market research on consumer buying preferences in out-of-stock situation

7/30/2019 Market Research on Consumer buying preferences in Out-of-Stock Situation .

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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


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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

retailers control. Understanding how consumers respond to stockouts is therefore the starting poin t 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 revealstockouts 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 Customers 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.Thepopulation size is 131(Responses from Online Survey).
http://en.wikipedia.org/wiki/Inventoryhttp://en.wikipedia.org/wiki/Inventory


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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


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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, ProductPackaging 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 Coefficients Stage Cluster First Appears Next Stage

Cluster 1 Cluster 2 Cluster 1 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 44 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


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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

e

1.00 1.00 1.00

OutOfStockSituationReaction 3.00 2.00 3.00

OutOfStockSituationExperience 1.00 1.00 1.00

ProductUnavailabilityWaitingRe

action

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

Iteration Historya

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.


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Final Cluster Centers

Cluster

1 2 3

ToiletriesPurchaseFrequency 2.46 1.60 1.82

PreferenceOfBrand 1.33 1.30 1.00

RetailMarketLocationPreferenc

e

1.54 1.15 1.27

OutOfStockSituationReaction 2.33 2.35 1.09

OutOfStockSituationExperience 1.38 1.50 1.18

ProductUnavailabilityWaitingRe

action

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

Distances between Final Cluster Centers

Cluster 1 2 3

1 2.340 2.940

2 2.340 1.849

3 2.940 1.849

ANOVA

Cluster Error F Sig.

Mean

Square

df Mean Square df

ToiletriesPurchaseFrequency 5.895 2 .818 78 7.211 .001

PreferenceOfBrand .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

ProductUnavailabilityWaitingReaction 4.337 2 .644 78 6.736 .002

PreferenceOfProductPackaging .526 2 .143 78 3.673 .030

SubstituteInfluence 1.431 2 .501 78 2.856 .064

GenderOfPerson .462 2 .219 78 2.108 .128

Age 23.309 2 .601 78 38.777 .000

ToiletriesExpensePerMonth 9.045 2 .563 78 16.067 .000IncomePerMonth 33.380 2 .656 78 50.861 .000


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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 dont 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


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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 (Peoples 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

market research on consumer buying preferences in out-of-stock situation

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