- Group members:
- Anubhav Shrivastav
- Raj Mistry
- Yugansh Singh Rao
- Brand:Snitch Clothing
- Introduction:
Snitch is an emerging men’s clothing brand known for its stylish and versatile fashion offerings tailored to modern urban lifestyles. Based in India, Snitch focuses on delivering trendy, comfortable, and affordable men’s apparel, including t-shirts, shirts, trousers, and accessories. The brand is recognized for its fast-fashion approach, keeping pace with the latest styles and frequently updating its collections to meet current fashion trends. Snitch has gained popularity for its commitment to quality, unique designs, and seamless online shopping experience, appealing especially to younger consumers looking for a balance between fashion-forward looks and everyday comfort.
- Data collection:
A survey is a structured data collection tool commonly used to gather information from a specific group of people. Surveys can help organizations like Snitch clothing brand gain insight into customer preferences, satisfaction, demographics, and behaviors. We have collected data from family, friends, existing and from other brands customers.
| KMO and Bartlett’s Test | ||
| Kaiser-Meyer-Olkin Measure of Sampling Adequacy. | .513 | |
| Bartlett’s Test of Sphericity | Approx. Chi-Square | 53.126 |
| df | 45 | |
| Sig. | .190 | |
1.)
INTERPRETATION: The KMO value of 0.513 suggests marginal sampling adequacy, while Bartlett’s Test (Sig. = 0.190) indicates that correlations among variables are not significant. Overall, the data may not be ideal for factor analysis.
| Component Matrix | |||||
| Component | |||||
| 1 | 2 | 3 | 4 | 5 | |
| Range of product | -.673 | ||||
| Heavy social media | .588 | ||||
| Effective | .558 | ||||
| Easy availability | .513 | ||||
| UX/UI user friendly | .680 | ||||
| After sales | |||||
| Affordabal | -.699 | ||||
| Urban asethetic | .539 | ||||
| Men basied | .720 | ||||
| Unique design | .577 | ||||
2.)
INTERPRETATION: This Component Matrix from the Principal Component Analysis (PCA) reveals patterns in how different product attributes group together across five main components. In Component 1, attributes such as “Range of product,” “Heavy social media,” “Effective,” and “Easy availability” load strongly, indicating a potential focus on product variety, marketing presence, and accessibility. Component 2 shows strong loadings for “UX/UI user-friendly” and “Affordable,” suggesting a focus on user experience balanced with cost-effectiveness. Component 3 highlights “Urban aesthetic,” pointing to a style or design appeal. Component 4 is associated with “Men-based,” which could suggest a male-oriented product focus, while Component 5 emphasizes “Unique design,” showing a distinct emphasis on product uniqueness. Together, these components outline key themes in product attributes thaay be useful in understanding customer preferences or market positioning.
| Total Variance Explained | ||||||
| Component | Extraction Sums of Squared Loadings | Rotation Sums of Squared Loadings | ||||
| Total | % of Variance | Cumulative % | Total | % of Variance | Cumulative % | |
| 1 | 1.699 | 16.990 | 16.990 | 1.501 | 15.006 | 15.006 |
| 2 | 1.515 | 15.155 | 32.144 | 1.399 | 13.989 | 28.995 |
| 3 | 1.228 | 12.276 | 44.420 | 1.246 | 12.460 | 41.455 |
| 4 | 1.127 | 11.268 | 55.688 | 1.231 | 12.310 | 53.765 |
| 5 | 1.025 | 10.246 | 65.934 | 1.217 | 12.169 | 65.934 |
3.)
INTERPRETATION: The Total Variance Explained table from the Principal Component Analysis (PCA) shows that five components were extracted, collectively explaining 65.93% of the total variance in the data. Initially, Component 1 explains 16.99% of the variance, but after rotation, this reduces to 15.01%, which redistributes variance more evenly among components for clearer interpretation. Similarly, the other components explain between 10% and 15% of the variance after rotation, with each subsequent component adding to the cumulative percentage. By the fifth component, the cumulative variance explained reaches 65.93%, indicating that these five components account for a substantial portion of the variability in the data, providing a reasonably concise summary of underlying factors.
| Rotated Component Matrix | |||||
| Component | |||||
| 1 | 2 | 3 | 4 | 5 | |
| Heavy social media | .803 | ||||
| Easy availability | .710 | ||||
| Affordabal | .814 | ||||
| After sales | -.587 | ||||
| Urban asethetic | .790 | ||||
| Range of product | .609 | ||||
| Unique design | .822 | ||||
| UX/UI user friendly | .586 | ||||
| Men basied | .849 | ||||
| Effective | .510 | ||||
4.)
INTERPRETATION: The Rotated Component Matrix from the Principal Component Analysis (PCA), using Varimax rotation, shows clearer groupings of variables onto five distinct components. Component 1 has strong loadings for “Heavy social media” (0.803) and “Easy availability” (0.710), indicating these variables share a common underlying factor, possibly accessibility or visibility. Component 2 includes “Affordable” (0.814) and “After sales” (-0.587), suggesting a focus on cost and post-purchase support. Component 3 loads highly on “Urban aesthetic” (0.790) and “Range of product” (0.609), highlighting style and variety. Component 4 emphasizes “Unique design” (0.822) and “UX/UI user friendly” (0.586), which might relate to design and user experience. Lastly, Component 5 loads heavily on “Men based” (0.849) and “Effective” (0.510), possibly indicating a product focus targeting men and functionality. These rotated components make the underlying factors more interpretable by clustering related attributes.