Section A_Group 6_Suresh Neela_13PGP035 Session 6

Application of Chi Square Tests in Marketing

We have learnt what Chi Square test is and the step by step process how to implement it in class room discussion. But as a marketer, knowing how exactly this test can be implemented in real time business scenario is much more helpful. Hence, I would like to discuss the same in this article. Hope, it would be interesting for all the visitors of this blog.

Especially, companies are interested in knowing consumer behavior about the products. For example, Are all colors of refrigerators equally preferred among consumers? Is there any association between income of the family and brand preference to buy refrigerators? Like this, the day to day life of marketing job involves many such scenarios to make effective decision.

As we all know, Chi Square test is implemented to find out whether there are differences with Categorical variables ( Color category: Red,Blue,Green,Orange &  Income category: Lower, Middle, Upper etc..), it is broadly used in two different scenarios in marketing discipline

  1. 1.     Goodness of Fit Test

This is mainly used, to find out how closely expected and observed frequencies are matched. Only, single variable can be considered here as mentioned in the above examples “color of refrigerator”.

Let us consider a marketer wants to check the preference of 200 consumers among four colors (Red, White, Blue and Black) of refrigerators stating following null hypothesis

Null Hypothesis                :               All colors of refrigerator are equally preferred.

Alternative Hypothesis   :               All colors of refrigerator are not equally preferred  

After comparing Observed frequencies which might be from survey, questionnaire & Expected frequencies which are equal (50 for each in this case, response from 200 respondents) from the above mentioned null hypothesis.

Upon all calculations using the formula related to Chi square test, if the computed value is greater than critical value at 5% level of significance and 3 degrees of freedom i.e (n-1)  where n= 4 colors, then null hypothesis is rejected. Hence in this case we can infer all colors of refrigerators are not equally preferred

  1. 2.     Independence Test

This is mainly used, to find out whether, there is a relation or association between two categorical variables or not, that means are they independent or dependent? Two variables mentioned in the above example are “Income group of the family “(Lower, Middle, & Upper) and “Brand preference” (Samsung, LG, Whirlpool, Godrej)

Null Hypothesis               :  The two variables are independent

 Alternative Hypothesis: The two variables are dependent

Again from a sample of 200 consumers, the contingency table can be prepared with total cells 12 (3 from Income group * 4 from brands)

Upon all calculations using the formula related to Chi square test, if the computed value is greater than critical value at 5% level of significance and  6 degrees of freedom i.e (n1-1)*(n2-1)  where n1= 3 and n2=4, then null hypothesis is rejected. Hence, in this case we can infer Income group of the family and Brand preference are dependent

In this way depending on the research objective, marketing manager can implement Chi-square test in decision making process related to the products.

 

References:

http://www.slideshare.net/parth241989/chi-square-test-16093013

http://www.simafore.com/blog/bid/54594/How-to-use-Chi-Square-test-for-3-common-business-analytics-problems

http://davidmlane.com/hyperstat/viswanathan/chi_square_marketing.html

http://www.polarismr.com/Portals/58820/research-lifeline/chi-square-test.htm

 

 

Suresh Neela

13PGP035

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