Data analytics helps you to delve deeper than the surface level/superficial information to uncover the valuable insights within your data. It tells you what will work and not work.

Factor Analysis

Factor Analysis is a method of data reduction. Multiple variables having common attributes are grouped under a factor and separated from other variables with low or no correlation. It simplifies diverse relationships that exist among a set of observed variables by identifying common attributes that link together and provides insight into the significance of the underlying structure of the data.

There are two types of factor Analysis

Factor analysis can be used in the various marketing research domain:

Conjoint Analysis

Factor Analysis is a method of data reduction. Multiple variables having common attributes are grouped under a factor and separated from other variables with low or no correlation. It simplifies diverse relationships that exist among a set of observed variables by identifying common attributes that link together and provides insight into the significance of the underlying structure of the data.

Conjoint analysis can be used in:

Cluster Analysis

Factor Analysis is a method of data reduction. Multiple variables having common attributes are grouped under a factor and separated from other variables with low or no correlation. It simplifies diverse relationships that exist among a set of observed variables by identifying common attributes that link together and provides insight into the significance of the underlying structure of the data.

Text Analysis

Text analytics simply means to interpret the meaning out of the given text. The main concept is to analyse customer feedback for any company, brand, product or service through survey responses, online reviews and comments, social media discussions, emails, call centre notes. It is of absolute necessary to mould your product and services as per customer’s desires and text analytics enables you to achieve that giving a clear picture about the customers thoughts, opinions, problems areas, wants and preferences. We help you gather valuable qualitative data which when received in unstructured form is converted to structured information that can be applied to your business’s strategic action plan.

Correspondence Analysis

Correspondence analysis also referred as brand mapping is a statistical tool that provides a graphical representation of cross-tabulations and is used for summarizing tables.

The respondents are asked to check the association between the two sets of variables presented:

The number of “yes” received for each benefit on each brand is the input data. Brands and benefits are marked in the multidimensional space showing a link among them. It leads to a depiction of the data table in a map, in which distances and relative positions of points have a specific meaning. It can be used in a variety of exploratory research like brand positioning, brand association, new product development, and selection of the brand name.

Max Differential Analysis

Max Differential analysis is used when you want to determine a preference. For Example, a real estate developer wants to determine what resort features would be preferred by the customers. When respondents answer this question, features are compared, and a preference can be uncovered. A Max Differential survey question asks respondents to select what is most and least important from a list of items. When the results are received, each feature has points providing you actionable data.

Regression Analysis

Regression is a useful tool in making predictive or cause-effect relation analysis. It estimates the relationship between two or more variables. We can observe the benefits of using regression analysis while taking the example of advertising expenditure and sales of a product.

We help you to evaluate and determine the best set of variables for developing predictive models. If you are looking for a top data analytics company in India, reach us at info@growthlyne.com