Description
This is a documentation describes about market research of laptops.
Market Research Assignment Project on Three Major Laptop brands in market Market Segmentation and competitor analysis(Perceptual Mapping)
Cluster analysis
For this survey, a two cluster solution is the ideal solution. Since the Euclidean Difference is the highest between 2 and 3, we can either have a 2 or a 3 cluster solution. Since the sample size is only 50, a 3 cluster solution is not recommended. Agglomeration Schedule Cluster Coefficie Stage Nex Combined nts Cluste t r First Sta Appe ge ars Clust Clust Coefficie Cluste Clust er 1 er 2 nts r1 er 2 26 44 9 12 14 37 24 8 23 29 14 31 20 20 14 3 19 13 2 11 14 14 3 16 7 11 3 27 3 49 34 47 46 45 44 43 42 40 39 38 37 35 34 33 31 29 28 25 24 23 19 18 5 17 41 36 32 30 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 2 2 2 0 0 0 0 0 0 0 0 0 0 0 6 0 0 14 12 0 0 0 0 0 16 22 17 0 0 21 24 0 28 0 0 0 0 0 3 0 0 0 0 0 7 0 2 0 13 11 0 0 8 10 18 0 0 0 0 0 0 0 1 30 14 6 32 34 12 12 20 33 21 17 16 16 15 32 22 24 22 34 38 27 23 36 28 39 44 31 30 36 38
Sta ge
Difference between coefficients(Euclidean Distance) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
11 9 8 12 6 14 4 2 16 9 8 2 4 7 2 2 1 1
22 20 15 13 10 27 6 3 21 11 12 14 16 9 4 7 8 2
2 2 2 2 2 3 3 3.142857 3.5 3.733333 5 5.2 6.888889 7.636364 8.242424 10.19231 10.57143 13.41463
27 4 9 5 0 23 0 20 25 32 33 38 37 26 42 45 0 47
0 15 0 19 0 29 35 30 0 31 34 36 39 40 43 44 41 46
40 40 41 41 37 42 43 42 43 44 47 45 45 46 46 48 48 0
0 0 0 0 1 0 0.142857 0.357143 0.233333 1.266667 0.2 1.688889 0.747475 0.606061 1.949883 0.379121 2.843206
So we have a 2 cluster solution. CLUSTER SOLUTION: Also, from the ANOVA table, we can see that the difference in mean is significant for all attributes (brand, weight, price, memory). Thus, if any cluster is high on mean for a particular attribute, it is significantly different from the other cluster on that attribute. ANOVA Cluster Mean Square Brand Weight Price Memory 19.185 41.222 3.353 10.479 df 1 1 1 1 Error Mean Square .921 .414 .819 .831 df 47 47 47 47 F 20.821 99.499 4.094 12.605 Sig. .000 .000 .049 .001
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.
In this survey, cluster 1 has the highest mean for brand, price and memory, which is significantly higher than the mean of all these attributes for cluster 2.
Also, cluster 2 has the highest mean for weight, which is significantly higher than the mean of this attribute for cluster 1.
