market research- 1a

Section A - Group-1 Ankush Mittal |Akhil Bhambri | Pooja Sharma |

Sriram Venkatakrishnan | Viraj Hede

Background

• Smirnoff’s R&D team has prepared two new blends; claim to be superior than the one already in market• “Blind test” of the two new blends vs the one in the market among regular consumers of vodka needs to be done• Blind test to be done between Smirnoff drinkers and important competition brand like Fuel and Magic Moments drinkers• Any of the two new blends to be considered for a change, if it comes out to be significantly (at 95%) better than the current blend (with max 5% sampling error)• Markets chosen for study: Delhi, Bangalore, Mumbai, Chennai and Kolkata

Background Research Design Objectives Attribute

Primary Objective Analysis

Secondary Objective Analysis

Summary Conclusion Action Item

RESEARCH DESIGNBackground Research

Design Objectives AttributePrimary Objective Analysis



The research will be conducted with sequential monadic exposure with 3 blends placed for

consumption one after the other and feedback will be taken after each consumption

Neutralizers given after each consumption Current blend(control blend) is blend 2 in data. The two new brands are blend 1 and 3 in data

Target Group

Regular consumer of any one of the three

brands – Smirnoff,

Fuel ,Magic Moments

Males/Females in the age group of 25 – 35 yrs

Consuming vodka at least twice a week

• 760, each person being given 3 blends for feedback

• Thus there are 760 X 3 = 2280 data points as feedback on the vodka blends

Sample Size

Primary Objective of the Analysis

Can the Current Product (Control) be replaced by any of the two Test Products?

ACTION STANDARD: The Test Product has to be significantly better (at 90% or 95% Confidence level) in “Overall Likeability” and two of the other most important attributes

Additionally, it should do better than Control among important sub-groups (at 90% Confidence level)





Secondary Objectives of the Analysis

1• Which are the attributes that drive overall preference of vodka? What is the extent to which they drive? (Meaning, which are the more important and which are the less important drivers?)

2• Can the set of attributes be reduced to a smaller set of Factors, and later, it could be found, how the broad Factors drive the overall preference of vodka?

3• If the purchase intention is taken as a categorical variable (YES / NO), can it be predicted in future, by reading some of the ratings of the attributes only? How?





Information - Attributes Dependent:

• 5A_Attr1 : Overall Likeability of the vodka blend

Independent:

• 5A_Attr2 : Likeability of Aroma

• 5A_Attr3 : Likeability of Taste

• 5A_Attr4 : Likeability of Smoothness

• 5A_Attr5 : Likeability of Flavour

• 5A_Attr6 : Likeability of Throat-feel when the vodka goes down

• 5A_Attr7 : Likeability of After-taste

• 5A_Attr8 : Likeability of Mouth-feel when the vodka is sipped

Background Research Design Objective Attribute








CROSS TAB ANALYSIS:

Done over the entire sample on :

Overall Likeability Taste Mouth Feel

1) Overall Likeability:

• Product 2: Control Product Product 1 and Product 3: Test Products

• We consider the summation of the 9th and 10th rating of all the three products and add their respective errors. Hence we get percentage ranges for the products. If overlap found with 95% confidence level, we try with 90%.

• If there is no overlap between the ranges of product 1 and 2, Product 1 can be launched.





Overall Likeability, Confidence Level = 95%

For Product 1: 23.8% + 12.0% = 35.8%Error at Confidence level 95% with N = 7601.96 * sqrt (0.36 * 0.64 / 760) = 3.4%Range : 35.8% + 3.4% = 39.2% 35.8% – 3.4% = 32.4%Range = 32.4% to 39.2%

For Product 2: 18.4% + 11.8% = 30.2%Error at Confidence level 95% with N = 7601.96 * sqrt (0.30 * 0.70 / 760) = 3.2%Range : 30.2% + 3.2% = 33.4% 30.2% – 3.2% = 27%Range = 27% to 33.4%


The test products and the control have overlaps between their ranges. We will not be able to launch the new products with 95% confidence level based on

overall likeability





Overall Likeability, Confidence Level = 90%

For Product 1: 23.8% + 12.0% = 35.8%Error at Confidence level 90% with N = 7601.645 * sqrt (0.36 * 0.64 / 760) = 2.8%Range : 35.8% + 2.8% = 38.6% 35.8% – 2.8% = 33%Range = 33% to 38.6%



The range of Product 1 does not overlap with that of Product 2. We should launch Product 1 if considered with 90% confidence level based on Overall Likeability





Taste at Confidence Level = 95%




Since the test products and the control have overlaps between their ranges, we will not be able to launch the new products with 95% confidence level based on taste





For Product 1: 22.5% + 12.0% = 34.5%Error at Confidence level 90% with N = 760=1.645 * sqrt (0.35 * 0.65 / 760) = 2.8%Range : 34.5% + 2.8% = 37.3% 34.5% – 2.8% = 31.7%Range = 31.7% to 37.3%



Since the test products and the control have overlaps between their ranges, we will not be able to launch the new products even with 90% confidence level based on taste.

