Market Research on Consumer buying preferences in Out-of-Stock Situation .

MARKET RESEARCH ASSIGNMENT INDO-GERMAN TRAINING CENTER INDO GERMAN CHAMBER OF COMMERCE BANGALORE

CONSUMER RESPONSE TO THE RETAIL MARKET OUT OF STOCK SITUATION

DATE OF SUBMISSION: 21-04-2013

SUBMITTED BY: BINDU THUSHARA. N

SUMMARY: A stockout or out-of-stock (OOS) event is an event that causes inventory to be exhausted. While out-of-stocks can occur along the entire supply chain, the most visible kind is retail out-of-stocks in the fast moving consumer goods industry. Stockouts frustrate shoppers and force them to take a number of corrective actions that are beyond the retailer’s control. Understanding how consumers respond to stockouts is therefore the starting point for retailers who wish to improve on-shelf availability. When shoppers are unable to find an item that they had intended to purchase, they might switch stores, purchase substitute items (brand switch, size switch, and category switch), postpone their purchase or decide not to buy the item at all. Customer responses differ in severity and each entails negative consequences for retailers. Stockouts cause lost sales, dissatisfy shoppers, diminish store loyalty, jeopardize marketing efforts, and obstruct sales planning, because substitution disguises true demand. Moreover, shopper surveys reveal stockouts to currently be the most prevalent annoyance to shoppers. Shoppers spend a considerable amount of time looking for and asking for out-of-stock items. Shopper response to stockouts has been investigated by researchers with respect to cognitive response-perceived availability, affective response-store satisfaction and behavioural response-brand switching.

OBJECTIVE OF THE RESEARCH: To understanding and analyse customer buying behaviour of toiletries in general retail market out of stock situations. Variables related to Customer’s preference, age group, monthly toiletries expense and monthly income have to be considered and the data related to it has to be collected. RESEARCH METHODOLOGY: The data is collected using Primary Research. Quantitative method-Field/Online Survey is used to collect explanatory data about the Customer buying behaviour of toiletries in the Out-Of-Stock Situation in a Retail market. SAMPLE SIZE ESTIMATION: According to the interval scale 1-5, N= ((z*s)/e)^2 Where, Tolerable error in estimating the value, e: 0.10. Z= Desired confidence level: 90%=1.645 Standard Deviation, s: (Max-Min)/6 In this case, the Max value is 5 and Min value is 2, S= (5-2)/6=>3/6=>1/2=>0.5 Therefore, n= ((1.645*0.5)/0.10)^2=>67.65. The sample size needed for performing the analysis is 68.The population size is 131(Responses from Online Survey).

But, the research is based on the preferences of the customer involving explanatory data hence clustered sampling is considered. According to Probability clustered Sampling: Clusters are identified from the survey responses. This case demands three clusters. ANALYSIS OF THE SURVEY: CLUSTER ANALYSIS This case includes explanatory data that sorts cases into clusters for easy understanding and analysis. We perform this analysis in two steps: Hierarchical Clustering and K-Means Clustering (Quick Clustering). Hierarchical Clustering also generates Proximity matrix which is a Bottom-Up approach. Proximity matrix contains similarities between observations.

GGraph [DataSet1] C:\Users\Bindu Suhas\Desktop\MISC\MarketSurvey for OOS.sav

Customers were asked to answer 15 questions. For analysis 12 questions have been considered based on which three clusters have been formed. Questions related to Geographic details- Location was not included in the analysis as many did not answer this question. The questions were based on: Toiletries Purchase Frequency, Brand Preference, Out-Of-Stock Situation experience, Out-Of-Stock Situation reaction, Preference of Retail Market Location, Product Unavailability Waiting Reaction, Age, Gender, Substitute Influence, Product Packaging Preference, Monthly Toiletries Expense and Monthly Income. The Questionnaire form was designed with the help of Google Forms which automatically collects the Online Survey answers in the string format in Google spread sheet. This spread sheet was exported to SPSS and the string values were converted to Numerical values to perform Cluster Analysis.

Hierarchical Clustering: Average linkage (between groups)

[DataSet1] C:\Users\Bindu Suhas\Desktop\MISC\MarketSurvey for OOS.sav Agglomeration Schedule Stage

Cluster Combined Cluster 1

Coefficients

Cluster 2

Stage Cluster First Appears Cluster 1

Next Stage

Cluster 2

1

2

4

18.000

0

0

2

2

2

3

28.000

1

0

3

3

2

11

38.667

2

0

4

4

2

8

41.000

3

0

6

5

6

7

49.000

0

0

6

6

2

6

88.300

4

5

10

7

5

10

89.000

0

0

8

8

1

5

113.500

0

7

9

9

1

9

142.000

8

0

10

10

1

2

143.500

9

6

11

11

1

12

271.091

10

0

0

K-Means Clustering-Quick Cluster [DataSet1] C:\Users\Bindu Suhas\Desktop\MISC\MarketSurvey for OOS.sav

