Blind Product Test on new vodka belnds

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 .337Q5A_att3 .893
Q5A_att3 .373 Q5A_att8 .822
Q5A_att4 .139 Q5A_att6 .816
Q5A_att5 .057 Q5A_att4 .805
Q5A_att6 .212Q5A_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