Taste at Confidence Level = 90%





Mouth-Feel at Confidence Level = 95%




Since the test products and the control have overlaps between their ranges, we will not be able to launch the new products with 95% confidence level based on mouth-feel.








Since the test products and the control have overlaps between their ranges, we will not be able to launch the new products even with 90% confidence level based on mouth-feel.

Mouth-Feel at Confidence Level = 90%





Cross Tab Analysis is also done on the less important attributes, i.e. Aroma, Smoothness, Flavour and Throat-Feel

Aroma, Confidence Level = 95%

Overlap, hence should not launch based on Aroma at confidence level 95%

Smoothness, Confidence Level = 95%

Overlap, hence should not launch based on Smoothness at confidence level 95%

Range For Product 1: 30.3% to 37.1%Range For Product 2: 27% to 33.6%Range For Product 3: 23.8% to 30.2%

Range For Product 1: 28.8% to 35.4%Range For Product 2: 23.9% to 30.3%Range For Product 3: 23.2% to 29.4%





Cross Tab Analysis is also done on the less important attributes, i.e. Aroma, Smoothness, Flavour and Throat-Feel

Flavour, Confidence Level = 95%

Overlap, hence should not launch based on Flavour at confidence level 95%

Throat-feel, Confidence Level = 95%

Overlap, hence should not launch based on Throat-feel at confidence level 95%

Range for Product 1:27.8 to 34.2Range for Product 2: 24.9 to 31.1Range for Product 3:24.9 to 31.1

Range for Product 1: 26.8 to 33.2Range for Product 2: 22.9 to 29.1Range for Product 3: 22.9 to 29.1

Background

Research Design

Objective

Attribute

Primary Objectiv

e Analysis

Secondary

Objective

Analysis

Summary

Conclusion

Action Item

Which are the attributes that drive overall preference of vodka?

• From R value we could interpret that there is a strong positive co-relation between dependent & independent variable.

• R-Square value I high, indicating that independent variable is able to explain larger portion of dependent variable.

• Results shows that there is no significant difference between the means of dependent & independent variables

• Observing Sig. value we could conclude that all attributes except attribute 7 (Likeability of After-taste) drive the overall performance of Vodka.





What is the extent to which they drive?

• We removed the insignificant attribute – att7• Observing Sig. value we could conclude that all the 6 attributes drive the overall performance of Vodka.

Atribute Description % ImpactQ5A_att3 LikeabilityofTaste 30.03%

Q5A_att8 LikeabilityofMouth-feel 20.62%

Q5A_att2 LikeabilityofAroma 17.43%

Q5A_att4 LikeabilityofSmoothness 11.83%

Q5A_att6 LikeabilityofThroat-feel 10.11%

Q5A_att5 LikeabilityofFlavour 8.90%

30%

21%18%

12%

10%

9%

% Impact

Q5A_att3 Q5A_att8 Q5A_att2 Q5A_att4 Q5A_att6 Q5A_att5

Background

Research Design

Objective

Attribute

Primary Objectiv

e Analysis

Secondary

Objective

Analysis

Summary

Conclusion

Action Item

Can the set of attributes be reduced to a smaller set of Factors

• Sig. (0.000) < 0.05, there is no correlation between variables and thus we can do factor analysis • KMO Sampling adequacy is .945 ( ie. >0.5), we can conclude that sample size is adequate.

• We can interpret that by having single factor, 78.3 % variance of dependent variable could be explained by independent variables






• KMO Sampling adequacy is .945 ( ie. >0.5), we can conclude that sample size is adequate.

• We can interpret that by having three factor, 88.4 % variance of dependent variable could be explained by independent variables

• In order to improve the total variance explained, we have considered number of factors as 3.






Note :- In the above matrix, we have supressed absolute value below .55.• From the above Matrix, we could conclude that attributes could be

divided based on the below three factors:-• Experience (Att3, Att5, Att7, Att8)• Senses (Att4, Att6) • Aroma (Att2)

• LikeabilityofTaste• LikeabilityofFlavour• LikeabilityofAfter-taste• LikeabilityofMouth-feel

Experience

• LikeabilityofSmoothness• LikeabilityofThroat-feel when the vodka goes down

Senses

• LikeabilityofAromaAroma





Factor Analysis

Regression Equation-Y= 0.569 * Experience + 0.499 * Senses + 0.440 * Aroma

Factor 1 – “Experience” has the highest beta value and therefore it has the highest significance on the dependent variable.

Background Objective AttributePrimary Objective Analysis



If the purchase intention is taken as a categorical variable (YES / NO), can it be predicted in future, by reading some of the ratings of the attributes only? How?

