ammr project report group5

8/13/2019 AMMR Project Report Group5

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Group-5Advance Methods forMarketing Research

Effect of store layout on sales of a product

Submitted by:Debolin Dey PGP/16/015

Sudheer Guntupalli PGP/16/018Shashank Shekhar PGP/16/045Shuchi Garg PGP/16/047Suraj Dash PGP/16/173Mayank Bharti PGP/16/211

2013

Project ReportSubject: Advanced Methods for Marketing ResearchTerm: IVFaculty: Atanu Adhikari


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Abstract

The objective of this project is to analyze the effect on sales of a product in a store for certain

different store layout patterns. This includes advertising in store, shelf display and product

placement in store. The formulated hypotheses are tested with the real time primary data

collected in such a simulated store organized for project purpose. The analysis is done by

marketing research software namely SPSS and result section encompasses the observed result

of this project (the exhibits for analysis & results are attached under various section)

Introduction

In store display is a mechanism used by most companies to induce brand preference,mechanisms like purchasing shelf space, shelf displays, corner standees are used, the money

spent on these efforts constitutes almost 1% of the total store sales and through our project we

are trying to measure the effectiveness and value of these advertising efforts.

Below are the images that show different techniques of in store display of product and Shelf

advertisement.

These selling in store techniques are widely used today in modern retail outlets. The objective

of the vendor is to promote certain products for sale by using these techniques. These impact

the affective, selective and point of sale behaviour of a shopper. These results in influenced

decision making by the customer and is directly aimed at profitability of the sale. The brands

of different product category are employing these techniques in retail stores to increase the

sales of their product. They often pay a fee to the retail shop owner in doing so.


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For the purpose of this study, a store is simulated in the campus and these different

techniques were employed and kept under observation for a week each to capture the change

in buying behaviour and decision making process of a customer.

Literature ReviewWith the changing market scenario, the function of a retail outlet has changed from just being

a place for buyers and sellers to meet to a product and brand showroom. The situation of the

retailers has worsened due to the following reasons [1]

The situation is similar in the Indian Market.

1. Consumer spending is being squeezed due to the increase in the number of products

available in the market and in the needs of the consumer.


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Second, quantities sold often respond at least as much to changes in relative locations (in-

store displays) as to changes in relative prices. Stigler (1961)s costly search paradigm

provides a natural framework within which to analyze both effects: the week-to-week price

churn induced by sales creates a natural economic motive for search, and the presence of

search in turn helps to explain observed display effects. This work is most closely related to

two existing studies in industrial organization. First, using panel data on laundry detergent

purchases, Hendel and Nevo (2006) estimate a discrete-choice demand model incorporating

dynamic responses to sales, finding that intertemporal substitution is an important avenue by

which consumers respond to sales. Second, Goeree (2008) develops a model purchase under

limited information in differentiated-product markets.

Price variation offered to consumers in the form of discounts is temporary and most products

have a regular price around which sales take place. As the week -to- week changes dont

change, discounts help in evaluating the price elasticity of the consumers. As can be seen in

the graph below, there is a regular price around which most sales happen and discounts are

temporary and dont last for too long. But analysis does state that discounts definitely

increase the volume sale of the product and make consumers look for alternatives beyond

their regular brands.

The analysis done previously have been recorded in Table 3. This analysis was done for the

detergent powders market. Several key patterns in Table 3 suggest the presence of search

effects. First, as expected, display and feature effects are large and significant in every

regression where they appear. Second, the magnitude of the coefficient on discount falls

consistently as promotional covariates are added: nearly two-thirds from Column (1) to

Column (4). Finally, displays and features strongly increase the quantity effects of price


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reductions. This can be seen most clearly from Column (4), which suggests that displayed

and featured price reductions have

quantity effects roughly three times larger than those not advertised. This last finding in

particular strongly supports the idea that promotional activities convey price information. [2]

