OBJECTIVE The Objective of the project is to study the preferences and perceptions of the consumers towards the various Chips Brands in the market. We conducted a survey to identify the number and nature of Dimensions consumers use to perceive different brands in the marketplace. The Positioning of current brands on these dimensions and the gaps in the market will help a new Chips brand to fit in the space where there is a gap.

1. Group 10
UM19137- Ameeya Kumar
UM19144- Anant Tomar
UM19148- Biswadeep Ghosh Hazra
UM19160- Karthik J
UM19177- Rohan Bhadury
MARKETING RESEARCH
PROJECT
TO STUDY THE PREFERENCES AND
PERCEPTIONS OF THE CONSUMERS
TOWARDS THE DIFFERENT CHIPS
BRANDS IN THE MARKET

2. 1
CONTENTS
 OBJECTIVE
 RESEARCH METHODOLOGY
 MULTI-DIMENSION SCALING
 ALSCAL
 MULTI-DIMENSION SCALING ANALYSIS -
INTERPRETATION
 INFERENCES AND SUGGESTIONS

3. 2
OBJECTIVE
The Objective of the project is to study the preferences and perceptions of the consumers
towards the various Chips Brands in the market.
We conducted a survey to identify the number and nature of Dimensions consumers use to
perceive different brands in the marketplace. The Positioning of current brands on these
dimensions and the gaps in the market will help a new Chips brand to fit in the space where
there is a gap.
RESEARCH METHODOLOGY
1. Preparation of questionnaire to collect Perception Data:
 Direct Approach: To obtain the input perception data we have used direct
approach where the respondents were asked to judge how similar or dissimilar
the various brands are using their own criteria.
Respondents were required to rate all possible pairs of brands on a Likert Scale.
The number of pairs evaluated were 45.
 Derived Approach: The Respondents were asked to to rate the brands on the
identified attributes using Likert scale.
A questionnaire was circulated and the total number of questions in the questionnaire
were 5. Total number of responses collected were 50.
2. Analysis of Responses: The data collected through the survey method was
analysed using Multi- Dimensional Scaling in SPSS.
3. Recording the findings and analysis: Perceived relationships among stimuli are
represented as Spatial Maps to understand the behaviour and preferences of consumer
towards chips brands in the market.
MULTI-DIMENSION SCALING
Multidimensional scaling (MDS) is a technique that represents perceptions and preferences of
respondents by the means of visual display.
It is used in marketing research to identify:
1. The number and Nature of dimensions consumers use to perceive different brands in
the market place.
2. The positioning of current brands on these dimensions.
3. The positioning of the consumer’s Ideal brand on these dimensions.

4. 3
ALSCAL
Multidimensional scaling can be carried out for elements and constructs separately using
ALSCAL in SPSS. ALSCAL operates and uses a alternating least-squares algorithm to
perform MDS.
ALSCAL produces a two-dimensional nonmetric Euclidean multidimensional scaling
solution. Input is assumed to be one or more square symmetric matrices.
MULTI-DIMENSION SCALING ANALYSIS – INTERPRETATION:
ALSCAL ANALYSIS
We have done an Alternate Least Square Algorithm Output (Alscal) where we found out that
when we took one dimension, the stress was more than 0.15 (0.45654) and R2 was less than
0.6 (0.3010) and when we took two dimensions, stress was 0.252 and R2 was 0.577. So we
added two more dimensions to our analysis and then we got stress as 0.09504 and R2=
0.84622. So we took four dimensions. The results we obtained from the analysis were:
Stimulus Name 1 2 3 4
Uncle Chipps -1.855 -0.3797 -0.5855 0.4222
Lays 0.5403 -1.9535 -0.2203 -1.1791
Bingo 0.305 -1.3969 1.807 0.1126
Parle 0.4049 1.3298 -0.5904 -0.9287
Doritos 0.9183 -0.2773 -1.0938 1.2804
Kurkure -0.9422 0.0629 -1.2008 -0.9325
Pringles 1.4639 1.4846 1.1685 -0.5822
HALDIRAM CHIPS 1.2167 0.0536 -0.7257 1.2192
Crax -1.6859 0.4161 0.5404 -0.5694
Diamond chips -1.366 0.6602 0.9005 1.1575

5. 4
So after the analysis of the stimuli and the derived stimulus configuration and the Inversed
Matrix (High score-high relatability) we came to the conclusion that dimension 1 can be
mostly related with price worthiness as it is the closest fit amongst all other attributes and
dimension 2,3 and 4 can be the combination of all other attribute i.e. Taste, Quantity,
Packaging, Brand Recognition in choosing our new product.

6. 5
INFERENCESAND SUGGESTIONS
Dimension 1 vs 2
Dimension 1vs2
[CELLRANGE], -
0.3797
[CELLRANGE], -
1.9535
[CELLRANGE], -
1.3969
[CELLRANGE],
1.3298
[CELLRANGE], -
0.2773
[CELLRANGE],
0.0629
[CELLRANGE],
1.4846
[CELLRANGE],
0.0536
[CELLRANGE],
0.4161
[CELLRANGE],
0.6602
-2.5
-2
-1.5
-1
-0.5
0
0.5
1
1.5
2
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
Chart Title
[CELLRANGE], -
0.5855
[CELLRANGE], -
0.2203
[CELLRANGE],
1.807
[CELLRANGE], -
0.5904
[CELLRANGE], -
1.0938
[CELLRANGE], -
1.2008
[CELLRANGE],
1.1685
[CELLRANGE], -
0.7257
[CELLRANGE],
0.5404
[CELLRANGE],
0.9005
-1.5
-1
-0.5
0
0.5
1
1.5
2
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
Chart Title

7. 6
Dimension 1 vs 3
After the analysis using MDS we came to the conclusion that the gap lies in the first
scatter plot in the third quadrant. (which implies that the new product should be priced
in that quadrant).
So, a new product can be introduced in that segment to reap the benefits of the gap
analysis done by us and the resulting product placement can be beneficial.
Online Link to Questionnaire: https://forms.gle/Dakuheay8R3BFB6EA
[CELLRANGE],
0.4222
[CELLRANGE], -
1.1791
[CELLRANGE],
0.1126
[CELLRANGE], -
0.9287
[CELLRANGE],
1.2804
[CELLRANGE], -
0.9325
[CELLRANGE], -
0.5822
[CELLRANGE],
1.2192
[CELLRANGE], -
0.5694
[CELLRANGE],
1.1575
-1.5
-1
-0.5
0
0.5
1
1.5
-2 -1.5 -1 -0.5 0 0.5 1 1.5 2
Chart Title