Final Cluster Centers Cluster 1 Brand Weight Price Memory 3 2 3 3 2 1 4 2 2
Thus, population in cluster 1 give more weightage to brand, price and memory and the population in cluster 2 give more weightage to weight before buying a laptop. The laptop manufacturer should keep it in mind and segment its target accordingly. COMPARE MEANS: Cluster 1 has a higher mean in brand, price and memory than the total mean of these attributes. And Cluster 2 has a higher mean in Weight than the total mean of this attribute. Also according to the ANOVA table, the difference of means of all the attributes for both the clusters is significant. Thus, population of cluster 1 gives more importance to brand, price and memory. And the population of cluster 2 gives more importance to weight of the laptop. This reinforces our previous analysis. Report Cluster Number of Case 1 Mean N Std. Deviation 2 Mean N Std. Deviation Total Mean N Brand 2.68 19 1.204 1.40 30 .770 1.90 49 Weight 1.68 19 .671 3.57 30 .626 2.84 49 Price 2.74 19 .933 2.20 30 .887 2.41 49 Memory 3.32 19 .885 2.37 30 .928 2.73 49
Report Cluster Number of Case 1 Mean N Std. Deviation 2 Mean N Std. Deviation Total Mean N Std. Deviation Brand 2.68 19 1.204 1.40 30 .770 1.90 49 1.141 Weight 1.68 19 .671 3.57 30 .626 2.84 49 1.124 Price 2.74 19 .933 2.20 30 .887 2.41 49 .934 Memory 3.32 19 .885 2.37 30 .928 2.73 49 1.016
ANOVA Table Sum of Squares Brand * Cluster Between Groups (Combin Number of Case ed) Within Groups Total Weight * Between Groups (Combin Cluster Number ed) of Case Within Groups Total Price * Cluster Between Groups (Combin Number of Case ed) Within Groups Total Memory * Between Groups (Combin Cluster Number ed) of Case Within Groups Total 19.185 43.305 62.490 41.222 19.472 60.694 3.353 38.484 41.837 10.479 39.072 49.551 df 1 47 48 1 47 48 1 47 48 1 47 48 10.479 .831 12.6 05 .001 3.353 .819 4.09 4 .049 41.222 .414 99.4 99 .000 Mean Square 19.185 .921 F 20.8 21 Sig. .000
CONCLUSION: If the laptop manufacturer is launching a light weight laptop then it should target cluster 2. Whereas if a manufacturer is planning to launch a value for money product, with a high brand value and a good memory capacity it should eye cluster 1.
Perceptual mapping for brands of laptop
We tried to gauge the perception of laptops in the mind of people.We tool the survey of 50 people on perception of 3 major laptops in the market. Brands used in the survey 1) HP 2) VAIO 3) DELL We just didn’t use the brands we also had attributes in our survey.We used 6 attributes in total Attributes 1) Performance 2) Battery Life 3) Looks 4) Speed 5) Price 6) Quality We had a matrix which was asked to be filled on a rate of 1-5 BatteryFeatures Performance life Speed
Price HP Vaio Dell
Looks
Price
We organized all the survey in a spreadsheet with 150 rows (50 x 3)
P MAP As we had used 6 attributes we used factor analysis to reduce the data to just two components.Then used the factors to plot the P map for individual brands. MDS can also be used to do the same with discriminant analysis. Below we can see the P map for the survey.The brands are represented by red dots and attributes by blue dots.
The P MAP
Interpretation of P map We see in the P Map that there are 3 brands VAIO in the second quadrant,HP and DELL in the 3rd quadrant. If we see the attributes side by side we see that Quality is not very significant as compared to other attributes. On Attributes related to Performance(both speed and performance included) we see that Dell is perceived to be better than HP and VAiO. On looks VAIO scores higher than both HP and DELL and also on battery life too.HP and DELL both are closer ro each other on Looks and battery life. Now if we look at price Dell and HP both are value for money products whereas Vaio is perceived to be costly product . Overall if we see the overall scenario we see that on performance DELL scores the best,On Looks VAIO is the best(also battery life) HP scores equally on all attributes.
CONJOINT ANALYSIS
We also did conjoint analysis for the data in question number 3. The results We didn’t publish it here as the variables didn’t have the desired significance levels.
Appendix Questionnaire
Name: Age: Education Background:
Questions
Q1. Rank the following attributes of a laptop on a scale of 1-4, where 1 means most preferred and 4 means least preferred. Brand Weight Price Memory Q2. Rate the following attributes of a laptop on a scale of 1-10, where 1 means least preferred and 10 means most preferred. Price HP Vaio Dell BatteryFeatures Performance life Speed Looks Price
Q3. Rank the following configurations of laptops on a scale of 1-9, where 1 means most preferred and 9 means least preferred. The configurations are in the following format: Brand-Weight-Price-Memory (RAM) a. HP Pavillion-1.64-44000-2048MB b. Dell Inspiron-2.75-34000-2GB c. Vaio-1.19-30000-1GB d. Dell Studio-2.75-46000-4GB
e. Vaio-0.594-70000-2GB f. HP Pavillion-2.65-66000-4096MB g. Dell Inspiron-2.25-36000-3GB h. HP Pavillion-2.17-41500-4096MB i. Vaio-0.320-50000-1GB
doc_762437703.docx
This is a documentation describes about market research of laptops.