The function of displays is to highlight specific products by adding signals or marks (e.g.,

tags), changing the presentation layout (e.g., rack arrangement), or presenting the product in a

different, often more isolated area of the shelf or store (e.g., end-of-aisle displays). According

to the papers by Babin and Darden 1995; Donovan and Rossiter 1982, these changes in the

store environment affect the psychology of the consumer by attracting attention and

stimulating exploratory behavior. Inman, McAlister, and Hoyer 1990 say that many

customers seem to interpret ISD as signals or cues of a good deal. As the kind of product

stored and sold in a shop directly link to the image of the shop, consumers have some faith on

the brands whose display has been put up in the store. In low involvement, repeat buying

situations, such as grocery purchases, th ese cues tend to increase a displayed products

purchase probability, because customers do not want to go through a complete search and

evaluation procedure but instead prefer to settle for satisfying outcomes obtained with

minimum effort (Hoyer and MacInnis 2010). [3] Due to time constraints and low involvement

in the product (especially for FMCG market) consumers experience the need to simplify their

decision process using choice heuristics or cues (Hoyer and MacInnis 2010). The extent to


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which they rely on ISD as a choice tactic or react to them also depends on their sensitivity to

store environment influences and their willingness to change purchase plans. Based on the -

regulation tendency and sensitivity to environmental stimuli of the consumers, previous

research have distinguished two groups of consumers Action-oriented consumer and the

State-oriented consumer. Action-oriented consumers plan their behavior in advance and do

not change their plans based on external stimuli e.g. economic gain, displays etc. They are

guided more by intrinsic goals and less prone to emotional and environmental influences.

State-oriented consumers do not plan their behavior in advance and hence this group is most

affected by external stimuli. They are guided by social and emotional aspects rather than

intrinsic goals. They often decide on the spot, engage in exploratory behavior e.g try new

brands and change their purchase plans in reaction to environmental incentives (Babin and

Darden 1995).

There are three kinds of displays (a) Entrance (b) aisle (c) shelf tags. The three locations

represent different shopping zones and have distinct functions, such as zones used for

traveling (store and aisle entrances) and zones used more for shopping (shelf area within the

aisle) (East et al. 2003; Larson, Bradlow, and Fader 2005). The display location have

different effects on the consumers in order of appearance: Entrance displays typically are

encountered first and are larger and more exclusive, followed by aisle displays which are

comparatively small and a little cluttered shelf tag displays are small and are presented

simultaneously with similar displays of several other brands.

Researching deep into the topic we have summarized a few more past researches that have

happened on the topics related to ours. One of the researches explains the relationship

between visual merchandising elements and consumer affective response.

Design/methodology/approach followed for this research was that different perspective on

visual merchandising is offered through the different types of intimate apparel retailers

varying from fashion-oriented, mass market-oriented to fashion forward. Chosen sample were

of ages of 25 and 35. Virtual merchandising (VM) refers to the design of a retail environment

is related to the store atmosphere creation. It is important issue in order to get the desired

affective response of consumers and aims to enhance purchase probability. In real practice

the fastest moving SKUs and the ones that earn higher margins are under focus while

designing the VM of a store. VM can affect the affective perception of consumers on the

retail environment by selective attention. Customers have two points of view when evaluating

VM in stores, which include utilitarian and hedonic aspects. Former relates to the actualneeds of consumers and the later relates to the hedonic aspect. Display elements, such as


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techniques to for identifying the decision-making processes involved in purchases. The

research compares the buying behaviour results of a concept test conducted using a

traditional methodology with those obtained using a virtual store. Below are the different

schemes of virtual store and a real store.

