Quasi-Experimental Study: What It Is and What It Brings to Market Research

The quasi-experimental study is a widely used research methodology in social sciences, marketing, and business when conducting a fully controlled experiment is not possible. This approach allows for the analysis of cause-and-effect relationships without requiring strict random assignment, making it ideal for real-world scenarios where full control is unfeasible.

At Netquest, we apply this type of study to extract insights based on real data, helping companies better understand consumer behavior and improve their market strategies.

What Is a Quasi-Experimental Study?

A quasi-experimental study is a research method that aims to evaluate the impact of an independent variable on a dependent variable, but without randomly assigning participants to study and control groups.

Unlike traditional experiments, where subjects are randomly assigned to eliminate bias, in a quasi-experimental study the groups are either pre-existing or formed naturally.

This type of study is useful when:

• Random assignment is not feasible due to ethical or logistical constraints.

• Phenomena are studied in natural environments where full control isn't viable.

• There is a need to evaluate the impact of an intervention or policy in a real-world context.

Example: If a company launches a new pricing strategy in a specific region and wants to evaluate its impact on sales, it can compare the results with a similar region where the strategy was not implemented—conducting a quasi-experimental study.

Differences Between Experimental and Quasi-Experimental Studies

Although both methods aim to identify cause-and-effect relationships, there are key differences:

Quasi-experimental studies are used when a strict experimental design is not possible but there is a need to understand the effects of a variable in a practical context.

Types of Quasi-Experimental Studies

There are several designs within quasi-experimental studies, depending on the level of control and structure of the analysis:

1. Interrupted Time Series Design

This method analyzes changes in a variable before and after an intervention. It is used when historical data is available and the impact of a specific action is to be evaluated.
📌 Example: A supermarket introduces a new pricing strategy in June and analyzes sales six months before and after the change to assess significant shifts.

2. Non-Equivalent Control Group Design

Two similar groups are compared, one of which receives the intervention and the other does not. Although there is no random assignment, researchers attempt to ensure the groups are as comparable as possible.
📌 Example: A cosmetics brand launches a new ad campaign in one city and compares it to a similar city where the campaign was not run, to measure its impact.

3. Cohort Designs

Groups of people with similar characteristics are studied over time to evaluate the effect of a variable. This method is common in health studies and consumer behavior analysis.
📌 Example: An analysis of the impact of a loyalty program on returning customers versus new customers not enrolled in the program.

How We Apply Quasi-Experimental Studies at Netquest

At Netquest, we use quasi-experimental methodologies to help businesses analyze changes in consumer behavior and assess the impact of campaigns or business strategies.

Through our consumer panels, we can:

• Measure the effect of new pricing strategies across different customer segments.

• Evaluate the impact of advertising campaigns by comparing exposed vs. non-exposed audiences.

• Analyze how changes in customer experience affect retention and loyalty.

• Apply cohort analysis to study the behavior of recurring users.

Thanks to our high-quality data collection, we deliver accurate insights to improve strategic decision-making.