Market Research Assignment Project on Three Major Laptop brands in market Market Segmentation and competitor analysis(Perceptual Mapping)
Cluster analysis
For this survey, a two cluster solution is the ideal solution. Since the Euclidean Difference is the highest between 2 and 3, we can either have a 2 or a 3 cluster solution. Since the sample size is only 50, a 3 cluster solution is not recommended. Agglomeration Schedule Cluster Coefficie Stage Nex Combined nts Cluste t r First Sta Appe ge ars Clust Clust Coefficie Cluste Clust er 1 er 2 nts r1 er 2 26 44 9 12 14 37 24 8 23 29 14 31 20 20 14 3 19 13 2 11 14 14 3 16 7 11 3 27 3 49 34 47 46 45 44 43 42 40 39 38 37 35 34 33 31 29 28 25 24 23 19 18 5 17 41 36 32 30 26 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 2 2 2 2 2 0 0 0 0 0 0 0 0 0 0 0 6 0 0 14 12 0 0 0 0 0 16 22 17 0 0 21 24 0 28 0 0 0 0 0 3 0 0 0 0 0 7 0 2 0 13 11 0 0 8 10 18 0 0 0 0 0 0 0 1 30 14 6 32 34 12 12 20 33 21 17 16 16 15 32 22 24 22 34 38 27 23 36 28 39 44 31 30 36 38
Sta ge
Difference between coefficients(Euclidean Distance) 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48
11 9 8 12 6 14 4 2 16 9 8 2 4 7 2 2 1 1
22 20 15 13 10 27 6 3 21 11 12 14 16 9 4 7 8 2
2 2 2 2 2 3 3 3.142857 3.5 3.733333 5 5.2 6.888889 7.636364 8.242424 10.19231 10.57143 13.41463
27 4 9 5 0 23 0 20 25 32 33 38 37 26 42 45 0 47
0 15 0 19 0 29 35 30 0 31 34 36 39 40 43 44 41 46
40 40 41 41 37 42 43 42 43 44 47 45 45 46 46 48 48 0
0 0 0 0 1 0 0.142857 0.357143 0.233333 1.266667 0.2 1.688889 0.747475 0.606061 1.949883 0.379121 2.843206
So we have a 2 cluster solution. CLUSTER SOLUTION: Also, from the ANOVA table, we can see that the difference in mean is significant for all attributes (brand, weight, price, memory). Thus, if any cluster is high on mean for a particular attribute, it is significantly different from the other cluster on that attribute. ANOVA Cluster Mean Square Brand Weight Price Memory 19.185 41.222 3.353 10.479 df 1 1 1 1 Error Mean Square .921 .414 .819 .831 df 47 47 47 47 F 20.821 99.499 4.094 12.605 Sig. .000 .000 .049 .001
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.
In this survey, cluster 1 has the highest mean for brand, price and memory, which is significantly higher than the mean of all these attributes for cluster 2.
Also, cluster 2 has the highest mean for weight, which is significantly higher than the mean of this attribute for cluster 1.
