One of the most crucial challenges for any type of business is to collect an in-depth understanding of the market. This is where the different types of marketing analysis methods, techniques, and tools come to help.
In today data-driven world and crazy competition business environment, you need constantly searching and updating your market knowledge. That is why the basic market research analysis methods are a valuable weapon for any company.
They are a great basis for forecasting sales, data and marketing intelligence, decision making, creating strategies and etc.
We already explained what is market analysis and what are its main characteristics. Here is a list of the key techniques and methods of market data analysis.
Factor analysis is a well-known statistical technique used to decline a large number of variables (which strongly correlate with each other) into a common group called a factor. The factor analysis is an exploratory analysis. It groups similar variables into dimensions.
Factor analysis has a very important role in marketing strategies and market research. Marketing factor analysis is changing one marketing variable/factor to see what will be the change to the other variable – the outcome.
Factor analysis requires when a company changes one marketing point, such as the price of a product, to see the changes of the sales of that product.
A crucial point here is to change only one variable at a time to be able to measure the relationship between the variables and the outcome.
Typically, companies test marketing variables with factor analysis using tools such as focus groups, surveys or other quantitative and qualitative research methods.
Factor analysis in marketing area is one of the key marketing analysis methods because it reflects the perception of the buyer of the product. You are able to find out what is important to your customers of the product.
Cluster analysis is a powerful statistical tool used to classify different types of objects into groups (clusters). Businesses use this type of market analysis method to analyze data that has been categorized on similarities and differences.
As you might guess, cluster analysis is widely used for market segmentation.
It can be given a wide range of market segmentation examples made with the help of cluster analysis.
Used to quantify and characterize customer segments, cluster analysis enables you to target your customers according to their needs, beliefs, geographical location, behavioral and etc.
Because once a company finds out which type of consumer fits into a group, it can create successful marketing strategies related to the needs of its target segments.
This definitely helps you to discover new market opportunities.
Logistic Regression is an other statistical classification method widely used in market research. This is one of the popular marketing analysis methods also known as logit regression or logit modeling.
Logistic regression is very similar to the linear regression models, as it is used to get an understanding of the relationship between a dependent variable and one or multiple independent variables.
Good examples of logistic regression application in marketing could be to predict if it has a probability for a consumer to buy a product, given that their age is known.
Marketers use widely logistic analysis to assess the scope of customer acceptance and customers’ purchase intentions of a new product. This way, marketers can predict potential sales of that product.
Discriminant analysis as the cluster analysis is widely used for marketing segmentation. Discriminant analysis classification is based on the measured values for a group of characteristics for each unit separately.
It means that each unit has characteristics than could be measured and those values vary from one observational unit to the other. In comparison with other marketing analysis methods, DA is much easier, especially if there are more than two groups.
Discriminant analysis is often used for creating Perceptual Mapping by marketers.
Distinguish between heavy, medium, and light users; defining how market segments differ in media assimilation; defining the traits of consumers who will respond to direct marketing campaigns and etc.
Regression is a famous prediction technique that quantifying the relationship between dependent variable to one or more independent variables.
In marketing field, the regression analysis is widely used to predict how the relationship between two variables, (for example between advertising and sales), can develop in the time.
The main goal of regression analysis is to predict and control the relationship between at least two variables. This type of market analysis method is used for variations in market sales, share, and brand loyalty.
Business or marketing intelligence specialists can draw the regression line with data extracted from sales in the past periods.
Regression analysis can also be used in customer satisfaction research to answer questions such as: “Which product characteristics contribute most to someone’s overall satisfaction?”
To put it in a simple way, correlation analysis is a technique used to identify how closely related two variables are to each other. It studies the degree of a relationship between two, numerically measured variables.
This type of analysis method is useful when you want to know if there are possible connections between variables.
Correlation analysis is used for answering questions such as: “Do longer blog posts get more shares on Facebook?”
According to B2B International, correlation analysis helps you to “find out what satisfies your customers and employees – and what keeps them loyal.”
What is the difference with regression analysis? Regression analysis emphasis on the prediction while correlation analysis focuses on the strength/the degree of a relationship between two variables.
There is a wide range of statistical analysis techniques and tools but only a few of them can be used for market analysis. The reason is mainly that of the specificity of marketing information and area.
Nevertheless, marketing analysis methods have a crucial role in understanding different customer needs, the relationship between important market variables (such as price and sales), predicting and forecasting the outcomes of different campaigns, and etc.
In addition, they are a great basis for performing and developing business and marketing intelligence and for taking the right business decisions at the right time.
Silvia Vylcheva has more than 10 years of experience in the digital marketing world – which gave her a wide business acumen and the ability to identify and understand different customer needs.
Silvia has a passion and knowledge in different business and marketing areas such as inbound methodology, data intelligence, competition research and more.