How to Segment the Market When Considering Income As a Demographic Variable
The use of income as the sole determining factor when predicting buying preferences of a market can be misleading. Instead of using income as a single indicator, consider income in combination with other demographic data. This will enable you to understand which segments of the market are likely to buy what products or services.
Statistics provide evidence of correlation and causation
Correlation and causation are important concepts in statistics. Using statistics to determine relationships between demographic variables can make it easier to understand the cause and effect of a particular event. For example, income and education are often correlated, as individuals with more education earn more money. These relationships can be explored using cross tabulation.
However, correlation does not imply causation. A correlation must be strengthened by other underlying variables and a relationship with other populations to support its validity. In addition, the two variables may have nonlinear relationships. This may be due to the fact that the data set contains distinct subgroups or a single outlier.
Correlation measures the size of the association between two variables. It does not show a direct relationship, but it indicates if a change in one variable leads to a change in another. In contrast, a causal relationship indicates a direct relationship between two events.
The difference between correlation and causation is important because we need to understand the direction of the causal relationship. For example, we need to know if the effect comes before the cause, or the other way around. It is also important to understand that cause and effect may be related to each other, but the timing of the two may not be obvious. For example, we could conclude that people with an active lifestyle are more likely to be healthier and have higher levels of cognitive function.
Correlation and causation were not clearly distinguished in the early history of statistics. This led to the development of sophisticated statistical methods that allowed us to draw quantitative conclusions based on observed data. However, despite their advances, correlation and causation are still two different concepts.
In addition to correlation, causality can be determined by the use of counterfactuals. Counterfactuals, or hypothetical scenarios, can help clarify the causal effect. For example, the top-performing employee in the office might not be related to the boss. Moreover, he may not have received a promotion for years. Or, his team could have performed poorly under a different manager.
Using the main diagonal of the data matrix, the correlations in cell A and cell D are equal to 1. This is because both variables are perfectly correlated. However, the sample sizes for the variables are different in cell A versus cell D. One reason for this is the presence of missing data. For instance, a larger sample size for the height and weight variables in cell A than for cell D is because of missing data in cell D.
Using lifestyle data to segment a market
Lifestyle segmentation involves the separation of the market into specific groups based on buying preferences or lifestyle variables. This allows companies to target specific groups with their product or service and find ways to meet their needs. This approach is also helpful in retaining customers.
Lifestyle data is a useful source for segmenting a market when considering income as a variable. Its usefulness is in marketing research because it allows companies to target a specific subset of customers. By combining this information with demographic data, marketers can create detailed customer profiles.
One example of a lifestyle-based segmentation is in the fitness industry. For example, a protein supplement provider might collect information on gym-goers and advertise their products through a subscription box for fitness enthusiasts. Similarly, a soap company might find out that certain age groups prefer body wash over bar soap. Based on this data, the company may develop a new body wash product to target younger consumers.
In the final segment, the universal statement rarely appears. This is because it tends to be the same across segments. It is therefore unlikely to explain much in a multivariate analysis. Because variables must move up and down in the survey, the highest and lowest rated variables may fall out of the equation. However, a universal statement might provide a foundation for strategy and positioning.
