Choose a segmentation basis for a stated decision
Demographic, behavioral, attitudinal, needs-based, and Jobs-to-Be-Done approaches describe different kinds of similarity. Compare them on observability, stability, mechanism, privacy, assignment ambiguity, maintenance, and the content or offer decision at stake. No basis is universally superior. A variable useful for media reach can be irrelevant to article structure, while a situational job useful for content may be hard to estimate at population scale. Write the intended use and prohibited use before choosing.
Evidence: IT University of Copenhagen Research Portal; Government Digital Service
Demographic segments are accessible but easily stereotyped
Age, location, household, occupation, or organization size can be available in public or first-party data and may matter for eligibility, language, regulation, access, or distribution. They often fail to explain why two similar people choose differently. Use demographics when the attribute directly changes the service condition or evidence needed, and review fairness and privacy. Avoid assuming preferences, skill, income, or motivation from membership. A demographic description without a decision mechanism becomes a convenient persona costume.
Evidence: Government Digital Service; IT University of Copenhagen Research Portal
Behavioral segments describe action inside observed systems
Recent pages, purchases, feature use, support contact, or renewal can create operational groups tied to actual events. These segments are useful for maintenance and next-step support, but the system observes only its users and its own event definitions. Behavior may be an effect of current design rather than an enduring need. Use it when timing and action matter. Avoid inferring sensitive intent, treating non-observation as absence, or assuming a past action will persist after circumstances change.
Evidence: Government Digital Service
Attitudinal and stated-needs segments reveal meaning with response limits
Interviews and surveys can distinguish risk tolerance, desired assurance, perceived barriers, or decision criteria. Wording, recruitment, memory, and social desirability affect what is reported, and expressed preference may not predict action. Use these segments to tailor explanation and test hypotheses. Avoid creating broad psychographic characters from a few vivid quotes. Tie every label to evidence, context, and a choice the person recently made or attempted.
Jobs-to-Be-Done segments prioritize progress and switching context
JTBD groups episodes by the progress sought in circumstances, including triggers, alternatives, anxieties, and success. It can reveal that the true competitor is a workaround or inaction rather than another product. The Christensen Institute and HBR provide primary explanations of the approach. Use it for content, innovation, and decision pathways. Avoid treating a job as a permanent identity, estimating its size without separate methods, or writing a poetic job statement that changes no operational decision.
Evidence: Clayton Christensen Institute; Harvard Business Review
Score approaches and allow a layered answer
Build rows for decision relevance, evidence source, population coverage, assignment rule, expected error, change frequency, privacy, fairness, implementation cost, and review trigger. A layer can combine geography for eligibility, behavior for timing, and situation for content—provided the team documents how intersections are handled. Apply vetoes for unlawful or unfair use, unavailable evidence, and attributes with no causal or operational connection. More layers can increase precision on paper while making assignment and maintenance unusable.
Evidence: Government Digital Service; IT University of Copenhagen Research Portal
Select the simplest basis that changes the experience responsibly
Choose demographic grouping for real access conditions, behavior for observed lifecycle support, attitudes for explanation hypotheses, and jobs for situation-based progress. Preserve the rejected approach's strongest advantage and a trigger to reconsider. Segments are models with error, not facts about individuals. Let readers self-select when possible and provide an inclusive default. The matrix cannot guarantee effectiveness or fairness; it makes the rationale, evidence, and cost of grouping visible for review.
Evidence: Clayton Christensen Institute; IT University of Copenhagen Research Portal
Combination creates interaction errors as well as precision
A geographic eligibility layer, a behavioral timing layer, and a job-based content layer may each be defensible, yet their intersections can produce tiny groups, contradictory rules, and opaque assignments. Test common and edge combinations, document precedence, and provide a safe default. If the team cannot explain why two similar readers receive different content, simplify. Layering should reduce a known decision error, not recreate every available data field in a targeting engine.
Evidence: Government Digital Service; IT University of Copenhagen Research Portal
Sources and further reading
These references informed this article. A source supports a claim; it does not imply endorsement of TenMultigure or any future product reference.
- Our Theories: Jobs To Be DoneClayton Christensen Institute · Accessed August 10, 2026
Provides the circumstances-and-progress logic evaluated in the Jobs-to-Be-Done option and its switching-context strengths.
- Learning about users and their needsGovernment Digital Service · Accessed August 10, 2026
Grounds evidence-based user needs and cautions against assigning individual requirements from unsupported group assumptions.
- Know Your Customers' Jobs to Be DoneHarvard Business Review · Accessed August 10, 2026
Supplies the primary managerial account of job-based competition across product categories and current alternatives.
- Is segmentation a theory? Improving the theoretical basis of a foundational concept in business-to-business marketingIT University of Copenhagen Research Portal · Accessed August 10, 2026
Provides independent theoretical scrutiny used to compare mechanisms, assignment error, stability, and layered complexity.
Reviewed by TenMultigure Editorial Review. See an error or a source that has changed? Tell the editorial team.
Review method: AI-assisted desk research with editorial checks. Reviewed ; next scheduled review . Compared four segmentation bases by evidence and operational use, added a layered matrix with privacy and fairness vetoes, and preserved inclusive defaults and self-selection.