Cluster Analysis of SNITCH Brand

• CLUSTER ANALYSIS OF SNITCH BRAND:

 

• Group members:
• Anubhav Shrivastav
• Raj Mistry
• Yugansh Singh Rao
• Brand: Snitch Clothing

Snitch is a contemporary Indian menswear brand known for its trendy and affordable clothing. Founded in 2019, it has quickly gained popularity for its fast-fashion approach, offering a wide range of styles from casual wear to work essentials. Snitch emphasizes high-quality fabrics, unique designs, and frequent new collections, catering to the fashion-forward, urban male consumer. The brand operates predominantly online, leveraging social media and influencer collaborations to build a strong digital presence and engage with its young, style-conscious audience.

 

ANOVA
		Sum of Squares	df	Mean Square	F	Sig.
Affordable	Between Groups	773.720	81	9.552	.	.
	Within Groups	.000	0	.
	Total	773.720	81
Easy Availability	Between Groups	700.598	81	8.649	.	.
	Within Groups	.000	0	.
	Total	700.598	81
Unique Design	Between Groups	609.476	81	7.524	.	.
	Within Groups	.000	0	.
	Total	609.476	81
Men Baised	Between Groups	550.451	81	6.796	.	.
	Within Groups	.000	0	.
	Total	550.451	81
Range of Product	Between Groups	725.024	81	8.951	.	.
	Within Groups	.000	0	.
	Total	725.024	81
Effective Supply Chain Management	Between Groups	519.805	81	6.417	.	.
	Within Groups	.000	0	.
	Total	519.805	81
After Sales Service	Between Groups	728.110	81	8.989	.	.
	Within Groups	.000	0	.
	Total	728.110	81
UXUI user friendly	Between Groups	617.720	81	7.626	.	.
	Within Groups	.000	0	.
	Total	617.720	81
Urban Aesthetic	Between Groups	712.195	81	8.793	.	.
	Within Groups	.000	0	.
	Total	712.195	81

 

 

• INTERPRETATION:

The data provided is a summary of an Analysis of Variance (ANOVA) for various attributes related to a product or brand, possibly from the perspective of brand features or customer perceptions. The variables include factors like “Affordability,” “Easy Availability,” “Unique Design,” and more.

However, there is an issue in the data structure, as the “Within Groups” sum of squares (SS) and degrees of freedom (df) are zero for all variables. This scenario suggests that there might be only one group, or there was an issue with data entry. Normally, ANOVA is meaningful only when there are two or more groups to compare. Here’s a summary of what the data implies and what might be missing:

1. Sum of Squares (SS) Between Groups: The values (e.g., 773.720 for “Affordable,” 700.598 for “Easy Availability”) represent the variation in scores between the group averages and the grand mean. Higher SS values could indicate larger differences between groups for each variable, although this alone does not indicate statistical significance.
2. Degrees of Freedom (df): The degrees of freedom for the between-groups analysis are all listed as 81. Typically, this would mean there were 82 groups (df + 1). However, ANOVA results usually include a separate “Within Groups” df to help assess variation within groups.
3. Mean Square (MS): The MS values for each factor (such as 9.552 for “Affordable”) represent the average variability between the groups for each characteristic. This is calculated by dividing the SS between groups by the df.
4. F-statistic and Significance (Sig.): The F and Sig. (p-value) columns are incomplete in your data, which makes it impossible to assess the statistical significance of the factors. Typically, a lower p-value (e.g., < 0.05) would indicate significant differences among groups, which would suggest that respondents’ perceptions vary significantly across groups for that factor.

 

Cluster Membership
Case Number	sn	Cluster	Distance
1	1	3	7.678
2	2	4	10.010
3	3	2	7.382
4	4	1	9.472
5	5	4	6.482
6	6	4	7.423
7	7	4	6.763
8	8	3	5.339
9	9	1	9.075
10	10	4	6.144
11	11	1	5.874
12	12	3	6.637
13	13	2	7.447
14	14	1	7.846
15	15	1	6.919
16	16	3	6.502
17	17	1	8.726
18	18	3	8.573
19	19	4	6.297
20	20	3	6.214
21	21	4	7.374
22	22	2	5.289
23	23	1	7.188
24	24	2	6.742
25	25	3	6.196
26	26	4	7.312
27	27	1	7.503
28	28	1	6.781
29	29	2	6.394
30	30	3	8.947
31	31	4	7.130
32	32	3	8.290
33	33	2	8.437
34	34	3	8.377
35	35	2	6.661
36	36	2	6.703
37	37	2	4.371
38	38	1	5.169
39	39	2	9.145
40	40	1	5.575
41	41	3	5.482
42	42	2	7.035
43	43	4	7.706
44	44	1	7.863
45	45	1	8.586
46	46	4	5.512
47	47	4	8.465
48	48	2	8.318
49	49	2	6.651
50	50	2	5.538
51	51	2	6.904
52	52	4	5.479
53	53	2	5.987
54	54	1	7.077
55	55	2	7.551
56	56	4	5.770
57	57	3	8.263
58	58	4	7.503
59	59	4	6.669
60	60	2	6.222
61	61	1	7.232
62	62	4	7.927
63	63	4	7.399
64	64	2	9.076
65	65	1	6.264
66	66	3	7.375
67	67	1	8.412
68	68	3	7.390
69	69	4	6.517
70	70	4	8.497
71	71	1	4.772
72	72	2	4.793
73	73	1	5.725
74	74	3	8.554
75	75	3	5.148
76	76	2	7.146
77	77	2	6.312
78	78	4	6.232
79	79	2	7.750
80	80	3	8.134
81	81	4	6.923
82	82	3	8.753

 

This data presents the results of a cluster analysis, where each “Case Number” is assigned to one of four clusters. The “Distance” column indicates the Euclidean distance between each case and the centroid of its assigned cluster. Lower distances imply that a case is closer to the cluster centre, suggesting it fits well within that cluster, while higher distances indicate cases further from the centroid, suggesting they may be less typical representatives of the cluster.

1. Cluster Distribution: Cases are spread across four clusters, with each cluster likely representing a different group based on shared characteristics. The distribution of cases among clusters can be analysed to understand the balance or dominance of certain clusters.
2. Cluster Cohesion: The range of distances within each cluster reflects its internal cohesion. A cluster with consistently lower distances (e.g., most cases around 5-7 units from the centroid) suggests a tightly knit group with similar traits. In contrast, a wide range of distances may indicate more variability within that cluster.
3. Outliers: Cases with notably high distances (e.g., 10.010 for Case 2 in Cluster 4, or 8.753 for Case 82 in Cluster 3) could be outliers, as they are farther from their respective centroids, possibly making them less representative of their clusters.

To deepen the interpretation, analysing the characteristics that define each cluster would be beneficial, as it would clarify the factors that differentiate these groups. Additionally, considering the overall spread and averages of distances could offer insights into the relative distinctiveness or similarity among the clusters.