Common demographic data include age, gender, race, religion, income, education, and employment and marital status. However, as stated earlier, we should be cautious about using demographic segmentation to avoid being left with large non-homogenous populations and because segmenting populations on demographic variables can lead to stereotyping.

Previously, we considered a population segmented by gender. What if we changed our segmentation variable to income level. Would you be confident saying that individuals with incomes of $100,000 annually were largely homogenous? That their attitudes, beliefs, needs, and wants are all relatively the same?

How would you reconcile for life stage, comparing earners who may not yet have families with those that do? What about earners who have a working spouse vs. those with non-working spouses? How would you account for regional differences and the associated costs of living? Is the buying power of $100,000 the same in San Francisco and Tulsa? Very quickly we begin to see how other factors make segmentation on a demographic variable incomplete.

Similarly, when a population is segmented demographically, stereotypes can be reinforced. Consider again the population segmented by marital status. Are you willing to project attitudes, beliefs, needs, and wants on married couples, assuming they are all the same? What about singles? How would you account for people who are divorced? What about people in long-term committed relationships but are unmarried? You see, we cannot say, “All married people think…” or “All single people believe…” or “All married couples want…” because this obviously is not and cannot be true. People of the same age, gender, race, or income, do not all think, believe, and act the same way. They are different people influenced by all of their experiences with their own unique perspectives.