerers vs rejecters
These segments help explain why metrics move and which groups are contributing to change.
Hypothesis-led segments
Often the strongest segments come from a clear hypothesis, such as:
younger audiences prefer a specific channel
certain behaviours correlate with willingness to pay
non-customers hold different perceptions from customers
Starting with a hypothesis helps avoid over-segmentation and keeps analysis focused.
Designing surveys with segmentation in mind
Effective segmentation often starts before analysis.
When designing a survey, it’s worth considering:
which groups you expect to compare
which behaviours or attitudes define those groups
whether you have the right questions to support those comparisons
For example, if you want to analyse differences between online and in-store shoppers, you’ll need to include a question that captures shopping behaviour clearly.
Using behaviour and attitude-based segments
Segments based only on demographics can be limiting.
Combining demographics with behavioural or attitudinal data often leads to more actionable insights, such as:
light vs heavy users
value-driven vs convenience-driven customers
satisfied vs dissatisfied users
These segments tend to map more directly to decisions around product, pricing, or messaging.
Things to watch out for
Small segments can be noisy and misleading. Always sense-check sample sizes.
Over-segmentation can create complexity without insight. Fewer, well-chosen segments are usually more effective.
Correlation is not causation. Differences between segments don’t always explain why something is happening.
Segment insights should be interpreted alongside context, sample size, and the broader story in the data.
Working with segments in Attest
In Attest, segments can be created using demographics, answers to questions, or combinations of both, and analysed using crosstabs, charts, and boards.
For practical guidance, see: