Market Research
Product- NISH HAIR
Submitted to :-
Professor- J.k Sachdeva
Student Details
| Name | Roll.no | Field |
| Muskan Jain | 21330024590 | Marketing |
| Mahek Chotrani | 21330024318 | Marketing |
| Ansh Seth | 21330024061 | Marketing |
INDEX
| Introduction | 2 |
| Objective | 3 |
| Data Collection | 3-5 |
| Data Analysis | 5-9 |
| KMO & Bartlett’s | 5-9 |
| No. of Factors | 10-12 |
| Rotated Components | 12-13 |
| Conclusion | 14 |
Introduction
Overview of the competitive landscape of Nish Hair Nua along with the existing market trends is presented in this report. The company’s goals include in-depth understanding of their customers’ expectations, quality assessment of offered products, and market segmentation based on potential development prospects. It’s been established in Raipur Chattisgarh believes that any report should reflect current facts and practices in that industry. Therefore, unlike other reports which often contain a whole lot of theories, this report we nish hair will include client focused strategies to result in increased nish hair brand loyalty market share and competitive edge over the existing competitors in the market.
Objectives
The objective of the report is to analyze the factors contributing to the success and popularity of Nish Hair as a brand in the hair extension industry. It focuses on the key characteristics and features of Nish Hair products that resonate with consumers and contribute to their positive brand perception.
Factor Analysis
Nish Hair has captured the hearts of many with its commitment to quality, innovation, and customer satisfaction.
- Effortless –
- Feather-Light Comfort: Imagine the freedom of flowing, beautiful hair without the weight. Nish Hair extensions are so lightweight, you’ll barely notice you’re wearing them.
- Quick and Easy Styling: Say goodbye to complicated hair routines. Nish Hair extensions are designed for hassle-free application, allowing you to effortlessly achieve stunning looks.
- Authentic
- Natural Perfection: Crafted from 100% human hair, Nish Hair extensions blend seamlessly with your own, creating a truly authentic look.
- Endless Possibilities: With a variety of textures, lengths, and colors, you can experiment with different styles and find the perfect match for your unique beauty.
- Last longing
- Durable and Heat-Friendly: Fearlessly style your hair with hot tools. Nish Hair extensions can withstand the heat, ensuring long-lasting beauty.
- Low-Maintenance Luxury: Minimal effort, maximum impact. These extensions are easy to care for, so you can spend less time on maintenance and more time enjoying your gorgeous hair.
- Customisable-
- Personalized Perfection: Want a custom look? Nish Hair offers tailored solutions to bring your hair dreams to life.
- Travel-Ready Glamour: Pack your beauty essentials and hit the road. Nish Hair extensions are compact and travel-friendly, ensuring you always look your best, wherever you go.
- Secure & Reliable
- Secure and Stylish: Nish Hair extensions are designed to stay put, giving you the confidence to conquer any occasion.
The product is the culmination of innovation, quality, and a customer-centric approach, Nish Hair empowers not only women but all individuals to express their personal style and feel confident every single day.
Data Collection
All the responses which we have added in the graphs.
