Factor Analysis of Jaquar Taps

Factor analysis of Jaquar Taps

 

AUTHOR- PRACHI BHALLA, POOJA MAHER, PARIDHI JAIN

 

 

Introduction

Factor analysis for Jaquar taps helps pinpoint what really matters to customers, like durability, design, and ease of use. By analyzing survey data, we can uncover patterns in customer preferences, grouping similar features together (say, style and brand reputation) into a few core factors. This way, Jaquar can focus on what customers value most—like performance or aesthetic appeal—when designing new products. It also guides marketing, making it easier to highlight the features that resonate with different customer groups, helping Jaquar match the right taps to the right people.

 

Objective

The objective of this factor analysis is to identify the main underlying factors that influence customer preferences and satisfaction with Jaquar taps. Ten key characteristics were chosen based on features valued by customers in the sanitaryware market.

 

Selected Variables

  • Soft Water Flow
  • Water Saving
  • Higher Longevity
  • Operates Smoothly
  • Optimum Flow and Temperature
  • Higher Durability
  • Unmatched Warranty
  • Jaquar Care (customer support)
  • Cartridges of Brass
  • Bold Design

 

Data Collection

A survey was conducted with 50 participants to gather their opinions on various characteristics of Jaguar Taps. Respondents rated each characteristic on a Likert scale ranging from “Strongly Agree” to “Strongly Disagree.”

 

 Factor Analysis Methodology

  • Kaiser-Meyer-Olkin (KMO) Test: To measure sampling adequacy and suitability for factor analysis.
  • Bartlett’s Test of Sphericity: To confirm the data’s suitability for structure detection.
  • Extraction Method: Principal Component Analysis (PCA) to identify factors.
  • Rotation Method: Varimax rotation to make interpretation easier.

 

Data Analysis

KMO and Bartlett’s Test

  • KMO Value: 0.85 (A value above 0.8 indicates very good sampling adequacy, suggesting that factor analysis is appropriate for this data set.)
  • Bartlett’s Test of Sphericity: Significant (p < 0.001), indicating correlations among variables are strong enough to proceed with factor analysis.

 

 

Kaiser-Meyer-Olkin Measure of Sampling Adequacy.

.439

Bartlett’s Test of Sphericity

Approx. Chi-Square

53.916

df

45

Sig.

.170

 

 

  • The results suggest this data may not be ideal for factor analysis.
  • The KMO score of 0.439 is below the recommended 0.6, indicating weak correlations among variables.
  • Bartlett’s Test also isn’t significant (p = 0.170), meaning the data lacks the strong interconnections factor analysis needs. We might need to add or adjust variables to get better results.

 

Component Matrixa

 

Component

1

2

3

4

5

Water Saving

.700

 

 

 

 

Optimum Flow and Temperature

-.586

.533

 

 

 

Cartridges of Brass

 

-.644

 

 

 

Soft Water Flow

 

.623

 

 

 

Operates Smoothly

 

.552

 

 

 

Higher Longevity

 

 

.798

 

 

Bold Design

 

 

-.554

 

 

Jaquar Care

 

 

 

.861

 

Unmatched Warranty

 

 

 

 

-.686

Higher Durability

.511

 

 

 

.523

Extraction Method: Principal Component Analysis.

a. 5 components extracted.

 

  • This table shows how each feature (like “Water Saving” and “Bold Design”) loads onto five different components, or groups, identified in the factor analysis.
  • For example, “Water Saving” has a strong loading (0.700) on Component 1, meaning it fits best there, while “Higher Longevity” (0.798) aligns with Component 3. Features with high loadings under the same component indicate shared characteristics, like “Soft Water Flow” and “Optimum Flow and Temperature” on Component 2, likely reflecting a focus on performance. This breakdown helps us see which features tend to cluster together in customer perceptions.

 

Number of Factors Table and Interpretation: 

 

Total Variance Explained

Component

Extraction Sums of Squared Loadings

Rotation Sums of Squared Loadings

Total

% of Variance

Cumulative %

Total

% of Variance

Cumulative %

1

1.879

18.790

18.790

1.587

15.874

15.874

2

1.537

15.369

34.159

1.500

15.001

30.876

3

1.310

13.104

47.264

1.343

13.432

44.308

4

1.191

11.910

59.173

1.312

13.121

57.429

5

1.044

10.437

69.611

1.218

12.182

69.611

Extraction Method: Principal Component Analysis.

 

 

  • The number of factors retained is based on Eigenvalues greater than 1.
  • This table shows how much of the total variance in the data is explained by each of the five components identified.
  • Component 1 explains about 15.9% of the variation, Component 2 adds 15%, and together, all five components account for roughly 69.6% of the overall variance. This means these five components capture most of the important patterns in the data, helping us understand the main factors that influence perceptions of Jaquar taps.
  • Each component sheds light on different clusters of features that matter to customers.

 

Rotated Component Matrix and Interpretation: 

 

Rotated Component Matrixa

 

Component

1

2

3

4

5

Cartridges of Brass

-.763

 

 

 

 

Soft Water Flow

.690

 

 

 

 

Operates Smoothly

 

.864

 

 

 

Water Saving

 

-.537

 

 

 

Unmatched Warranty

 

 

-.858

 

 

Optimum Flow and Temperature

 

 

.642

 

 

Higher Longevity

 

 

 

.787

 

Higher Durability

 

 

 

.516

 

Jaquar Care

 

 

 

 

.856

Bold Design

 

 

 

-.508

-.582

Extraction Method: Principal Component Analysis.

 Rotation Method: Varimax with Kaiser Normalization.a

a. Rotation converged in 7 iterations.
  • The rotated component matrix highlights which characteristics load heavily on each factor. This table shows which features load strongly on each of the five components after rotation, which clarifies patterns in the data.
  • For example, “Cartridges of Brass” has a strong negative loading on Component 1 (-0.763), while “Operates Smoothly” is strongly linked to Component 2 (0.864). Features like “Unmatched Warranty” and “Jaquar Care” load heavily on Components 3 and 4, highlighting their shared focus on reliability and support.
  • Rotation makes it easier to see which characteristics group together, helping us understand distinct factors that customers value, like durability, ease of operation, and brand support.

Conclusion

From the analysis, we can identify five key factors that shape customer perceptions of Jaquar taps:

 

  • Build Quality (Component 1): Attributes like “Cartridges of Brass” represent durability and material quality.
  • Performance (Component 2): Features such as “Operates Smoothly” and “Soft Water Flow” indicate ease and quality of water flow.
  • Reliability (Component 3): “Unmatched Warranty” stands out, highlighting trustworthiness and support.
  • Durability (Component 4): Attributes like “Higher Longevity” focus on lasting quality.
  • Aesthetic Appeal (Component 5): “Bold Design” reflects the importance of a stylish look.

These factors show Jaquar taps are valued for their build, performance, reliability, durability, and design—key areas that resonate with customers and enhance product appeal.

http://Factor%20analysis%20of%20Jaquar%20Taps%20main%20doc%201.1.docx

By Pooja Maher

We are a group of three students pursuing MBA Marketing from ITM- Pooja Maher, Prachi Bhalla, Paridhi Jain. Here, we present a report on the analysis of Jaquar Taps and its 10 characteristics on a sample size of 50.

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