A statistical study on the analysis of consumer buying behavior, impacted due to the presence of Malls and Marketplaces.

To understand the impact of malls on consumer buying behaviour:

 

The consumer buying behaviour is a complex structure, where the perception of the consumer towards the factors of decision making process plays a vital role.

An effort, to understand the perception of malls on consumers mind was brought about through few specific questions in questionnaire, which are as listed below:

 

Computation and analysis:

 

To have a general understanding of the trend exhibited by the collected data, the bar graph has been plotted using the raw data for the responses of the above specified questions.

 

The forward regression analysis method was used to establish the relationship between the mall preference and other factors which may affect the consumer buying behaviour, to name few: Information, Money, Perception etc…

OpenStat software was used to analyse the data.

The statistical inferences achieved are as below:

1. The adjusted R2 was found to be 1.

2. The standard error was 0.90

3. The prob>t was found to be 0 for all the independent variables.

 

The snapshot of the output explains the overall fit:

 

Thus, the final, multiple regression linear equation to be:

Pref_Mall = -0.362 + 0.82 * Visit_Yes + 1 * Pref_Mkt

 

Where,

Pref_Mall = preference of the customer towards a mall.

Visit_Yes = will the customer visit the mall, before making any buying decision.

Pref_Mkt = affinity of the customer towards the marketplace.

 

Thus, the complex buying behaviour of the customer depends on the various attributes present in the environment and most of all, the perception of a consumer towards the factors plays a vital role in buying decision.

From the model explained above, we can also infer that:

1. Consumer has negative perception about his preference towards a mall.

2. The consumer does visit the mall to collect information about the product he is willing to buy.

——————————————

 

New Service Introduction:

 

The new product is termed as “Shopping-Mall Cart”; the cart can be used just like any cart which are seen in retail stores like “Big-Bazaar, Tesco, and Star-Bazaar etc…” the carts in retail stores are being used to collect the items by the customers and move to billing counter to get the items billed. Similarly, the “Shopping-Mall Cart” will be in dispense to the customers at the time they enter into the mall and the same cart can be used to carry their items of purchases as well as the items they are going to purchase in any of the retail stores inside the mall. The “shopping-Mall Cart” has the following advantages:

1. The customer need not deposit their belongings in every retail stores while entering the stores.

2. The customer need not carry their belongings in their hands while they are walk in the mall.

The conjoint technique was used to understand the feasibility for introducing a new product to provide service to customers who visit the mall.

Conjoint Approach:

 

The customers of the mall were interviewed to rank their preferences to a mall based on the attributes listed below:

1. Parking Space and

2. Shopping Mall Cart.

The below shown question was used to understand the preference of the customer for the above listed attributes.

The above question is designed based on ‘Pairwise Approach’ / ‘Two – Factor Approach’. The attributes in evaluation has (3 level) X (2 level) = 6 profiles. The 3 levels are constituted from attribute “Parking Availability”, the 2 level is constituted from “Shopping- Mall Cart” attribute.

The statistical inferences after doing conjoint analysis are as below:

 

Inferences:

1. The coefficient of determination, or r2, is 0.930032285 which would indicate a strong relationship between the independent and dependent variables.

2. The critical level of F is 0.09803453. Since F = 1.37314E+31 is much higher, it is extremely unlikely that an F value this high occurred by chance. The hypothesis that there is no relationship between independent and dependent variables is to be rejected.

3. The regression equation will be:

Thus, the introduction of new service “Shopping-Mall Cart” in malls will increase the consumer preference towards such malls.

——————————————-

 

Comparison between Mall and Marketplace:

To understand the consumer orientation towards the mall or the marketplace, the following attributes were considered:

1. Distance of mall and marketplace from consumer’s residence.

2. Number of monthly visits made by consumers to mall and marketplace.

3. Approximate money spent in each of the places.

4. Time spent by consumers in each of the places.

5. Few of the personal information.

Analysis:

 

The analysis of the surveyed data was done purely by using descriptive analysis – Frequency distribution analysis.

The surveyed data was scaled to interpret the data, the scales are as described below:

1. For the data related to Distance between mall / marketplace, Staple scale was used. The scale being, [ 2 , 1, -1, -2] for distances [ less than 2km, 2km to 5km, 5km to 10km, more than 10km].

