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Factor analysis: Definition, sample questions Digital Customer Experience

Unveiling Patterns with Factor Analysis: Definition and Sample Questions

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Factor analysis is a statistical technique that aids in reducing a complex dataset into simpler, more manageable components, unveiling patterns and relationships within the data. By identifying underlying structures, it provides valuable insights into the variables and how they relate to one another. This blog explores the definition, types, examples, and sample questions of factor analysis to understand its applications and benefits fully.

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What is factor analysis?

Factor analysis, also known as dimension reduction, is a statistical method used to reduce a large volume of data to a smaller, more manageable dataset. It works by identifying patterns and common characteristics in the data, ultimately reducing the dimensions or variables to make it easier to understand.

Types of factor analysis:

There are two types of factor analysis 

1.  Explanatory Factor Analysis

Explanatory factor analysis is used when there is no prior understanding of the data’s structure. It aims to explore the relationships and patterns within the dataset, providing insights into how variables group together.

 2. Confirmatory Factor Analysis

Confirmatory factor analysis is employed when researchers have a clear hypothesis or theory about the data’s structure. It verifies whether the proposed structure aligns with the actual data, validating preconceived notions.

Factor analysis helps in condensing variables and uncovering clusters of responses by measuring shared variances across the variables. It creates a new scale by eliminating unique variables that do not share variances with other variables. This technique is often used by survey researchers to simplify question responses into shorter ones.

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Validity in factor analysis

works by measuring both unique and shared variances across the variables. But to be focused on one, we only consider the variables that share their variances with the other variables and not the unique variables. This is then used to identify various patterns in the variables and group them accordingly. 

Factor analysis basically creates a new scale by eliminating the unique variables or the ones that do not share their variances with some other variables. Along with its many applications, factor analysis is commonly used by survey researchers where they want to know whether they can simplify their question responses into shorter ones. 

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How does factor analysis work?

Factor analysis helps you in condensing variables and uncovering clusters of responses. Let us look at how it actually works with an example scenario:

You decide to ask some questions like the below that cover somewhat similar grounds of customer satisfaction –  

  1. How did you like our product?
  2. Will you consider referring us to your friends and family? 

As for evaluating the entire performance of the organization, it would be convenient for you to have only one variable that represents the customer experience score. It can be done in two ways:

  • Get an average of both the questions
  • Run a PCA (Primary Component Analysis) and have a factor-dependent variable. 

PCA technique is generally more effective than the average method as it calculates the weightage of the variables along with the calculations. 

There might be a case where you want to ask several questions to the customer but don’t exactly know which responses can be grouped together and which will be kept totally different. Example: purchase barriers of the target customers. The reasons for the same could be:

  1. Price is prohibitive
  2. Overall implementation costs
  3. We can’t reach a consensus in our organization
  4. Product is not consistent with our business strategy
  5. I need to develop an ROI, but cannot or have not
  6. We are locked into a contract with another product
  7. The product benefits don’t outweigh the cost
  8. We have no reason to switch
  9. Our IT department cannot support your product
  10. We do not have sufficient technical resources
  11. Your product does not have a feature we require
  12. Other (please specify)

Factor analysis helps you to group these responses like:

Factor analysis: Definition, sample questions Digital Customer Experience

We have three labels for the groups and according to them, factor analysis uses heat maps to group the factors clearly to tell us what affects the responses and what they imply.

Clustering the above information with its three components will tell us the customer trends that are high in Cost and Org but not both.

Factor analysis: Definition, sample questions Digital Customer Experience

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Sample questions for factor analysis

Effective factor analysis involves formulating well-crafted questions that can capture meaningful insights. Here are sample questions categorized based on the types of factors analyzed:

  •  Psychographics (Opinions)

These are the “Agree – Disagree” questions that tell the opinions of the customers. 

Example: 

  1. Do you agree that brand loyalty is important?
  2. Do you prioritize quality over affordability?
  •  Behavioral

These “Agree – Disagree” questions bring out the behavior of the customers.

Example:

  1. Do you prefer purchasing the costliest option available?
  2. Are you inclined to bargain while making purchases?
  • Attitudinal

These questions measure the attitudes of the customers in an “Agree–Disagree” manner.

Example: 

  1. How satisfied are you with the customer service provided?
  2. Are you content with the product pricing?
  • Activity-based

These “Agree – Disagree” questions tell you what the customer usually does.

Example: 

  1. How frequently do you opt for online shopping?
  2. How often do you visit zoos for leisure?

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Sample Output Reports

Factor analysis generates output reports that summarize the identified factors and their respective contributions to the dataset. These reports typically include factor loadings, eigenvalues, and variance explained, offering valuable insights into the data’s structure and underlying patterns.



Response ID 

Cost Barrier

IT Barrier

Org Barrier

Response_1

0.5

1.4

-0.6

Response_2

0.1

0.1

0.4

Response_3

-0.2

1.2

-0.2

Response_4

0.7

-0.4

-0.3

Response_5

2.7

-0.3

-0.3

Conclusion

Factor analysis is a powerful tool in the realm of data analysis, helping researchers comprehend complex data sets by uncovering inherent patterns and relationships. By asking the right questions and employing the appropriate type of factor analysis, businesses and researchers can unlock valuable insights that can drive informed decisions and strategies. Explore the potential of factor analysis in unraveling your data and propelling your research and business objectives.

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