ANOVA IN MARKETING RESEARCH

BY:VIVEK GOYAL DATA ANALYSIS WORKSHOP ON SPSS.(LPU)

MARKETING RESEARCHMARKETING RESEARCH IS GENERALLY ACTIVATED WHEN THERE IS A QUESTION TO BE ANSWERED , ONLY WHEN THERE IS A INFORMATION GAP.

MARKETING RESEARCH IS GENERALLY TAKEN UP AS A SHORT-TERM PROJECT WITH CLEARLY DEFINED SCHEDULED,BUDGET, WHICH SHOULD MAKE MARKETING DECISION –MAKING.

WHO DOES THE RESEARCH?

IT IS A INTERMITTENT ACTIVITY, BASED ON A IDENTITICAL INFORMATION GAP OR INFORMATION NEED.

A LARGE NUMBER OF PROFESSIONAL RESEARCH ARE AVIALABLE TO DO THE NEEDFUL. THEY HAVE BRANCHES ( IN CASE OF INDIA) ALL OVER THE COUNTRY AND SOME HAVE GLOBAL ASSOCIATES IF RESEARCH IS GOING BEYOND INDIA.

WHEN TO DO MARKETING RESEARCH-WHEN THERE IS A INFORMATION GAP WHICH CAN BE FILLED BY DOING RESEARCH.

-THE COST FILLING GAP THROUGH MARKETING RESEARCH IS LESS THAN TAKING WRONG DECISION WITHOUT DOING RESEARCH.

-THE TIME TAKEN FOR THE RESEARCH DOES NOT DELAY DECISION –MAKING BEYOND RESONABLE LIMITS.

TEST MARKETING

THIS IS A NAME USED FOR A CLASS OF CONTROLLED EXPERIMENTS IN MARKETING RESEACH.

ITS OBJECTIVE IS TO PREDICTS SALES BASED ON CHANGE IN MARKETING VARIABLE LIKE PRICE,PROMOTION,ADVERTISEMENT.

EXPERIMENTAL DESIGN ARE ALSO CLASSFIED IN TERMS OF NUMBER OF INDEPENDENT VARIABLE USED .

EXPERIMENTAL DESIGN

THE DESIGN OF EXPERIMENT IS MOST CRITICAL WHEN IT IS ANALYSED THROUGH ANOVA.

THE TYPES OF DESIGN USE IN MARKETING RESEARCH EXPERIMENT ARE:

COMPETELY RANDONMISED DESIGN IN ONE WAY-ANOVA.THIS DESIGN IS USED WHEN THREE IS ONLY ONE CATEGORY OF INDEPENDENT VARIABLE AND ONE DEPENDENT VARIABLE.EACH CATEGORY OF INDEPENDENT VARIABLE IS CALLED THE LEVEL.IN THIS TYPE OF DESIGN WE RANDOMLY ALLOCATE VARIOUS SAMPLE TO DIFFERENT LEVEL. THEN WE CONDUCT F-TEST UNDER ANOVA TO TEST THE NULL HYPOTHESIS THAT THE MEAN VALUE OF INDEPENDENT VARIABLE.

RANDOMLY BLOCK DESIGN:

IT IS USED IF THERE IS ADDITIONAL VARIABLE {CALLED BLOCK} WHICH HAS A IMPACT ON RELATIONSHIP BETWEEN INDEPENDENT VARIABLE AND DEPENDENT VARIABLE.

FACTORIAL EXPERIMENT:

WHEN TWO OR MORE VARIABLE DESIGN ARE TESTED THROUGH ANOVA THEN WE USE FACTORIAL DESIGN.

USE OF ANOVA INMARKETING RESEARCH

ANOVA uses the sample that are randomly selected from the population. The population should have equal variance.

ANOVA uses squared deviation of variance so that computation of distance of individual data point from their own mean or from grand mean can be summed ( i.e S.D sum to zero).

The total deviation of any particular data point may be partitioned into – BETWEEN GROUP VARIANCE and WITHIN GROUP VARIANCE.