Data Analysis
| KMO and Bartlett’s Test | ||
| Kaiser-Meyer-Olkin Measure of Sampling Adequacy. | .453 | |
| Bartlett’s Test of Sphericity | Approx. Chi-Square | 50.102 |
| df | 45 | |
| Sig. | .278 | |
|
Communalities |
|
| Initial | |
| [Light weight] | 1.000 |
| [Convenient application] | 1.000 |
| [100% human hair] | 1.000 |
| [Multiple varieties] | 1.000 |
| [Durable] | 1.000 |
| [Heat resistant] | 1.000 |
| [Easy maintenance] | 1.000 |
| [Customisable] | 1.000 |
| [Travel friendly] | 1.000 |
| [Firm hold] | 1.000 |
| Extraction Method: Principal Component Analysis. | |
|
Total Variance Explained |
||||
| Component | Initial Eigenvalues | Rotation Sums of Squared Loadingsa | ||
| Total | % of Variance | Cumulative % | Total | |
| 1 | 1.879 | 18.790 | 18.790 | 1.759 |
| 2 | 1.485 | 14.849 | 33.640 | 1.456 |
| 3 | 1.377 | 13.770 | 47.410 | 1.341 |
| 4 | 1.150 | 11.499 | 58.908 | 1.205 |
| 5 | 1.047 | 10.466 | 69.374 | 1.327 |
| 6 | .813 | 8.126 | 77.500 | |
| 7 | .752 | 7.517 | 85.017 | |
| 8 | .604 | 6.038 | 91.055 | |
| 9 | .538 | 5.379 | 96.434 | |
| 10 | .357 | 3.566 | 100.000 | |
| Extraction Method: Principal Component Analysis. | ||||
| a. When components are correlated, sums of squared loadings cannot be added to obtain a total variance. | ||||
| Total Variance Explained | ||||
| Component | Initial Eigenvalues | Rotation Sums of Squared Loadingsa | ||
| Total | % of Variance | Cumulative % | Total | |
| 1 | 1.879 | 18.790 | 18.790 | 1.759 |
| 2 | 1.485 | 14.849 | 33.640 | 1.456 |
| 3 | 1.377 | 13.770 | 47.410 | 1.341 |
| 4 | 1.150 | 11.499 | 58.908 | 1.205 |
| 5 | 1.047 | 10.466 | 69.374 | 1.327 |
| 6 | .813 | 8.126 | 77.500 | |
| 7 | .752 | 7.517 | 85.017 | |
| 8 | .604 | 6.038 | 91.055 | |
| 9 | .538 | 5.379 | 96.434 | |
| 10 | .357 | 3.566 | 100.000 | |
| Extraction Method: Principal Component Analysis. | ||||
| a. When components are correlated, sums of squared loadings cannot be added to obtain a total variance. | ||||
| Component Matrixa |
| a. 5 components extracted. |
| Pattern Matrixa | |||||
| Component | |||||
| 1 | 2 | 3 | 4 | 5 | |
| [Light weight] | .593 | -.103 | -.045 | .064 | -.184 |
| [Convenient application] | .705 | .068 | .231 | -.460 | -.090 |
| [100% human hair] | .771 | -.019 | -.055 | .185 | .218 |
| [Multiple varieties] | -.137 | -.212 | .185 | .006 | -.837 |
| [Durable] | .104 | .023 | .071 | .942 | -.030 |
| [Heat resistant] | .311 | -.011 | .818 | .055 | -.094 |
| [Easy maintenance] | -.094 | .823 | .184 | .168 | -.057 |
| [Customisable] | .304 | -.090 | -.660 | .009 | -.059 |
| [Travel friendly] | .010 | .759 | -.099 | -.164 | .108 |
| [Firm hold] | -.180 | -.343 | .291 | -.014 | .659 |
| Extraction Method: Principal Component Analysis.
Rotation Method: Oblimin with Kaiser Normalization. |
|||||
| a. Rotation converged in 8 iterations. | |||||
| Structure Matrix | |||||
| Component | |||||
| 1 | 2 | 3 | 4 | 5 | |
| [Light weight] | .632 | -.132 | -.072 | .068 | -.277 |
| [Convenient application] | .700 | .059 | .189 | -.469 | -.231 |
| [100% human hair] | .741 | -.066 | -.123 | .194 | .099 |
| [Multiple varieties] | -.002 | -.213 | .251 | -.003 | -.824 |
| [Durable] | .104 | -.056 | .051 | .939 | -.025 |
| [Heat resistant] | .269 | -.071 | .801 | .042 | -.188 |
| [Easy maintenance] | -.135 | .805 | .150 | .101 | -.050 |
| [Customisable] | .365 | -.071 | -.674 | .025 | -.073 |
| [Travel friendly] | -.036 | .775 | -.142 | -.217 | .105 |
| [Firm hold] | -.293 | -.350 | .285 | .026 | .673 |
| Extraction Method: Principal Component Analysis.
Rotation Method: Oblimin with Kaiser Normalization. |
|||||
|
Component Correlation Matrix |
|||||
| Component | 1 | 2 | 3 | 4 | 5 |
| 1 | 1.000 | -.046 | -.071 | .002 | -.165 |
| 2 | -.046 | 1.000 | -.051 | -.076 | -.003 |
| 3 | -.071 | -.051 | 1.000 | -.014 | -.054 |
| 4 | .002 | -.076 | -.014 | 1.000 | .027 |
| 5 | -.165 | -.003 | -.054 | .027 | 1.000 |
| Extraction Method: Principal Component Analysis.
