blind product test - data analysis
TRANSCRIPT
MARKET RESEARCH PROJECTBLIND PRODUCT TEST
Ankush Roy
Krishna Bollojula
Shubham Sharma
Suddhasheel Bhattacharya
AGENDA
• Background
• Objective
• Research Design
• Data Description
• Data Analysis
• Insights from Data Analysis
• Recommendations
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.
OBJECTIVEPrimary 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.
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.
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.
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)
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.
DATA ANALYSIS95% 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.
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
VarianceCumulative
%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
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 1Q5A_att2 .337Q5A_att3 .893
Q5A_att3 .373Q5A_att8 .822
Q5A_att4 .139Q5A_att6 .816
Q5A_att5 .057Q5A_att4 .805
Q5A_att6 .212Q5A_att2 .803
Q5A_att7 -.072Q5A_att5 .791
Q5A_att8 .148Q5A_att7 .772
Classification Resultsa,c
Q6_Int_p (Y=1,N=2)
Predicted Group Membership
Total1 2Original Count 1 519 204 723
2 125 1432 1557% 1 71.8 28.2 100.0
2 8.0 92.0 100.0a. 85.6% of original grouped cases correctly
classified.
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 2Original Count 1 515 208 723
2 119 1438 1557% 1 71.2 28.8 100.0
2 7.6 92.4 100.0a. 85.7% of original grouped cases correctly
classified.
Structure MatrixStandardized Canonical Discriminant Function
Coefficients
Function
Function
1 1Q5A_att3 .894Q5A_att2 .344
Q5A_att8 .823Q5A_att3 .374
Q5A_att6 .817Q5A_att4 .137
Q5A_att4 .806Q5A_att6 .205
Q5A_att2 .804Q5A_att8 .134
Q5A_att7a.793
Q5A_att5a.773
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
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 MagicMoments
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 3Q6_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%
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
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 )
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
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.