EXAMPLE:THE EXPERIMENT CONDUCTED ON ’20’ RANDOMLY SELECTED RESPONDENT (ICE-CREAM):

PRICE OF 1 KG.PACK CODERs. 100.00 1Rs.110.00 2Rs.130.00 3Rs.140.00 4

ICE-CREAM FLAVOURS CODESTRAWBERRY 1VANILLA 2CHOCOLATE 3PISTA 4

ANOVA ( FLAVOURS AND PRICE)

Sum of Squares df Mean Square F Sig.FLAVOURSBetween Groups 20.000 17 1.176 .471 .850 Within Groups 5.000 2 2.500 Total 25.000 19

PRICEBetween Groups 26.700 17 1.571 6.282 .146 Within Groups .500 2 .250 Total 27.200 19

OUTPUT OF SPSS OF ICE-CREAM

RANDOMISED DESIGN ONE WAY ANOVA

ANOVA( SALES AND FLAVOURS)

Sum of Squares df Mean Square F Sig.SALESBetween Groups 147616.875 3 49205.65 212.011 .000 Within Groups 65546.875 16 4096.680

Total 213163.750 19

FLAVOURSBetween Groups .000 3 .000 .000 1.000 Within Groups 25.000 16 1.563 Total 25.000 19

ANOVA ( PRICE*SALES)

Sum of Squares df Mean Square F Sig.PRICEBetween Groups .000 3 .000 .000 1.000 Within Groups 27.200 16 1.700 Total 27.200 19

SALESBetween Groups 11463.750 3 3821.250 .303 .823 Within Groups 201700.000 16 12606.250 Total 213163.750 19

SALES*FLAVOURS OF ICE-CREAM Crosstab

Count FLAVOURS 1 2 3 4 Total SALES 900 0 0 0 1 1 930 0 0 1 0 1 1000 1 1 0 0 2 1020 0 0 0 1 1 1025 0 1 0 0 1 1050 1 0 0 0 1 1075 1 0 0 0 1 1090 0 0 0 1 1 1100 0 0 1 0 1 1110 0 0 0 1 1 1125 0 0 1 0 1 1150 0 1 0 0 1 1175 0 1 0 0 1 1180 0 0 1 0 1 1200 1 0 0 1 2 1220 0 1 0 0 1 1225 0 0 1 0 1 1310 1 0 0 0 1Total 5 5 5 5 20

Chi-Square Tests( SALES*FLAVOURS)

Value df Asymp. Sig. (2-sided)Pearson Chi-Square 52.000(a) 51 .435Likelihood Ratio 49.900 51 .517

Linear-by-Linear Association .813 1 .367N of Valid Cases 20

a 72 cells (100.0%) have expected count less than 5. The minimum expected count is .25.

SALES*PRICE OF ICE-CREAMCrosstab

Count PRICE 1 2 3 4 Total SALES 900 0 0 0 1 1 930 0 0 0 1 1 1000 0 0 0 2 2 1020 0 0 1 0 1 1025 0 0 1 0 1 1050 1 0 0 0 1 1075 0 0 1 0 1 1090 1 0 0 0 1 1100 0 0 1 0 1 1110 0 1 0 0 1 1125 1 0 0 0 1 1150 0 1 0 0 1 1175 1 0 0 0 1 1180 0 1 0 0 1 1200 1 1 0 0 2 1220 1 0 0 0 1 1225 1 0 0 0 1 1310 1 0 0 0 1Total 8 4 4 4 20

Chi-Square Tests(PRICE*SALES)

Value df Asymp. Sig. (2-sided)Pearson Chi-Square 56.250(a) 51 .285Likelihood Ratio 50.515 51 .493Linear-by-Linear Association 12.232 1 .000N of Valid Cases 20

a 72 cells (100.0%) have expected count less than 5. The minimum expected count is .20.

Chi-Square Tests(PRICE*FLAVOURS)

Value df Asymp. Sig. (2-sided)Pearson Chi-Square .000(a) 9 1.000Likelihood Ratio .000 9 1.000Linear-by-Linear Association .000 1 1.000N of Valid Cases 20

a 16 cells (100.0%) have expected count less than 5. The minimum expected count is 1.00.

Descriptive Statistics

N Minimum Maximum Mean Std. Deviation Variance Statistic Statistic Statistic Statistic Std. Error Statistic StatisticSALES 20 900 1310 1104.25 23.685 105.920 11219.14FLAVOURS 20 1 4 2.50 .256 1.147

1.316PRICE 20 1 4 2.20 .268 1.196

1.432Valid N (listwise) 20

DESCRIPTIVE

REFERENCERajendra Nargundkar , Text and Cases, 3rd Edition

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