Final Cluster Centers Cluster 1 Brand Weight Price Memory 3 2 3 3 2 1 4 2 2
Thus, population in cluster 1 give more weightage to brand, price and memory and the population in cluster 2 give more weightage to weight before buying a laptop. The laptop manufacturer should keep it in mind and segment its target accordingly. COMPARE MEANS: Cluster 1 has a higher mean in brand, price and memory than the total mean of these attributes. And Cluster 2 has a higher mean in Weight than the total mean of this attribute. Also according to the ANOVA table, the difference of means of all the attributes for both the clusters is significant. Thus, population of cluster 1 gives more importance to brand, price and memory. And the population of cluster 2 gives more importance to weight of the laptop. This reinforces our previous analysis. Report Cluster Number of Case 1 Mean N Std. Deviation 2 Mean N Std. Deviation Total Mean N Brand 2.68 19 1.204 1.40 30 .770 1.90 49 Weight 1.68 19 .671 3.57 30 .626 2.84 49 Price 2.74 19 .933 2.20 30 .887 2.41 49 Memory 3.32 19 .885 2.37 30 .928 2.73 49
Report Cluster Number of Case 1 Mean N Std. Deviation 2 Mean N Std. Deviation Total Mean N Std. Deviation Brand 2.68 19 1.204 1.40 30 .770 1.90 49 1.141 Weight 1.68 19 .671 3.57 30 .626 2.84 49 1.124 Price 2.74 19 .933 2.20 30 .887 2.41 49 .934 Memory 3.32 19 .885 2.37 30 .928 2.73 49 1.016
ANOVA Table Sum of Squares Brand * Cluster Between Groups (Combin Number of Case ed) Within Groups Total Weight * Between Groups (Combin Cluster Number ed) of Case Within Groups Total Price * Cluster Between Groups (Combin Number of Case ed) Within Groups Total Memory * Between Groups (Combin Cluster Number ed) of Case Within Groups Total 19.185 43.305 62.490 41.222 19.472 60.694 3.353 38.484 41.837 10.479 39.072 49.551 df 1 47 48 1 47 48 1 47 48 1 47 48 10.479 .831 12.6 05 .001 3.353 .819 4.09 4 .049 41.222 .414 99.4 99 .000 Mean Square 19.185 .921 F 20.8 21 Sig. .000
CONCLUSION: If the laptop manufacturer is launching a light weight laptop then it should target cluster 2. Whereas if a manufacturer is planning to launch a value for money product, with a high brand value and a good memory capacity it should eye cluster 1.
Perceptual mapping for brands of laptop
We tried to gauge the perception of laptops in the mind of people.We tool the survey of 50 people on perception of 3 major laptops in the market. Brands used in the survey 1) HP 2) VAIO 3) DELL We just didn’t use the brands we also had attributes in our survey.We used 6 attributes in total Attributes 1) Performance 2) Battery Life 3) Looks 4) Speed 5) Price 6) Quality We had a matrix which was asked to be filled on a rate of 1-5 BatteryFeatures Performance life Speed
Price HP Vaio Dell
Looks
Price
We organized all the survey in a spreadsheet with 150 rows (50 x 3)
P MAP As we had used 6 attributes we used factor analysis to reduce the data to just two components.Then used the factors to plot the P map for individual brands. MDS can also be used to do the same with discriminant analysis. Below we can see the P map for the survey.The brands are represented by red dots and attributes by blue dots.
The P MAP
Interpretation of P map We see in the P Map that there are 3 brands VAIO in the second quadrant,HP and DELL in the 3rd quadrant. If we see the attributes side by side we see that Quality is not very significant as compared to other attributes. On Attributes related to Performance(both speed and performance included) we see that Dell is perceived to be better than HP and VAiO. On looks VAIO scores higher than both HP and DELL and also on battery life too.HP and DELL both are closer ro each other on Looks and battery life. Now if we look at price Dell and HP both are value for money products whereas Vaio is perceived to be costly product . Overall if we see the overall scenario we see that on performance DELL scores the best,On Looks VAIO is the best(also battery life) HP scores equally on all attributes.
CONJOINT ANALYSIS
We also did conjoint analysis for the data in question number 3. The results We didn’t publish it here as the variables didn’t have the desired significance levels.
Appendix Questionnaire
Name: Age: Education Background:
Questions
Q1. Rank the following attributes of a laptop on a scale of 1-4, where 1 means most preferred and 4 means least preferred. Brand Weight Price Memory Q2. Rate the following attributes of a laptop on a scale of 1-10, where 1 means least preferred and 10 means most preferred. Price HP Vaio Dell BatteryFeatures Performance life Speed Looks Price
Q3. Rank the following configurations of laptops on a scale of 1-9, where 1 means most preferred and 9 means least preferred. The configurations are in the following format: Brand-Weight-Price-Memory (RAM) a. HP Pavillion-1.64-44000-2048MB b. Dell Inspiron-2.75-34000-2GB c. Vaio-1.19-30000-1GB d. Dell Studio-2.75-46000-4GB
e. Vaio-0.594-70000-2GB f. HP Pavillion-2.65-66000-4096MB g. Dell Inspiron-2.25-36000-3GB h. HP Pavillion-2.17-41500-4096MB i. Vaio-0.320-50000-1GB
doc_762437703.docx