- The marketing team conducted a blind taste test of Smirnoff's current vodka blend against two new test blends among regular vodka drinkers. Attribute ratings and purchase intentions were collected. - Data analysis found that taste and mouthfeel were the key drivers of overall preference. Test Blend 1 rated significantly higher than the current blend on these attributes. - Based on the findings, it is recommended that Smirnoff replace the current blend with Test Blend 1, and focus future product development on aroma, taste, and mouthfeel. Some anomalies in the data need to be investigated.

1. MARKET RESEARCH PROJECT
BLIND PRODUCT TEST
Ankush Roy
Krishna Bollojula
Shubham Sharma
Suddhasheel Bhattacharya

2. AGENDA
• Background
• Objective
• Research Design
• Data Description
• Data Analysis
• Insights from Data Analysis
• Recommendations

3. BACKGROUND
• R&D team of “SMIRNOFF” claims has prepared two new blends which they claim are
superior than the one in the market.
• Marketing team would like to do a Blind Test of the two new blends vs. the one in
the market among regular consumers of vodka to test the market acceptance.
• This test would be done among “Own” brand (Smirnoff) drinkers and important
competition brand (Fuel and Magic Moments) drinkers.
• Any of the two new blends will be considered for a change, if it comes out to be
significantly better than the current blend.

4. OBJECTIVE
Primary Objective :
• To replace the current product with any of the two test products if found
significantly(statistically) superior.
Secondary Objective :
• To understand which parameters are the key drivers for overall vodka preference
and to what extent.
• To predict the factors(by reducing attributes) which influence the preference of
vodka.
• To predict the purchase intention by evaluating the attribute ratings.

5. RESEARCH DESIGN
• Sequential monadic exposure method is used to collect responses.
• All the three blends are placed for consumption one after the other and feedback is
taken after each consumption.
• Neutralizer is given after each consumption to ensure the unbiased responses.
• The current product in the market “SMIRNOFF” is the Control Blend and the other
two blends are Test Blend1 and Test Blend2.
• Sample Size :
 Total 760 sample size which gives you 2280 data points as each respondent has given
feedback on all three products.

6. RESEARCH DESIGN(CONTD.,)
• Target Group:
Males/Females in the age group of 25 – 35 years.
Consuming vodka at least twice a week.
Regular consumer of any one of the three brands – Smirnoff, Fuel or Magic Moments.

7. DATA DESCRIPTION
• Centers : 1. Delhi 2. Mumbai 3. Kolkata 4. Bangalore 5.Chennai
• Main Brands : Magic Moments , Smirnoff , Fuel
• Age Category : 1. 25 - 30 2. 31-35
• Panel :1. Blend 1 has been placed first.
2. Blend 2 has been placed first.
3. Blend 3 has been placed first.
• Attributes rated on 10 point scale : Overall Likeability, Aroma, Taste, Smoothness, Flavor,
Throat-Feel, After-taste and Mouth-feel.
• Attributes rated on 5 point scale : Strengths of Aroma, Taste, smoothness, Flavor and After-
Taste.
• Intention to buy attribute(1-Yes, 2-No)

8. DATA ANALYSIS
• Attributes that drive overall preference of vodka blends are found by doing a
regression analysis between overall likeability and all other attributes.
OL= 0.391 + 0.07Aroma_Neat + 0.04Aroma_Mixer + 0.11Aroma + 0.29Taste + 0.11Smoothness +
0.07Flavor + 0.09ThroatFeel + 0.05AfterTaste + 0.19MouthFeel
• However, we noticed that few attributes are not contributing much to the model as
their standardized beta coefficients are very less.
• We run the step-wise regression to eliminate less contributing attributes and arrive
at the best fit model.
OL= 0.478 + 0.334Taste + 0.269MouthFeel + 0.152Aroma + 0.159Smoothness + 0.089Aroma_Neat
• We found that the Taste and MouthFeel are two important drivers for overall
preference of vodka.

9. DATA ANALYSIS
95% and 90% Confidence levels for top2(10&9) and top3(10&9&8) ratings
• At 95% Confidence level
• Top 2(10&9) Rating: We found there is no significant difference for attributes (OL, Taste
and MouthFeel) across all blends.
• Top 3(10&9&8) Rating: We found there is no significant difference for MouthFeel
attribute and Testblend1 is better than Control product for Overall Likeability and Taste.
• At 90% Confidence level
• Top 2(10&9) Rating: We found there is no significant difference for MouthFeel and Taste
attributes and Testblend1 is better than Control product for Overall Likeability.
• Top 3(10&9&8) Rating: We found there is significant difference for all attributes. This
shows that TestBlend1 is better than control product.

10. DATA ANALYSIS
• We conduct a factor analysis to reduce dimensions and arrive at more concrete
factors.
• Using PCA, we find that the first factor itself explains
more than 70% of overall variance
• Taking the first 3 factors we found that the model
explains 85.3% of variance
• We recommend not to go for factor analysis as one
factor itself explains more than 70% of overall variance.
Component
Initial Eigenvalues
Total % of Variance Cumulative %
1 6.382 70.906 70.906
2 .947 10.520 81.427
3 .345 3.835 85.261
4 .277 3.077 88.338
5 .269 2.986 91.324
6 .228 2.529 93.853
7 .217 2.409 96.262
8 .178 1.972 98.235
9 .159 1.765 100.000

11. DATA ANALYSIS
• To predict the purchase intention of vodka blends based on the ratings on different
attributes, we did discriminant analysis.
• But we found negative values and very less values for
some attributes in standardized co-efficient values
and in structure matrix.
• Now, we run the step-wise discriminant analysis to find classify better.
Standardized Canonical
Discriminant Function
Coefficients
Structure Matrix
Function Function
1 1
Q5A_att2 .337 Q5A_att3 .893
Q5A_att3 .373 Q5A_att8 .822
Q5A_att4 .139 Q5A_att6 .816
Q5A_att5 .057 Q5A_att4 .805
Q5A_att6 .212 Q5A_att2 .803
Q5A_att7 -.072 Q5A_att5 .791
Q5A_att8 .148 Q5A_att7 .772
Classification Resultsa,c
Q6_Int_p (Y=1,N=2)
Predicted Group
Membership
Total1 2
Original Count 1 519 204 723
2 125 1432 1557
% 1 71.8 28.2 100.0
2 8.0 92.0 100.0
a. 85.6% of original grouped cases correctly classified.

