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In market research, explanatory variables refer to the characteristics that influence consumer behavior. Response variables, on the other hand, are the outcomes of interest that are measured in response to the changes in the explanatory variables.
In experimental research, a variable is a factor that can change and can be changed. These factors can be altered and controlled for an experiment to measure the effect of one variable on the other.
The experiment includes different types of variables. It aims to determine the causal relationships between two or more variables. Among many variables, two of which we will discuss are Explicative Variables and Response Variables.
An explanation variable is a factor that you can manipulate in an experiment to determine the change caused by the response variable. It is often referred to as an Independent Variable.
In market research, these variables can include demographic, socioeconomic, geographic, and psychographic factors. For example;
1. Demographic: Age, gender, education level, occupation, marital status, etc.
2. Socioeconomic: Social class, household size, household income, employment status, etc.
3. Geographic: Location, urban/rural classification, etc.
4. Psychographic: Personality traits, values, lifestyle, interests, attitudes, etc.
This type of variable plays a key role in market research to help you identify patterns and relationships between consumer characteristics and their resulting behavior. By understanding the causal relationship between these variables you can tailor the marketing strategies to target specific customer segments.
Two simple ways to identify explanatory variables include:
1. Surveys – Leverage online survey tools to gather data directly from customers through structured surveys across multiple accessible channels.
2. Interviews – Utilize phone surveys or mobile-offline survey tools to engage consumers in person and gather insights into consumer attitudes and motivations to identify relevant explanatory variables.
Response variable is the result of the experiment where the explanatory variable is manipulated. It is a factor whose variation is explained by the other factors. Response Variable is often referred to as the Dependent Variable.
In market research, this variable represents the key metric businesses seek to understand about their consumers and influence through their marketing initiatives. Examples of response variables in terms of market research include;
1. Purchase behavior: The frequency, volume, and types of products or services purchased by consumers.
2. Customer satisfaction: The level of satisfaction or dissatisfaction experienced by customers with a product, service, or brand.
3. Brand loyalty: Repeat purchase patterns and likelihood to recommend brands/products.
This type of variable provides you with a measurable indicator of consumer behavior and attitude. It helps gauge the impact of your marketing initiatives, allowing for a timely adjustment to those strategies.
Three ways you can identify response variables are:
1. Surveys – Gather direct feedback from consumers to identify customer satisfaction, brand loyalty, and purchase behavior.
2. Observation – Observe customer behavior in natural or simulated environments.
3. Experimentation – Conduct controlled experiments by manipulating variables and measuring the impact on response variables.
Here are some reasons why understanding these variables is crucial in market research:
The best way to identify the two variables separately and understand the difference is to remember that You change the value of explanatory variables to observe the impact it has and how it influence the response variable.
The explanatory variable explains the variation caused by the response variable. There is a cause-and-effect relationship between the two variables. The number of variables in each type may be more than one, depending on the research question.
For Example,
You want to find out if alcohol decreases the ability to drive safely. The alcohol a participant consumes determines its effect on their driving performance. In the experiment, the amount of alcohol consumed gives an explanation for the driving skill.
Therefore, in the experiment,
Identifying and understanding the causal relationship between explanatory and response variables enables you to interpret market research outcomes and make insightful and informed decisions. Explanatory variables influence or cause changes in response variables. However, a causal relationship requires careful analysis and consideration.
Statistical data analysis methods such as correlation and regression analysis can help explore, identify, and validate any causal relationship between variables in market research.
Let’s look at some examples of explanatory and response variables in market research.
You can identify distinct marge segments with unique preferences and needs by understanding the relationship between explanatory variables like demographic or geographic factors and response variables like purchase behavior and brand loyalty.
This enables you to customize market strategies to the specific preferences of each segment, leading to more targeted customer retention and acquisition.
The insight into the causal relationship between consumer preferences and purchase behavior can inform product development efforts. By identifying features that are valued by target consumers, you can design products that align with customer needs and preferences.
Exploring the causal relationship between explanatory variables and response variables within your target market can help you tailor marketing communications that resonate with the target audience.
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When you have paired data, you may use Scatterplot to demonstrate the causal relationship between the Explanatory and Response Variables.
A paired data implies that you have one variable for each type. This means that the outcome of every response variable for each participant is linked with every explanatory variable.
In such a case, in a scatterplot, the explanatory variable is plotted along the X-axis, which is horizontal, and the response variable is plotted along the Y-axis, which is vertical in a Cartesian coordinate system.
Let’s say you want to observe if there is any causal relationship between the number of hours spent studying and the performance on the test. You experimented with 100 students in a school.
You can demonstrate the result in a scatter plot by plotting the hours spent studying on the X-axis and the test score on the Y-axis. Each data point in the scatterplot is the paired data of each student.
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In conclusion, understanding the relationship between explanatory and response variables is essential to conducting meaningful market research and making insightful business decisions. An understanding of the causal relationship between the two variables enables you to leverage insights into various business functions and make decisions that meet the target market’s needs.
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