The hypothesis is that a purchase is the result of an attitude toward a product and a behavioral

process. It depends of (i) affect & (ii) attitude & emotion. The objective is to compare the

results of buying behavior in virtual store with the physical stores response. Observe the

buying pattern and as per the response replicate the virtual shopping model in physical store

as per the behaviour for better sales. The research data was collected the same way in

experimental real stores and in a virtual one in order to allow comparisons. Behavioural

measures are observed in both the experimental real store and the virtual one. Time spent at

shelf was recorded and the purchase rate determined by of the sample shoppers who bought

at least one product. The buying behaviour at the presentation mode on virtual store is close

as per to the reality level of the display, some attitudinal and behavioral indicators may vary

as virtual one cant c apture the behavioral essence regarding the feel of touching the product.

Research result concludes that conclude that virtual stores can be used to test and capture

innovative buying behavior concepts for a certain product. However, they should not be used

as a basis for decisions for your store layout/display management. Limitation is that the real

experimental store cant exactly be replicated as virtual store, the time spend to buy at virtual

store is less than the time spend at real store. For future research adding a comparison of wall

screen shelf and on line shelf could yield interesting results. Such studies could also be

pursued in other distribution channels, such as specialized distribution.

Methodology


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The approach that we pursued is two pronged. The first phase was to put up the items at

different levels in HO and ask people to rate the product preference. This way we can

understand brand bias.

Week 2: The next week we put up banners for the reputed brands and put them in the lower

stands and checked the preference.

Week 3: The next week we put up discounts for the lesser brands and tried to see the change

of buyer perception after discounts.

Week 4: We asked people to rate the similarity of various brands so that we could perform

multi dimension scaling to find out to see how products fall on a map.

We attempted to see if people were more influenced by the brand or discount when choosing

between similar products.

Analysis & Results

Discriminant Analysis

This tests helps us in finding out the linear combinations of the independent variables which

will best best discriminate between the categories of the dependent variable (groups). Wewould also like to examine whether significant differences exist among the groups, in terms

of the independent variables. Here we would also try and find out which variable leads to

most of the inter group differences. We would classify cases to one of the groups based on

the values of the independent variable and evaluate the accuracy of the classification.

First of all, we would look at the effect of variables like brand, location and advertisement on

the purchase intention without ads of discounts and which variable has the highest effect.

Secondly, we would look at the effect on purchase intention of individuals having considered,

discount ads as well along with the above variables and try and find out which variable has

the highest effect on purchase intention after the discount ads have been floated.

Without Discount Ads

Test Results

Box's M 13.114


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F Approx. 2.038

df1 6

df2 7.537E3

Sig. .057

Tests null hypothesis of

equal population

covariance matrices.

The Boxs M test showing insignificant means that the null hypothesis that the intra group

variances being equal is not rejected thus, our groups are homogenous within.

Eigenvalues

Functi

on Eigenvalue

% of

Variance

Cumulative

%

Canonical

Correlation

1 1.940 a 100.0 100.0 .812

a. First 1 canonical discriminant functions were used in

the analysis.

The eigenvalue is the ratio between Between group variance and within group variance. It

should be greater than 1 and here it is 1.94 as then it means that the between group variance

is higher than within group variance. Thus, our groups are heterogenous across and

homogenous within.

Wilks' Lambda

Test

of

Functi

on(s)

Wilks'

Lambda Chi-square df Sig.

1 .340 67.397 3 .000


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Wilks Lambda shows the ratio between within group variance and Total Variance. It should

be lesser than 0.4 and we have it as 0.34. This shows that the groups of purchase and not

purchase were highly homogenous within that is they had very less within group variance.

Standardized

Canonical

Discriminant

Function Coefficients

Function1

Brand .955

Location .188

advert .123

The above table shows that Brand discriminates more in segregating the two groups than the

other variables as it has the highest Standardized Discriminant Function Coefficient.

Classification Results b,c

Buyin

g

Prefer

ence

Predicted Group

Membership

Total0 1

Original Count 0 17 2 19

1 5 42 47

% 0 89.5 10.5 100.0

1 10.6 89.4 100.0

Cross-validated a Count 0 17 2 19

1 6 41 47

% 0 89.5 10.5 100.0

1 12.8 87.2 100.0


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The

discriminant function correctly classifies 89.4% of originally grouped cases in their specific

group that is whether they are in the purchase or not purchase category.