12. DATA ANALYSIS
• In step-wise discriminant analysis, flavor and after taste attributes are removed and
we got 0.1% increase in predictability.
• If we add the Arom_Neat and Aroma_Mixer, the
overall classified levels are getting down(0.2%) and
their standardized canonical discriminant function co-efficients are also.
• Hence, we don’t include Aroma_Neat and Aroma_Mixer attributes.
Classification Resultsa,c
Q6_Int_p (Y=1,N=2)
Predicted Group
Membership
Total1 2
Original Count 1 515 208 723
2 119 1438 1557
% 1 71.2 28.8 100.0
2 7.6 92.4 100.0
a. 85.7% of original grouped cases correctly classified.
Structure Matrix
Standardized Canonical
Discriminant Function
Coefficients
Function Function
1 1
Q5A_att3 .894 Q5A_att2 .344
Q5A_att8 .823 Q5A_att3 .374
Q5A_att6 .817 Q5A_att4 .137
Q5A_att4 .806 Q5A_att6 .205
Q5A_att2 .804 Q5A_att8 .134
Q5A_att7
a
.793
Q5A_att5
a
.773

13. DATA ANALYSIS
• Additionally, we did cross tabulations and chi-square test of independence between
purchase intention and strength attributes(3-Just right).
• We found the following insights,
• Despite giving the just right rating on all strength attributes, majority of respondents
chose Not-to-Buy.
Purchase Intention Y=1 N=2
Aroma-Strength 195(25.7%) 182(23.9%)
Taste-Strength 194(25.5%) 190(25%)
Smoothness-Strength 189(24.9%) 169(22.2%)
Flavor-Strength 183(24.1%) 193(25.4%)
AfterTaste-Strength 184(24.2%) 193(25.4%)
Purchase Intention Y=1 N=2
Aroma-Strength 162(21.3%) 178(23.4%)
Taste-Strength 151(19.9%) 164(21.6%)
Smoothness-Strength 149(19.6%) 180(23.7%)
Flavor-Strength 156(20.5%) 155(20.4%)
AfterTaste-Strength 146(19.2%) 183(24.1%)
Test Blend1 Test Blend2

14. DATA ANALYSIS
• To find out the reason behind this anomaly, we did a cross tabulation between Main
brand and strength attributes.
• But, we observed that Number of respondents saying Yes and No to purchase the
new blends are both have Smirnoff as main brand.
Main Brand Magic
Moments
Smirnoff Fuel
Aroma-Strength 11.3% 27.0% 8.3%
Taste-Strength 11.1% 26.7% 8.1%
Smoothness-Strength 11.8% 26.6% 7.1%
Flavor-Strength 11.3% 26.5% 7.4%
AfterTaste-Strength 11.6% 26.7% 8%
Q6_Int_p (Y=1,N=2) * MAIN_BRND Crosstabulation
MAIN_BRND
Total1 2 3
Q6_Int_p
(Y=1,N=2)
1 Count 138 473 112 723
% of Total 6.1% 20.7% 4.9% 31.7%
2 Count 381 946 230 1557
% of Total 16.7% 41.5% 10.1% 68.3%
Total Count 519 1419 342 2280
% of Total 22.8% 62.2% 15.0% 100.0%

15. DATA ANALYSIS
• Additionally, we split the whole dataset based on categories such as Centre's, Panel,
Ages and main brand.
• If we split dataset based on Centre's, the sample size is getting very low and error
levels are getting very high in the model.
• We found the overall likeability is driven by factors as follows.
• Across Ages: Taste, Aroma, MouthFeel
• Across Main brand:
• Main Brand1 & Main Brand2 – Taste, Aroma, Mouth Feel
• Main Brand3 – Taste, ThroatFeel and MouthFeel
• Across Panels:
• Panel1 and Panel2 – Taste, Aroma, MouthFeel
• Panel 3 – Taste, Smoothness, MouthFeel

16. DATA ANALYSIS
• We have done cross tabulation and chi-square test between Rating of an
attribute(available) to the strength of that attribute and found they are associated.
• We have done cross tab and chi-square for ages and purchase intention, panel and
purchase intention, but we didn’t find any association.
• As proportion of Smirnoff drinkers in the population are high, we can normalize and
find which factors are driving their likeability.
• We observed that even the total number of respondents are 760, we found that last
respondent number in the dataset is 804.(Just an observation )

17. RECOMMENDATIONS
• The company should go ahead with the replacement of the current blend with the
new blend, Test product 1 as it ranks consistently higher in all attributes at 90 %
C.I.
• Further thrust areas for product development should be on:
Aroma
Taste
Mouth - feel
As they are the common attributes in overall likeability and purchase intention

18. ACTION AREAS
• The response anomaly(difference in no of response and respondent number) should
be looked into- Could be due to Missing data-points.
• Robustness and validity of scales should be checked- we observed anomalies with
respect to Smirnoff users and their purchase intentions.

19. THANK YOU