• Possible to predict the purchase intention for a case if information/ratings of the attributes are available for that case.

• Predict group membership of a case.

• Purchase intention• A predictive model built using

ratings of the attributes will classify and put the case in appropriate group.

• Two categories - Yes or No• Discriminant function

analysis.• Discriminant Analysis used

continuous variables as input and gives the output of categorical data type.

Variables that appear in Discriminant Analysis.Discriminating Variables

• Discriminates between the case based on the ratings given to the

attributes.• Dependent variable in the predictive model derived from the

discriminant analysis.• Variables used for discrimination is Q6.• “Intention to buy the vodka blend.”• Yes and No categories.

Q2A Likeability of Aroma, after pouring the neat vodkaQ3A Likeability of Aroma, after adding relevant mixer5A_Attr2 LikeabilityofAroma5A_Attr3 LikeabilityofTaste5A_Attr4 LikeabilityofSmoothness5A_Attr5 LikeabilityofFlavour5A_Attr6 LikeabilityofThroat-feel when the vodka goes down5A_Attr7 LikeabilityofAfter-taste5A_Attr8 LikeabilityofMouth-feel when the vodka is sipped

Grouping Variable

Background Objective AttributePrimary Objective Analysis



Test in SPSSTest in SPSS

Tests of Equality of Group Means

Wilks' Lambda F df1 df2 Sig.

Q2A.828 472.729 1 2278

.000 Q3A .797 579.505 1 2278 .000

Q5A_att2 .727 856.331 1 2278 .000Q5A_att3 .683 1057.337 1 2278 .000Q5A_att4 .726 860.431 1 2278 .000Q5A_att5 .733 829.545 1 2278 .000Q5A_att6 .720 883.702 1 2278 .000Q5A_att7 .742 791.519 1 2278 .000Q5A_att8 .718 896.497 1 2278 .000

EigenvaluesFunction

Eigenvalue

% of Variance

Cumulative %

Canonical Correlatio

n1 .592a 100.0 100.0 .610

Wilks' LambdaWilks' Lambda Chi-square df Sig.

.628 1057.567 9 .000

Function1

Q2A .089Q3A .110

Q5A_att2 .226

Q5A_att3

.365

Q5A_att4 .139Q5A_att5 .037Q5A_att6 .201Q5A_att7 -.056Q5A_att8 .137

Classification of casesOnce we have computed the classification scores for a case, it is easy to decide how to classify the case: in general we classify the case as belonging to the group for which it has the highest classification score. Thus, if we are to find if a person is going to buy a vodka brand, we could put in the values of variables in the classification functions to predict what each is most likely to do with a vodka blend – show intention to buy or not buy. Reliability of the predictive Model85.5% of original grouped cases correctly classified.

• Can the Current Product (Control) be replaced by any of the two Test Products?• We considered Overall Likeability as 1 attribute & compared to all three products,

we found • We could not launch the new blend with 95% confidence BUT• We could launch the product with 90% confidence

• We considered taste as 1 attribute & compared to all three products, we found• We could not launch the new blend under both the confidence level ( 90% and

95%).• We considered mouth feel as 1 attribute & compared to all three products, we

found• We could not launch the new blend under both the confidence level ( 90% and

95%).





• Which are the attributes that drive overall preference of vodka?• The Most influencing attributes that drive overall preference we found from the

study are:• Likeability of taste with 30.03% impact on overall preference• Mouth-feel with 20.62% impact • Aroma with 17.43% impact

• We found that likeability after taste is insignificant on overall preference of vodka Hence we have removed that parameter.





• Can the set of attributes be reduced to a smaller set of Factors.• We found that it can be divided into only 1 factor which defined 78.3 % variance of dependent

variable which could be explained by independent variables.• But In order to improve the total variance explained, we have considered number of factors as

3• We divided the attributes into 3 set of factors which are:

• Experience• Senses• Aroma

• If the purchase intention is taken as a categorical variable (YES / NO), can it be predicted in future, by reading some of the ratings of the attributes only? How?• We have done discriminant analysis and divided into grouping variables

• We have found that it is Possible to predict the purchase intention for a case if ratings of the attributes are available









• We wouldn’t be launching the product on 95% confidence level as the cross tab analysis show none of the two products when tested for all attributes are significantly better in Overall Likeability than the existing product.

• We could launch the product on 90% confidence level as the cross tab analysis show no overall with the two product in case of overall likeability.

• We have only considered rating 9 & 10 as they are generally acceptable by the customer.

• Even when Eigen value is less than 1 for other 2 factors, in order to improve the total variance explained ,we have taken 3 factors

• The data given is for region wise but we have not concentrated on that as we wanted to have a study of the market on a broader sense rather than changing the blend of the product region-wise

market research- 1a

Documents