Regression analysis: Precise Forecasts and Predictions
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Regression analysis plays a vital role in contemporary market research, offering a powerful tool for making accurate forecasts and addressing intricate interdependencies within challenges and decisions. It enables us to predict user behavior and gain valuable insights for optimising business strategies. This article aims to elucidate the concept of regression analysis, delve into its working principles, and explore its applications in the field of market research.
What is regression analysis?
Regression analysis serves as a statistical method and acts as a translator within the realm of market research, enabling the conversion of ambiguous or complex data into concise and understandable information.
By investigating the relationship between two or more variables, regression analysis sheds light on crucial interactions, such as the correlation between user behavior and screen time in smartphone applications.
What does regression analysis do?
Regression analysis serves multiple purposes.
- It identifies correlations between two or more variables, allowing us to understand and visualize their interrelationship.
- It has the capability to forecast potential changes when variables are altered.
- It can capture values at specific time points, enabling us to examine the impact of fluctuating parameters on the overall outcomes.
Origins of Regression Analysis
Regression analysis traces its roots back to the late 19th century when it was pioneered by the renowned British statistician, Sir Francis Galton. Galton explored variables within human genetics and introduced the concept of regression.
By examining the relationship between parental height and the height of their offspring, Galton laid the foundation for linear regression analysis. Since then, this methodology has found extensive applications not only in market research but also in diverse fields such as psychology, sociology, medicine, and economics.
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What types of regression analysis are there?
Regression analysis encompasses various regression models, each serving specific purposes depending on the research objectives and data availability.
Employing a combination of these techniques allows for in-depth insights into complex phenomena. Here are the key regression models:
Simple linear regression
The classic model examines the relationship between a dependent variable and a single independent variable, revealing their association. For instance, it can explore how daily coffee consumption (independent variable) impacts daily energy levels (dependent variable).
Multiple linear regression
Expanding upon simple linear regression, this model incorporates multiple independent variables, such as price, advertising, competition, or sales figures. In the context of energy levels, variables like sleep duration and exercise can be added alongside coffee consumption.
When the relationship between variables deviates from a straight line, non-linear regression comes into play. This is particularly useful for phenomena like exponential growth in app downloads or user numbers, where traditional linear models may not be suitable.
For complex correlations or patterns characterised by ups and downs, quadratic regression is utilised.
It fits data that follows non-linear trends, such as seasonal sales fluctuations. For instance, it can help determine market saturation points, where growth typically plateaus after an initial rapid expansion.
Hierarchical regression allows the researcher to control the order of variables in a model, enabling the assessment of each independent variable's contribution to predicting the dependent variable.
For example, in demographic-based analyses, variables like age, gender or education levels may be weighted differently.
Multinomial logistic regression
This model examines the probabilities of outcomes with more than two variables, making it valuable for complex questions.
For instance, a music app may predict users' favourite genres based on their previous preferences, listening habits, and other factors like age, gender, or listening time, enabling personalised recommendations.
Multivariate regression analysis
When multiple dependent variables and their interactions with independent variables need to be explored, multivariate regression analysis is employed.
For instance, in the context of fitness data, it can assess how factors such as diet, sleep, or exercise intensity influence variables like weight and health status.
Binary logistic regression
This model comes into play when a variable has only two possible answers, such as yes or no. Binary logistic regression can be utilised to predict whether a specific product will be purchased by a target group. Factors like age, income, or gender can further segment the buyer groups.
How is regression analysis used in market research?
The versatility of regression analysis is reflected in its diverse applications within the field of market research. Here are selected examples of how regression analysis is utilised:
- Predicting market trends
Regression analysis enables the exploration of future market trends.
For instance, a real estate company can forecast future home prices by considering factors such as property location, size, and age of the property. Similarly, a food company may employ regression analysis to identify the ice cream flavour with the highest sales potential.
- Customer satisfaction
Companies can employ regression analysis to investigate the factors influencing customer satisfaction. By conducting customer surveys and analysing the data through regression analysis, a customer service company can identify the aspects of their service that have the greatest impact on customer satisfaction.
- Usage behavior
Regression analysis provides insights into the factors influencing the usage of smartphone apps. It allows for differentiation based on variables such as age, gender, or education level, shedding light on the drivers of app usage.
- Advertising impact
Regression analysis measures the effectiveness of advertising campaigns. By analysing advertising expenditure in relation to product sales, it enables the classification of advertising effectiveness and informs decision-making regarding advertising strategies.
- Measuring market maturity
Regression analysis helps evaluate the reception of a product or service among the target audience. It identifies positive and negative evaluations, as well as determining which features should be emphasised. Through regression analyses, insights can be gained into products and services even before their market launch.
How does a simple linear regression analysis work?
Suppose a company aims to determine the relationship between advertising spending and product sales, requiring a simple linear regression analysis. Here are five possible steps to conduct this analysis:
- Data collection
To commence the analysis, data on advertising spending and product sales needs to be collected.
- Chart generation
The data is plotted on a scatter plot where one axis represents advertising spending and the other represents product sales.
- Determine the regression line
A straight line is drawn to intersect as many data points as possible. This regression line illustrates the average relationship between the two variables.
- Predicting developments
The regression line serves as the foundation for making future predictions. By manipulating one variable, you can examine its influence on the other variable.
- Interpretation of the results
Valuable insights can be derived from the results. For instance, the analysis may reveal that an additional £10,000 in advertising spending could lead to an average increase in sales of 500 units.
Regression analysis: All-rounder in market research
Regression analysis stands as a powerful and versatile tool in the realm of market research. It offers a range of regression models, varying in complexity depending on the research question or objective at hand. Whether investigating the relationship between advertising spend and sales, analysing usage behavior, or identifying market trends, regression analysis provides data-driven insights that empower informed and sound decision-making.
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