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Freedom251 › Blog › audience segmentation

How to Build Audience Segments Without Relying on Demographic Assumptions

By Freedom251 Team · · 5 min read

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Build audience segments around evidence of what people are trying to do, the situations they face, and the outcomes they want—not around assumptions that everyone sharing an age, gender, or income label has the same motivation. Start with the decision the segment should improve, investigate real needs and behavior, define membership in observable terms, then validate and revise the grouping.

Start with the decision, not the audience spreadsheet

Before grouping anyone, specify what the organization will do differently: change a content pathway, product feature, message, service, or channel. If two proposed groups would receive the same help and experience, the distinction may not be useful. Segmentation is meant to guide a decision, not merely produce a detailed description of the audience.

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Salesforce recommends setting a goal for each segmentation project and comparing findings with the wider audience. Its guide suggests checking whether the project is relevant, timely, feasible, and measurable: Salesforce’s market segmentation guide.

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How do you figure out your audience’s problems?

Write a testable hypothesis

Describe a situation and a need you expect to find before giving the group a persona or identity label. For example: “People who need to complete a task while away from their usual device may need a short, mobile-friendly explanation.” This is a hypothesis to check, not a claim that every person in a particular demographic behaves alike. Google News Initiative recommends developing an audience hypothesis and testing it through user research: Google News Initiative’s audience strategy playbook.

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Ask about recent, specific experiences

Ask people to recount what happened rather than asking them to endorse a generalized description of themselves. Useful prompts include:

  • What were you trying to do?
  • What prompted you to do it at that moment?
  • What did you try first?
  • What got in the way?
  • What would a good outcome have looked like?

Turn the pattern you hear into a job story: “When [situation], I want to [need or action], so that [outcome].” The situation and outcome matter: the same task can arise from different motivations, while different people can share a need. Google News Initiative separates underlying motivation from the task itself and uses this job-story structure. NSW Government’s OneCX Program similarly advises asking why a user arrived, what problem they are solving, and what job they need to do: NSW Government’s audience framework.

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Combine what people say with what they do

Interviews and open-ended survey responses help explain needs, barriers, and intended outcomes. Site or app analytics, content choices, customer feedback, reviews, and social interactions can help show behavior in context. Salesforce lists these as possible inputs, and Google Ad Manager documents first-party segments based on how people use a site or app and the content they see: Google Ad Manager audience-segment documentation.

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Do not treat a click, visit, or page view as proof of a lasting motive. It establishes that an action occurred in a particular context; interviews or other evidence may help explain why. Look for agreement across sources, and note when they point in different directions rather than forcing a tidy story.

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Decide which differences deserve a segment

People belong in separate segments when their situations or desired outcomes imply meaningfully different treatment. Compare candidate groups by asking whether they need different information, product support, messages, or channels. If not, keep the audience broader.

For product and content decisions, compare the outcome sought, constraints encountered, and help required. For campaign activation, also consider whether the group can be reached, whether the available signals assign people accurately, whether the channel fits, and how sensitive the data is. Weigh those practical concerns against the value of acting differently.

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Demographic and contextual information can still be relevant when it demonstrably affects access or a decision—for example, eligibility, language, geography, or a measured choice. The problem is using a demographic label as a shortcut for an untested assumption about motivation.

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Define membership so the segment can be checked

For every segment, record the observable evidence that qualifies someone, the time window, any exclusions, where the data came from, the level of confidence, and who owns the definition. Clear rules make it possible to review assignments, identify uncertainty, and revisit the group as circumstances or evidence change.

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Google Ad Manager offers one platform-specific example: “Restaurant Enthusiasts” can be defined using two page views or app activities in three days. That is an example of an operational rule in the product documentation, not a universal threshold and not proof that the people assigned share a particular motivation. The documentation’s Audience Solutions feature is limited to Ad Manager 360; its recommendation to move from third-party cookies to PPID is a platform recommendation for greater coverage and durability, not a substitute for appropriate privacy governance.

The IAB Tech Lab’s Audience Taxonomy distinguishes demographic, interest-based, and purchase-intent labels to improve clarity and comparability across providers. Version 1.1 followed a 30-day public comment period in October 2020; that version history is not evidence that one way of segmenting performs better than another: IAB Tech Lab Audience Taxonomy.

Validate the hypothesis and update it

Google News Initiative suggests that teams can begin testing assumptions with 10 to 20 interviews when they recruit relevant potential users and ask about lived experience. Treat that as an initial practical suggestion, not a statistical representativeness standard or a guarantee that interviews will reveal every meaningful pattern. The playbook’s separate 2 to 6 percent figure is a rough active-user goal in an example for a news business, conditioned on market size, marketing budget, competition, and organic growth; it is not a segmentation or conversion benchmark.

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Check whether the proposed group behaves or responds differently from a sensible baseline, look for cases that contradict the hypothesis, and revise the definition when new evidence changes the picture. Keep names focused on needs or situations rather than identities. Personas can help a team communicate, but a composite character is not a description of every member; Google notes that personas may evolve after interviews and may become less useful than jobs-to-be-done.

NSW Government’s experience illustrates why provisional models matter: its first research callout produced over 70 personas, after which the program moved toward consolidating mindsets and archetypes; it also says its audience does not fit neatly into groupings. Its new website saw more than 14 million users over the first six months of the COVID-19 pandemic—a figure tied to that specific site and period, not a general measure of segmentation success.

A practical review checklist

  • Decision: Is there a specific action this grouping will change?
  • Need: Is the shared situation, barrier, behavior, or outcome supported by evidence?
  • Difference: Would this group genuinely need a different response?
  • Rule: Can someone else apply and review the membership criteria?
  • Confidence: Are uncertainty, contradictory cases, and likely assignment errors visible?
  • Stewardship: Are data sensitivity, reachability, ownership, and review timing considered?

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