Rotation Method: Oblimin with Kaiser Normalization. |
|||||
Interpretation of Above table
KMO and Bartlett’s Test:
– *KMO (Kaiser-Meyer-Olkin Measure)*: The KMO value is 0.453, which indicates a low level of sampling adequacy. A value closer to 1 suggests that the data is suitable for factor analysis, whereas values below 0.5 (like in this case) suggest that the correlations between variables may not be large enough for reliable factor analysis.
– *Bartlett’s Test of Sphericity*: The p-value is 0.278, which is greater than 0.05. This means that the null hypothesis (that the correlation matrix is an identity matrix) cannot be rejected, indicating that the variables do not correlate enough to proceed with factor analysis confidently.
ANOVA Table:
– The ANOVA table indicates whether the means of the clusters are significantly different. For example:
– “Convenient application” has a significant F value (p = 0.012), showing a difference between clusters for this variable.
– On the other hand, variables like “100% human hair” (p = 0.736) do not show significant differences between the clusters.
– Keep in mind that the F-test results in clustering analysis are descriptive and not hypothesis tests.
*Component Rotation*:
– The Oblimin with Kaiser Normalization rotation method was used. This method allows for correlation between components. Rotation helps in better interpreting factors by redistributing the variance across the components.
– The structure matrix shows the correlations between variables and the factors. For instance, “light weight” loads moderately on factor 1, but “durable” has a strong loading on factor 4
| Initial Cluster Centers | ||
| Cluster | ||
| 1 | 2 | |
| [Light weight] | 3 | 5 |
| [Convenient application] | 1 | 4 |
| [100% human hair] | 1 | 4 |
| [Multiple varieties] | 3 | 4 |
| [Durable] | 2 | 5 |
| [Heat resistant] | 1 | 5 |
| [Easy maintenance] | 1 | 5 |
| [Customisable] | 5 | 1 |
| [Travel friendly] | 3 | 2 |
| [Firm hold] | 2 | 2 |
| Iteration Historya | ||
| Iteration | Change in Cluster Centers | |
| 1 | 2 | |
| 1 | 3.801 | 3.712 |
| 2 | .337 | .396 |
| 3 | .000 | .000 |
| a. Convergence achieved due to no or small change in cluster centers. The maximum absolute coordinate change for any center is .000. The current iteration is 3. The minimum distance between initial centers is 9.000. | ||
| Cluster Membership | ||
| Case Number | Cluster | Distance |
| 1 | 1 | 4.476 |
| 2 | 1 | 4.041 |
| 3 | 2 | 4.328 |
| 4 | 2 | 3.167 |
| 5 | 1 | 3.868 |
| 6 | 2 | 3.916 |
| 7 | 2 | 4.127 |
| 8 | 1 | 3.887 |
| 9 | 2 | 3.105 |
| 10 | 2 | 4.722 |
| 11 | 1 | 4.100 |
| 12 | 1 | 3.350 |
| 13 | 1 | 4.087 |
| 14 | 2 | 3.682 |
| 15 | 2 | 3.740 |
| 16 | 1 | 3.911 |
| 17 | 2 | 4.267 |
| 18 | 1 | 4.269 |
| 19 | 2 | 4.158 |
| 20 | 2 | 3.809 |
| 21 | 1 | 4.221 |
| 22 | 2 | 4.437 |
| 23 | 2 | 5.286 |
| 24 | 1 | 4.776 |
| 25 | 2 | 3.933 |
| 26 | 1 | 3.383 |
| 27 | 2 | 2.895 |
| 28 | 1 | 4.405 |
| 29 | 1 | 4.678 |
| 30 | 1 | 3.426 |
| 31 | 2 | 4.318 |
| 32 | 1 | 3.288 |
| 33 | 2 | 3.966 |
| 34 | 1 | 4.023 |
| 35 | 1 | 5.026 |
| 36 | 1 | 5.066 |
| 37 | 2 | 4.977 |
| 38 | 1 | 2.502 |
| 39 | 1 | 3.873 |
| 40 | 2 | 3.555 |
| 41 | 1 | 5.450 |
| 42 | 1 | 4.480 |
| 43 | 1 | 4.955 |
| 44 | 2 | 2.411 |
| 45 | 2 | 5.262 |
| 46 | 1 | 4.203 |
| 47 | 1 | 4.100 |
| 48 | 1 | 4.823 |
| 49 | 2 | 4.287 |
| 50 | 2 | 3.809 |
| Final Cluster Centers | ||
| Cluster | ||
| 1 | 2 | |
| [Light weight] | 3 | 3 |
| [Convenient application] | 3 | 4 |
| [100% human hair] | 3 | 3 |
| [Multiple varieties] | 3 | 3 |
| [Durable] | 3 | 3 |
| [Heat resistant] | 2 | 4 |
| [Easy maintenance] | 3 | 4 |
| [Customisable] | 3 | 2 |
| [Travel friendly] | 3 | 3 |
| [Firm hold] | 3 | 2 |
| Distances between Final Cluster Centers | ||
| Cluster | 1 | 2 |
