case solution_brand equity
TRANSCRIPT
GROUP 6
S. No. Name Roll Number1 Govinda Kumar 500122 Ruchika Wardhan 600323 Satya Prakash 600334 Shalini 600355 Shilpa Gupta 600366 Sushant Saurav 600427 Utkarsh Anand 60043
Case Study On BRAND EQUITY
Qs1.What statistical analysis is suitable to measure Brand equity with the collected data? Why?
. Brand equity is identified as the value added to a brand due to its name. High Brand equity helps companies
maintain their competitive advantage.
Brand equity is a difficult concept to measure due to its intangible and complex nature. Ariel research
tried to quantify brand equity, that is turn an intangible concept to tangible measurement. It created a multi-dimensional measure with five
main dimensions:
Familiarity, popularity, relevancy, loyalty & uniqueness.
They used binary variables (0,1) for analysing the data. They collected 125000 records and split the
responses into high (1) & low.(0) On a scale of 1 to 10, 1-7 was given low (0) and 8,9,10 was given
high.(1)
From our group analysis mode has been used for statistical analysis.
For example,if we consider a particular age group(i.e.20 to 25years) we would see how many
times 0 or 1 is coming. The highest frequency shows the consumers being more loyal to a
particular brand.
Ans
Comparing of BrandsBrands 263 264 265 266
Ratings Closer to 0 Closer to 1 Closer to 0 Closer to 1 Closer to 0 Closer to 1 Closer to 0Questions
Famil 128 155 127 164 164 152 47Uniqu 173 108 195 95 200 114 131Relev 196 85 197 92 212 103 162Loyal 196 87 190 101 215 101 142Popul 155 128 117 171 174 139 30
FindingsMost familiar brand is 266
Most unique brand is 265 and least unique brand is 264Most relevent brand is 265 and least relevent brand is 263
Most loyal brand is 265 and least loyal brand is 263Most popular brand is 266
Supporting data for Comparision
Brands Rating Famil Uniqu Relev1 15 16 292 15 14 173 13 19 214 12 18 13
263 5 34 51 516 24 26 327 15 128 29 173 33 1968 52 44 339 21 29 16
10 82 155 35 108 36 85Total 283 281 281
Question 2 . Compare loyality ,relevance,familiarity,uniqueness and popularity for its brands using the appropriate statistical analysis.
We have calculated total number of rating of familiarity, uniqueness, relevence, loyality and popularity o f each brand . After that we have compared the all brand on the basis of familiarity,uniqueness,relevence,loyality and popularity
Assumption : we have consider responses 1-7 is "Closer to 0" and 8-10 is "Closer to 1"
Brands and their number of ratings closer to 0 and 1 on the basis of familliarity,uniqueness,relevence,loyality and popullarity
Brands Rating Famil Uniqu Relev1 16 17 362 11 16 183 12 22 194 10 23 20
264 5 32 48 416 15 34 327 31 127 35 195 31 1978 47 44 349 41 24 24
10 76 164 27 95 34 92Total 291 290 289
Brands Rating Famil Uniqu Relev1 50 40 532 22 20 233 13 14 124 11 14 26
265 5 26 55 406 18 33 317 24 164 24 200 27 2128 36 53 429 37 25 24
10 79 152 36 114 37 103316 314 315
Brands Rating Famil Uniqu Relev1 3 10 182 4 11 153 2 7 184 1 10 19
266 5 10 40 366 10 19 237 17 47 34 131 33 1628 43 61 549 67 37 45
10 150 260 79 177 47 146307 308 308
Brands Rating Famil Uniqu Relev1 41 35 432 13 17 23
3 12 12 124 15 15 17
267 5 24 45 456 25 22 267 26 156 34 180 23 1898 44 44 389 31 30 23
10 71 146 47 121 51 112302 301 301
266 267
Closer to 1 Closer to 0 Closer to 1
260 156 146177 180 121146 189 112166 205 97277 149 152
Loyal Popul25 925 1311 1118 1859 4227 2631 196 36 15533 3816 3938 87 51 128
283 283
Question 2 . Compare loyality ,relevance,familiarity,uniqueness and popularity for its brands using the
We have calculated total number of rating of familiarity, uniqueness, relevence, loyality and popularity o f each brand . After that we have compared the all brand on the basis of familiarity,uniqueness,relevence,loyality and popularity
Loyal Popul47 1321 320 513 1134 3023 1632 190 39 11749 5018 4334 101 78 171
291 288
Loyal Popul63 3829 1312 1619 1443 3824 2925 215 26 17441 5620 3240 101 51 139
316 313
Loyal Popul21 411 116 114 040 615 925 142 9 3070 3935 5961 166 179 277
308 307
Loyal Popul58 3124 6
12 813 1346 2826 2326 205 40 14935 5722 3040 97 65 152
302 301
LOYALITY vs AGE for BRAND 263Count of loyalbin Column Labels
20 21 22 23 24 25Brand 263 LOW 1 1 5 3 2
HIGH 1 1 2
LOYALITY vs REGION for BRAND 263
Count of loyalbin Column LabelsRow Labels Maritiimes Quebec Ontario West Grand TotalBRAND 263 TOTAL 21 66 102 94 283BRAND 263 LOW 16 46 89 45 196BRAND 263 HIGH 5 20 13 49 87
Q 3). Analyze a Fast Food Brand to determine relationship between Loyality and Respondats profile (eg. Age , Region , Income ).