In case of one out cross validation, the discriminant function correctly measures 87.9% of

Cross validated grouped cases which is high enough to be sure of the validity of the

discriminant function as most of the cases it correctly predicts whether the respondents are in

the purchase group or in the not purchase group.

With Discount Ads

Here after having tested the respondents without discount ads, we now try to find their

responses after having put up discount ads and check which Independent variable has the

most discriminating power like the brand in the above case.

Eigenvalues

Functi

on Eigenvalue

% of

Variance

Cumulative

%

Canonical

Correlation

1 1.510 a 100.0 100.0 .776

a. First 1 canonical discriminant functions were used in

the analysis.

a. Cross validation is done only for those cases in the analysis. In cross

validation, each case is classified by the functions derived from all

cases other than that case.

b. 89.4% of original grouped cases correctly classified.C. 87.9% of cross-validated grouped cases correctly

classified.


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Eigenvalues

Functi

on Eigenvalue

% of

Variance

Cumulative

%

Canonical

Correlation

1 1.510 a 100.0 100.0 .776

An eigenvalue higher than 1(1.510) shows that the groups have

been clearly discriminated and the groups are heterogeneous

across.

Wilks' Lambda

Test

of

Functi

on(s)

Wilks'

Lambda Chi-square df Sig.

1 .398 57.063 4 .000

Wilks Lambda


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The above table shows discount advertising to be the second most effective independent

variable in discriminating between the groups. Due to it, the preference towards brand has

come down drastically from .955 to .773 and discount advertising also has a substantial value

showing its effect to be highly effective in discriminating between the two groups.

Classification Results ,c

Buyin

g

Prefer

ence

Predicted Group

Membership

Total0 1

Original Count 0 8 2 10

1 3 53 56

% 0 80.0 20.0 100.0

1 5.4 94.6 100.0

Cross-validated a Count 0 8 2 10

1 4 52 56

% 0 80.0 20.0 100.0

1 7.1 92.9 100.0

a. Cross validation is done only for those cases in the analysis. In cross

validation, each case is classified by the functions derived from all

cases other than that case.

b. 92.4% of original grouped cases correctly classified.

c. 90.9% of cross-validated grouped cases correctly

classified.

Here, in the original grouped cases 92.4% cases are correctly classified showing a high

efficiency level of the discriminant function. Whereas in the cross validated case as well, the

capability of correctly classifying the grouped cases is still very high at 90.9%.


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Multi-Dimensional Scaling: Dove, Park Avenue and Pears

The experiment was conducted with the three brands Dove, Park Avenue and Pears. Before

the experiment was conducted and after the experiment was over, Multi-Dimensional scaling

was conducted to know the change in the position of these three brands in the minds of the

consumer just because of the promotional offer on Dove. But, as we are dealing with only

three brands, multi-dimensional scaling in SPSS is not possible to perform, since the

minimum number of variables required for performing Multi-dimensional scaling in SPSS is

5. Hence, a couple of dummy brands vivel and lux were taken to make the variables under

consideration as 5. Responses were taken from the respondent prior to the experiment and

post the experiment. Twelve responses were taken using aggregate level analysis under the

assumption that all respondents used the same dimensions to evaluate the brands, but theweights of the dimensions might be different. It is a direct approach multi-dimensional

scaling in the sense that the set of attributes were not identified by the research team.

Scale:

Dissimilarity data is taken on a continuous scale there by allowing decimal ratings. A rating

of zero means very similar and a rating of ten means very dissimilar.