 

The reason for selecting such weights being, nearer the mall / marketplace, its more convenient for the consumers to visit such places.

 

2. For the data related to Number of Visits, Staple scale was used. The scale being [ -2, -1, 1, 2] for the Number of Visits [ 0, 1 to 5, 6 to 10, more than 10].

 

The reason for selecting such weights being, more the visits more likely the customer is going to spend / gets acquainted with such places.

 

3. For the data related to Money Spent in previous visits, Semantic Differential scale was used. The scale being, [ -2, -1, 0, 1, 2] for spending [0rs, less than 500rs, 500rs to 1000rs, 1000rs to 5000rs, more than 5000rs].

 

The reason for selecting such weights being, if the customer spends no money, the malls/marketplace will not be able to earn any revenue, in such a case, it suffers loss. If the customers spend less than 500rs, the mall may not make any profits when compared to investments it has made. If the customer spends between 500rs to 1000rs, the spending may meet the necessary expenses of the mall but, need not make any profits. Contrarily, if the customer spends anything more than 1000, the malls may earn some profits.

1. For the data related to time spent, Ranking / Ordinal scale is being used. The scale being, [ 4, 3, 2, 1] for the time spending [ less than an hour, 1hr to 3hrs, 3hrs to 5hrs, more than 5hrs].

 

The reason being, if the customers take less time to make any buying decisions and leave the mall/marketplace, allows the mall/marketplace to establish more Footfalls, thereby, creating new opportunities.

The following questions from questionnaire were used to do a comparative study between the mall and marketplace:

 

The following were the inferences made after analyzing the data:

 

Thus, if a retail stores owner has to make a decision either to setup his establishment either in a mall or in a marketplace, he can opt to choose a marketplace rather than in mall, by considering the statistics listed in the table above. An opportunity lies more in marketplace, rather than in a mall.

 

 

 

 

 

 

YES NO

 

4> What do you prefer?

 

 

 

 

 

 

MALL MARKET-PLACE

 

 

Computation and analysis:

 

To have a general understanding of the trend exhibited by the collected data, the bar graph has been plotted using the raw data for the responses of the above specified questions.

The forward regression analysis method was used to establish the relationship between the mall preference and other factors which may affect the consumer buying behaviour, to name few: Information, Money, Perception etc…

OpenStat software was used to analyse the data.

The statistical inferences achieved are as below:

1. The adjusted R2 was found to be 1.

2. The standard error was 0.90

3. The prob>t was found to be 0 for all the independent variables.

 

The snapshot of the output explains the overall fit:

 

Thus, the final, multiple regression linear equation to be:

Pref_Mall = -0.362 + 0.82 * Visit_Yes + 1 * Pref_Mkt

 

Where,

Pref_Mall = preference of the customer towards a mall.

Visit_Yes = will the customer visit the mall, before making any buying decision.

Pref_Mkt = affinity of the customer towards the marketplace.

Thus, the complex buying behaviour of the customer depends on the various attributes present in the environment and most of all, the perception of a consumer towards the factors plays a vital role in buying decision.

From the model explained above, we can also infer that:

1. Consumer has negative perception about his preference towards a mall.

2. The consumer does visit the mall to collect information about the product he is willing to buy.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Note: the detailed statistical computation on consumer behaviour is attached in appendix 4

New Service Introduction:

 

The new product is termed as “Shopping-Mall Cart”; the cart can be used just like any cart which are seen in retail stores like “Big-Bazaar, Tesco, and Star-Bazaar etc…” the carts in retail stores are being used to collect the items by the customers and move to billing counter to get the items billed. Similarly, the “Shopping-Mall Cart” will be in dispense to the customers at the time they enter into the mall and the same cart can be used to carry their items of purchases as well as the items they are going to purchase in any of the retail stores inside the mall. The “shopping-Mall Cart” has the following advantages:

1. The customer need not deposit their belongings in every retail stores while entering the stores.

2. The customer need not carry their belongings in their hands while they are walk in the mall.

The conjoint technique was used to understand the feasibility for introducing a new product to provide service to customers who visit the mall.

Conjoint Approach:

 

The customers of the mall were interviewed to rank their preferences to a mall based on the attributes listed below:

1. Parking Space and

2. Shopping Mall Cart.

The below shown question was used to understand the preference of the customer for the above listed attributes.