1. For Brand 263 , Number of LOW is more than Number of High. It signifies, More samples are not loyal to the brand.
2. For Brand 263 ,from the age group 31 to 40 , Number of LOW is significantly more than Number of High. It signifies, More samples are
not loyal to the brand esp in this age zone.
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 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 640
2
4
6
8
10
12
14
16
1 1
5
32
43
4 4 45
16
89
7
2
6
9
5
7
3
7
3
7
5 5
3
78
3
54
1
3 32
3 32
32
32
1 12
34
3 34
32 2 2
4 4 4
2 2 2 21
32 2
12 2 2
3 3
1
32
3
12 2
1
LOW HIGH
Maritiimes Quebec Ontario West0
20
40
60
80
100
120
16
46
89
45
5
2013
49
TOTAL LOW HIGH
LOYALITY vs INCOME for BRAND 263
Count of loyalbin Column LabelsRow Labels <30 k 30 - 49.9 K 50-74.9 K 75 k+BRAND 263 LOW 47 52 57 40BRAND 263 HIGH 23 18 25 21BRAND 263 70 70 82 61
Maritiimes Quebec Ontario West0
20
40
60
80
100
120
16
46
89
45
5
2013
49
TOTAL LOW HIGH
<30 k 30 - 49.9 K 50-74.9 K 75 k+0
10
20
30
40
50
60
4752
57
40
2318
2521
LOW HIGH
26 27 28 29 30 31 32 33 34 354 3 4 4 4 5 16 8 9 73 4 3 3 4 3 2 2
1.In WEST region , more people are loyal to the Brand 263 . As it shown by the number of samples for HIGH is more than Number of Samples for LOW
1.In ONTARIO region , There is a wide gap between the loyal and not loyal samples for BRAND 263. as
the Number for LOW loyality exceeds .
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 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 640
2
4
6
8
10
12
14
16
1 1
5
32
43
4 4 45
16
89
7
2
6
9
5
7
3
7
3
7
5 5
3
78
3
54
1
3 32
3 32
32
32
1 12
34
3 34
32 2 2
4 4 4
2 2 2 21
32 2
12 2 2
3 3
1
32
3
12 2
1
LOW HIGH
1.For all income range, there is uniformity , as LOW loyality is more than the HIGH Loyality samples.
2. In the middle income Range, From 30 to 49.9 K and 50 to 74.9 K , the Gap between Loyal and NOT LOYAL is large , showing affinity of other brands.
36 37 38 39 40 41 42 43 44 452 6 9 5 7 3 7 3 7 52 4 4 4 2 2 2 2 1 3
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 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 640
2
4
6
8
10
12
14
16
1 1
5
32
43
4 4 45
16
89
7
2
6
9
5
7
3
7
3
7
5 5
3
78
3
54
1
3 32
3 32
32
32
1 12
34
3 34
32 2 2
4 4 4
2 2 2 21
32 2
12 2 2
3 3
1
32
3
12 2
1
LOW HIGH
46 47 48 49 50 51 52 53 54 555 3 7 8 3 5 4 1 3 32 2 1 2 2 2 3 3 1 3
56 57 58 59 60 61 62 63 64 (blank)2 3 3 2 3 2 3 2 42 3 1 2 2 1 1
Grand Total196
87
LOST INFORMATION
Q4.Ariel created binary variables for familiarity ,uniqueness,relevance,loyality and popularity by splitting responses into “high” and “low”.why would they would choose to do(or not do)this?In other words ,what information is gained and what information is
lost?
Ans: ariel research created binary variables by splitting responses into “high” and “low”.They have considered various factors while calculating brand equity .if they would
have chosen exact numbers(like 1 to 7) or (8,9,10),then it was even more difficult to analyze the data.In fact the data consisits of 125000 records.By creating binary variables ,they
became somewhat comfortable in analyzing data.
now by creating binary variables for responses, exact information for a brand got lost.if we are measuring a data on a scale of 1 to 7(i.e.for low) there can be a huge difference
between 1 and 7 but in this data sheet they are clubbed into same category as low. Now this creates confusion while analyzing data.1 and 7 can be extreme values but they have
been grouped into one category.
Q4.Ariel created binary variables for familiarity ,uniqueness,relevance,loyality and popularity by splitting responses into “high” and “low”.why would they would choose to do(or not do)this?In other words ,what information is gained and what information is
Ans: ariel research created binary variables by splitting responses into “high” and “low”.They have considered various factors while calculating brand equity .if they would
have chosen exact numbers(like 1 to 7) or (8,9,10),then it was even more difficult to analyze the data.In fact the data consisits of 125000 records.By creating binary variables ,they
became somewhat comfortable in analyzing data.
now by creating binary variables for responses, exact information for a brand got lost.if we are measuring a data on a scale of 1 to 7(i.e.for low) there can be a huge difference
between 1 and 7 but in this data sheet they are clubbed into same category as low. Now this creates confusion while analyzing data.1 and 7 can be extreme values but they have
Q 5) . Do You agree Ariel's Measure of BRAND EQUITY ?
In an era of high competition and expectations, customer satisfaction surveys are essential tools for listening to customers about their satisfaction levels, and for developing strategies for improvement. Now
that, Brand Equity and Quality has become a deciding factor in product selection for the customer.
I think whatever the method was adopted by Ariel Research , a market research company to measure Brand Equity is appropriate. They asked the survey respondents to rate their satisfaction, using a scale
from 1 to 10 .The more they agreed with a questions, the closer the score was to 10, the less they
agreed ,the closer the score was to 1. So, Ariel decided that a response of 8,9 or 10 indicated high
brand loyalty