| 1 | 2.916 | |
| 2 | 2.916 | |
| ANOVA | ||||||
| Cluster | Error | F | Sig. | |||
| Mean Square | df | Mean Square | df | |||
| [Light weight] | 3.507 | 1 | 2.260 | 48 | 1.552 | .219 |
| [Convenient application] | 12.823 | 1 | 1.875 | 48 | 6.839 | .012 |
| [100% human hair] | .206 | 1 | 1.787 | 48 | .115 | .736 |
| [Multiple varieties] | 8.675 | 1 | 1.545 | 48 | 5.616 | .022 |
| [Durable] | 2.746 | 1 | 1.894 | 48 | 1.450 | .235 |
| [Heat resistant] | 26.729 | 1 | 1.402 | 48 | 19.066 | <.001 |
| [Easy maintenance] | 22.671 | 1 | 1.820 | 48 | 12.458 | <.001 |
| [Customisable] | 14.630 | 1 | 1.802 | 48 | 8.120 | .006 |
| [Travel friendly] | 3.423 | 1 | 1.869 | 48 | 1.832 | .182 |
| [Firm hold] | 10.172 | 1 | 1.690 | 48 | 6.020 | .018 |
| The F tests should be used only for descriptive purposes because the clusters have been chosen to maximize the differences among cases in different clusters. The observed significance levels are not corrected for this and thus cannot be interpreted as tests of the hypothesis that the cluster means are equal. | ||||||
| Number of Cases in each Cluster | ||
| Cluster | 1 | 27.000 |
| 2 | 23.000 | |
| Valid | 50.000 | |
| Missing | .000 | |
cluster Analysis:
– Initial and Final Cluster Centers : This refers to the mean values of the variables for each cluster. The interpretation focuses on how characteristics like “light weight,” “convenient application,” etc., differ between clusters. Cluster 1 might represent consumers who prioritize different aspects than Cluster 2.
– Iteration History : The analysis converged after 3 iterations, meaning that the cluster centers stabilized after 3 recalculations.
– Distances Between Clusters :The distance between the final cluster centers is 2.916, which measures how distinct the two clusters are from each other.
Conclusion
Thus, to sum up this analysis factor and cluster analyses were highly informative for the nature of product-related attributes with respect to hair care.
These components, which we extract using factor analysis then can give us these underlying dimensions in the hair product attributes. For instance, words such as “light weight” only somewhat map to Factor 1 and hence indicate that it has some overlap with the features which are denoted by this factor. Meanwhile, “durable” is highly associated with Factor 4: this seems to be one of the more defining characteristics within this component.
Cluster Analysis: This analysis demonstrated patterns of differentiation among the cases, where certain hair products or groups of product differed in combination with some characteristics. Each of these clusters represent a different set or groupings, for lack of better phrasing, so depending if more weight is assigned to something like durability vs. Weight you may be able to sort and use that data as well targeting consumer segments etc..
Correlations: This is shown by the structure matrix as before which displays how closely correlated each variable correlates with the factors. This not only confirms the factor and cluster findings but also suggests directions of strategic importance for a product market, since high loadings imply attributes that are significant in distinguishing products from one another.
So: Analyzing the composition allows for targeted product differentiation by isolating which attributes strike specific nerves. In general, manufacturers should focus marketing efforts on properties that load high to unique factors (e.g., durability in Factor 4) for consumers who identify those attributes as being important. Clusters provide a basis for segment-specific strategies, complementing product and marketing development with the fulfillable demand of consumers characteristic specific attribute combinations.