 

In the grid given below, rank your preferences with the assistance of the interviewer.

 

Note: Please rank on a scale of 1 to 10, 1 being the lowest and 10 the highest.

Please DO NOT repeat the ranks once used to help us in interpreting the data.

 

SHOPPING-MALL CART AVAILABILITY

NO

YES

PARKING AVAILABILITY

Not at all

Limited

Ample

 

 

 

The above question is designed based on ‘Pairwise Approach’ / ‘Two – Factor Approach’. The attributes in evaluation has (3 level) X (2 level) = 6 profiles. The 3 levels are constituted from attribute “Parking Availability”, the 2 level is constituted from “Shopping- Mall Cart” attribute.

The statistical inferences after doing conjoint analysis are as below:

Measure

Description

Value

se1, se2…. se4

The coefficients of attribute and constant

Se1 = Constant = 2.09

Se2 = coefficient of ShoppingMallCarts = 2.15

Se3 = coefficient of AmpleParkingSpace = 4.72

Se4 = coefficient of LimitedParkingSpace = 1.99

r2

The Coefficient of determinate

0.930032285

F

The F-observed value.

1.37314E+31

df

The degrees of freedom

2

Critical Value of F

Critical Value of F

0.09803453

 

Inferences:

1. The coefficient of determination, or r2, is 0.930032285 which would indicate a strong relationship between the independent and dependent variables.

2. The critical level of F is 0.09803453. Since F = 1.37314E+31 is much higher, it is extremely unlikely that an F value this high occurred by chance. The hypothesis that there is no relationship between independent and dependent variables is to be rejected.

3. The regression equation will be:

Shopping Mall preference = 2.09 +2.15 * ShoppingMallCarts + 4.72 * AmpleParkingSpace + 1.99 * LimitedParkingSpace

Thus, the introduction of new service “Shopping-Mall Cart” in malls will increase the consumer preference towards such malls.

 

 

 

 

 

 

 

 

 

 

 

Note: the detailed statistical computation is attached in appendix 2

Comparison between Mall and Marketplace:

 

To understand the consumer orientation towards the mall or the marketplace, the following attributes were considered:

1. Distance of mall and marketplace from consumer’s residence.

2. Number of monthly visits made by consumers to mall and marketplace.

3. Approximate money spent in each of the places.

4. Time spent by consumers in each of the places.

5. Few of the personal information.

Analysis:

 

The analysis of the surveyed data was done purely by using descriptive analysis – Frequency distribution analysis.

The surveyed data was scaled to interpret the data, the scales are as described below:

1. For the data related to Distance between mall / marketplace, Staple scale was used. The scale being, [ 2 , 1, -1, -2] for distances [ less than 2km, 2km to 5km, 5km to 10km, more than 10km].

 

The reason for selecting such weights being, nearer the mall / marketplace, its more convenient for the consumers to visit such places.

 

2. For the data related to Number of Visits, Staple scale was used. The scale being [ -2, -1, 1, 2] for the Number of Visits [ 0, 1 to 5, 6 to 10, more than 10].

 

The reason for selecting such weights being, more the visits more likely the customer is going to spend / gets acquainted with such places.

 

3. For the data related to Money Spent in previous visits, Semantic Differential scale was used. The scale being, [ -2, -1, 0, 1, 2] for spending [0rs, less than 500rs, 500rs to 1000rs, 1000rs to 5000rs, more than 5000rs].

 

The reason for selecting such weights being, if the customer spends no money, the malls/marketplace will not be able to earn any revenue, in such a case, it suffers loss. If the customers spend less than 500rs, the mall may not make any profits when compared to investments it has made. If the customer spends between 500rs to 1000rs, the spending may meet the necessary expenses of the mall but, need not make any profits. Contrarily, if the customer spends anything more than 1000, the malls may earn some profits.

 

4. For the data related to time spent, Ranking / Ordinal scale is being used. The scale being, [ 4, 3, 2, 1] for the time spending [ less than an hour, 1hr to 3hrs, 3hrs to 5hrs, more than 5hrs].

 

The reason being, if the customers take less time to make any buying decisions and leave the mall/marketplace, allows the mall/marketplace to establish more Footfalls, thereby, creating new opportunities.