Perception post the experiment (Vivel and Lux being dummy brands)


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Results:

Final stress value obtained after five iterations is 0.02298 which shows that the model fit is

somewhere between perfect and excellent. A closer look at the scatter plots of the subjects

reveal that almost all of them had similar perception about each of these brands after the

experiment has taken place. Though Pears and Park Avenue appear to be closer to each other

in the perceptions of the people after the experiment, both of them are in different quadrants

on the perception map. Dove is located diagonally opposite to both of these brands after the

experiment which shows that in the minds of the people the perception about Dove is very

much different from the brands pears and Park Avenue.

Stepwise Regression:

We did stepwise regression to see the effect that each of our three factors location,

advertisements and brand had on purchase intent. We did a similar research post discount to

see the role that discount plays in purchase intent.

Pre discount:


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Interestingly SPSS did not need the location factor to create the model implying that the

additional r square that location provides to the regression model is minimal.

We see that regression model is significant and the collinearity diagnostics are appropriate.

(VIF


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Here we see again that the statistics are significant and VIF is within limits. Hence the model

is appropriate.

So we see that when in the second model both advertisements and discounts exist people

prefer going for the discounted product and the importance of the advertisement fades away

as shown by the lack of need of the advertisement factor in the model.

Logistic Regression

From the MDS analysis it was found that Pears and Park avenue occupies the same space in

the minds of the consumer and thus we decided that we could observe the effects of the brand

preference over advertisement for theses two brands in the store. So firstly we put an ad of

Park avenue describing product benefits over Pears and also how it was more helpful in

fighting germs and maintaining clean For this we asked the consumer two questions

If they wanted to buy Pears over Park Avenue

The questioner made a tick against the question of whether the consumer read the

advertisement if the customer spent an amount of time in observing the advertisement


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The experiment was conducted with 30 participants and a logit analysis was performed on the

responses

The output of the logit analysis is shown below

From the shown values we can see that the earlier the correct prediction of the percentage of

people who were buying and not buying when the intercept was entered was 53.3% which

increased to a 93.3% correct prediction when the variables regarding the advertisement and

preference of one soap over other were included


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This shows the coefficient of the equation for the determination of purchase behavior.

Discussion

Through the Logistic Regression, we saw whether the consumer was looking at the

advertisement and whether he/she is preferring a particular brand or not. On doing the

Logistic Regression, we found that advertisement had a higher impact on buying behaviour

than brand preference.

In case of Discriminant Analysis, we found that when ads were not put up then Brand was the

biggest discriminating factor between purchase and not purchase groups. When weintroduced the discount ads, the brand preference came down drastically and discount ads

proved to be a close second to brand preference (.773- brand vs .629- discount ads). Thus,

showing that discount ad is a potent factor behind discriminating between the two dependent

groups of buying and not buying a product.

Limitations & Future Research

The study we performed suffers from certain limitations like

The study was performed with a sample of students and some staff from High

Octane and the sample was collected with convenience and also had a very narrow

range

The number of products collected and studied were very small and few due to

budget constraints, the preference of advertisements should thus be seen on a greater

number


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No high end products were included which generally have a very loyal customer

base

The study could have also taken into account consumer behaviour in larger stores

with different ambience and advertising techniques like reliance fresh but due to

logistic difficulties in handling, we had to conduct the study in a simulated store

Bibliography

Testing FMCG innovations: Experimental real store versus virtual Journal of Product &

Brand Management by Etienne Bressoud

How does visual merchandising affect consumer affective response? by Derry Law, reach

him [email protected]

Impacts of in-store manufacturer brand expression on perceived value, relationship quality

and attitudinal loyalty by Philippe Aurier and Gilles Sere de Lanauze Montpellier 2

University MRM, Montpellier, France

2012 SHOPPER ENGAGEMENT STUDY a MEDIA TOPLINE REPORT Displays, Sales, and In-Store Search in Retail Market by Matthew Gentryy Effectiveness of in-store displays in a virtual store environment by Els Breugelmans and

Katia Campo, DEPARTMENT OF MARKETING AND ORGANISATION STUDIES

(MO)

ammr project report group5

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