Initial Cluster Centers Cluster 1

2

3

ToiletriesPurchaseFrequency

3.00

1.00

1.00

PreferenceOfBrand

1.00

2.00

1.00

RetailMarketLocationPreferenc

1.00

1.00

1.00

OutOfStockSituationReaction

3.00

2.00

3.00

OutOfStockSituationExperience

1.00

1.00

1.00

ProductUnavailabilityWaitingRe

2.00

3.00

1.00

PreferenceOfProductPackaging

1.00

1.00

1.00

SubstituteInfluence

1.00

3.00

1.00

GenderOfPerson

1.00

2.00

1.00

Age

4.00

1.00

1.00

ToiletriesExpensePerMonth

3.00

1.00

3.00

IncomePerMonth

4.00

4.00

1.00

e

action

a

Iteration History Iteration

Change in Cluster Centers 1

2

3

1

1.970

2.016

1.877

2

.000

.000

.000

a. Convergence achieved due to no or small change in cluster centers. The maximum absolute coordinate change for any center is .000. The current iteration is 2. The minimum distance between initial centers is 4.796.

Final Cluster Centers Cluster 1

2

3

ToiletriesPurchaseFrequency

2.46

1.60

1.82

PreferenceOfBrand

1.33

1.30

1.00

RetailMarketLocationPreferenc

1.54

1.15

1.27

OutOfStockSituationReaction

2.33

2.35

1.09

OutOfStockSituationExperience

1.38

1.50

1.18

ProductUnavailabilityWaitingRe

2.05

2.50

1.59

PreferenceOfProductPackaging

1.28

1.00

1.18

SubstituteInfluence

1.97

2.25

1.73

GenderOfPerson

1.59

1.85

1.64

Age

2.92

1.40

1.41

ToiletriesExpensePerMonth

2.92

1.85

2.14

IncomePerMonth

3.77

2.95

1.59

e

action

Distances between Final Cluster Centers Cluster

1

1

2

3

2.340

2

2.340

3

2.940

2.940 1.849

1.849

ANOVA Cluster Mean

Error df

Mean Square

F

Sig.

df

Square ToiletriesPurchaseFrequency

5.895

2

.818

78

7.211

.001

.838

2

.165

78

5.082

.008

RetailMarketLocationPreference

1.141

2

.213

78

5.362

.007

OutOfStockSituationExperience

.510

2

.545

78

.936

.397

OutOfStockSituationReaction

.557

2

.224

78

2.482

.090

4.337

2

.644

78

6.736

.002

.526

2

.143

78

3.673

.030

1.431

2

.501

78

2.856

.064

.462

2

.219

78

2.108

.128

23.309

2

.601

78

38.777

.000

9.045

2

.563

78

16.067

.000

33.380

2

.656

78

50.861

.000

PreferenceOfBrand

ProductUnavailabilityWaitingReaction PreferenceOfProductPackaging SubstituteInfluence GenderOfPerson Age ToiletriesExpensePerMonth IncomePerMonth

Anova table tells us which of the 12 variables is significantly different and significant across three clusters at 0.10 tolerance level (90% confidence level). Interpretations on clusters could be made with the help of significant variables. Considering the last column of Anova table, the probability values of variables ‘out of stock situation experience and gender of a person’ appear to have values more than 0.10 hence are not significant and others have probability values less than 0.10 equivalent to 90% confidence level.

Number of Cases in each Cluster

Cluster

1

39.000

2

20.000

3

22.000

Valid

81.000

Missing

50.000

INTERPRETATION: Considering the options provided to the questions, clusters are interpreted on the values given to those solutions.

Cluster 1: People stock up toiletry products for a month completely, are not very particular about brand, are not particular about retail market location, prefer good product packing, If they chose to wait and the product is still not available then they would check and confirm if at all the product would be made available in the retail market else would change the retail market, approach a different retail market in case their product is not available for long, have experienced out of stock situation If they don’t have a favourite brand, they would get attracted to better offers, Could be mostly women, could have toiletries expense between 600-1000, Could be aged between 40-50 years and have monthly income more than 15000rs.

Cluster 2: People of this cluster purchase toiletries weekly once, are brand specific, are particular about the retail market location, chose a substitute if their favourite brand is not available for long, have never experienced out of stock situation, will prefer sticking on to their favourite brand for some more time, will check and confirm with the retailer if the product would ever be made available before thinking to opt a substitute, usually men and less women, aged less than 25 or little above, will have toiletries expenses between 200-500 and income between 11000-15000.

Cluster 3: Purchase toiletries weekly once, are brand specific, not very particular about the retail market location, would not mind waiting for the product to be made available, have experienced out of stock

situation, toiletries expense would be 200-500, income will be between 5000-10000, could be equal number of men and women, aged about 25 or little more, they would check for better offers either in the same brand or a substitute, not very particular about product packaging.

Conclusion: Cluster analysis helped us segment the surveyed data (People’s responses) and understand how different opinions and preferences could be. From the analysis, it is understood that very less people wait but not for long for the same product and there are many chances that many would shift the retail market in case the product unavailability is frequent. Also, the threat of substitutes would hamper the business of a retailer hence inventory management has to be taken care by the retailer and also the company associated.

TOOLS USED FOR RESEARCH: 

Google survey form



SPSS-Statistical analysis software



Microsoft Excel