The following questions from questionnaire were used to do a comparative study between the mall and marketplace:

5> How far is the MALL that you visit frequently, from your residence?

 

 

 

Less than 2 kms

 

 

 

2 to 5 kms

 

 

 

 

 

 

5 to 10 kms

More than 10 kms

 

6> How often do you visit a MALL in a month?

 

 

 

 

 

 

Not even once 1 to 5 times

 

 

 

 

 

 

6 to 10 times More than 10 times

 

7> How much did you approximately spend in the last 3 visits at a MALL?

 

 

 

Did not spend

 

 

 

Less than Rs. 500

 

 

 

Rs.500 to Rs.1000

 

 

 

Rs.1000 to Rs.5000

 

 

 

Rs. 5000 and above

 

8> How much time do you approximately spend at a MALL?

 

 

 

Less than 1 hour

 

 

 

1 to 3 hours

 

 

 

3 to 5 hours

 

 

 

More than 5 hours

 

9> How far is the MARKET-PLACE you visit frequently from your residence?

 

 

 

Less than 2 Kilometers

 

 

 

2 to 5 Kilometers

 

 

 

 

 

 

5 to 10 Kilometers

More than 10 Kilometers

 

10> How often do you visit a MARKET-PLACE in a month?

 

 

 

 

 

 

Not even once 1 to 5 times

 

 

 

 

 

 

6 to 10 times More than 10 times

 

11> How much time do you approximately spend in a MARKET-PLACE?

 

 

 

Less than 1 hour

 

 

 

 

 

 

1 to 3 hours

 

 

 

3 to 5 hours

More than 5 hours

 

Age: ___

 

Monthly Income:

 

 

 

Less than Rs. 10,000

 

 

 

Rs. 10,000 to Rs. 25,000

 

 

 

Rs. 25,000 to Rs. 50,000

 

 

 

Greater than Rs. 50,000

 

 

 

Not Applicable

 

 

The following were the inferences made after analyzing the data:

Parameter /

Attribute

Mall

 

 

 

(freq) (%ge)

Marketplace

 

 

 

(freq) (%ge)

Inference

Distance

More than 10km = 23 0.14

5km to 10km = 42 0.26

2km to 5km = 54 0.33

Less than 2km = 45 0.27

More than 10km = 10 0.06

5km to 10km = 30 0.18

2km to 5km = 40 0.24

Less than 2km = 83 0.51

Marketplace is much closer to residence for most of the samples.

Monthly

Visits

No visit = 15 0.09

1 to 5 = 131 0.80

6 to 10 = 11 0.07

more than 10 = 6 0.04

No visit = 13 0.08

1 to 5 = 89 0.54

6 to 10 = 41 0.25

more than 10 = 18 0.11

Marketplaces have more opportunity to influence on the consumer behaviour as consumers visit them the most.

Money

Spending

No Spending = 5 0.03

less than 500rs = 13 0.08

500rs to 1000rs = 61 0.37

1000rs to 5000rs = 64 0.39

more than 5000rs = 21 0.13

No Spending = 3 0.02

less than 500rs = 10 0.06

500rs to 1000rs = 63 0.38

1000rs to 5000rs = 79 0.48

more than 5000rs = 9 0.05

Consumers spend more money in marketplaces rather than in malls.

Time

Spending

less than an hour = 4 0.02

1hr to 3hrs = 109 0.66

3hrs to 5hrs = 46 0.28

more than 5hrs = 3 0.02

less than an hour = 35 0.21

1hr to 3hrs = 93 0.57

3hrs to 5hrs = 30 0.18

more than 5hrs = 6 0.04

More footfalls can be achieved in marketplace rather than in mall.

 

Thus, if a retail stores owner has to make a decision either to setup his establishment either in a mall or in a marketplace, he can opt to choose a marketplace rather than in mall, by considering the statistics listed in the table above. An opportunity lies more in marketplace, rather than in a mall.

 

 

 

 

Note: the detailed analysis data can be found in appendix 3. This also has data related to monthly salary and age.

To understand the impact of malls on consumer buying behaviour:

 

The consumer buying behaviour is a complex structure, where the perception of the consumer towards the factors of decision making process plays a vital role.

An effort, to understand the perception of malls on consumers mind was brought about through few specific questions in questionnaire, which are as listed below: