final new project for visits in r-city mall

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Sales optimization management in R-city mall Use of statistics in solving real business issues Group members : Sneh Karamchandani(25) Ashwini kumar (28) Nirriti s.s. (35) Pratik Shah (47) Mukull Ovalkar (59) Abhishek Yadav (60)

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Page 1: Final New Project for Visits in R-City Mall

Sales optimization management in R-city mall

Use of statistics in solvingreal business issues

Group members : Sneh Karamchandani(25)

Ashwini kumar (28) Nirriti s.s. (35)

Pratik Shah (47) Mukull Ovalkar (59) Abhishek Yadav (60)

Page 2: Final New Project for Visits in R-City Mall

Observation

Welcome to R City! The city with a fresh experience for the true shopper With a blend of the best in shopping, entertainment and food

Approximately around 5000 people visit the mall every day

Page 3: Final New Project for Visits in R-City Mall

OBJECTIVE OF THE RESEARCHPerform a continuous probability analysis to

determine the customers’ visits and

their choice of buying ,during the time zone of 2pm-5pm and 7pm-9pm and use the analysis for the sales

optimization.

Page 4: Final New Project for Visits in R-City Mall

DETERMINATION OF SAMPLE SIZE

Sample 1Time zone- 2pm-5pmNo.of observations – 100 Sample 2 Time zone- 7pm-9pm No. of observations- 200

Page 5: Final New Project for Visits in R-City Mall

DESCRIPTION & CALCULATIONS

DATA –The following information provides the number of footfalls during the two time zones. The data is categorized into 3 different zones of the R-city mall.

Determine the probability that people entering the mall between

1)2-5pm 2)7-9 pm come only for entertainment purpose?TYPETIME ZONE

GROCERYSHOPPING (G)

ENTERTAINMENTSHOPPING (E)

APPAREL SHOPPING (A)

ROW TOTAL

TIME 2-5pm (T1)

70 20 10 100

TIME 7-9pm (T2)

30 110 60 200

COLUMN TOTAL

100 130 70 300

Page 6: Final New Project for Visits in R-City Mall

CALCULATIONS

P(T1) = 100/300 = 0.33 P(T2) = 200/300 = 0.67

CONDITIONAL PROBABILITY TABLE

JOINT PROBABILITY TABLE

G/T E/T A/T

T1 0.7 0.2 0.1

T2 0.15 0.55 0.3

p(G/T)*p(T) P(E/T)*p(T) p(A/T)*p(T)

T1 0.231 0.066 0.033

T2 0.1005 0.3685 0.201

Page 7: Final New Project for Visits in R-City Mall

TREE DIAGRAM

Page 8: Final New Project for Visits in R-City Mall

CALCULATIONSBAYES’S LAW = Condition1 -Time zone (T1)2-5pm P(E/T1) P(T1) P(T1/E) = P(E/T1)P(T1) + P(E/T2)P(T2) 0.2* 0.33 0.2*0.33 + 0.55*0.67P(T1/E)= 0.1519 =15.19% BAYES’S LAW = Condition2 -Time zone (T2) 7-9pm P(T2/E) = P(E/T2) P(T2) P(E/T1)P(T1) + P(E/T2)P(T2) 0.55* 0.67 0.2*0.33 + 0.55*0.67P(T1/E)= 0.848 = 84.8%

Page 9: Final New Project for Visits in R-City Mall

CONCLUSION-

The probability that people entering the mall between 2-5pm , come only for entertainment purpose is 15.19%

The probability that people entering the mall between 7-9pm , come only for entertainment purpose is 84.8%

Page 10: Final New Project for Visits in R-City Mall

INFERENCE

As people entering mall between 2-5 pm are concentrating more on grocery than any other entertainment purpose. So the mall management should concentrate to promote these products in afternoon

while people entering mall between 5-7 pm mainly come for the entertainment purpose so the management should concentrate entertainment products and food items etc.in the evening

This will ultimately optimize the net sales of the mall

Page 11: Final New Project for Visits in R-City Mall

WEAKNESSES IN SAMPLES

Not everyone in the mall come for the shopping or entertainment purpose

The observations conducted are not exact representation of the population

During the festival seasons the variation in the number of customers is very high

Page 12: Final New Project for Visits in R-City Mall

Thank You