Mission 2 - Consumer Psychology

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Fayetteville State University *

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Statistics

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Jan 9, 2024

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STAT E-100 FA 2023 Mission 2: Consumer Psychology Study Context 1) During Lecture 3, we explored a dataset called “Customer Personality Analysis” on Kaggle (https://www.kaggle.com/datasets/imakash3011/customer-personality- analysis ). In under 250 words sentences, what could be several aims or reasons for collecting this data? In other words, how might findings from this study provide value for a stakeholder (e.g., business owner, entrepreneur, marketer, consumer psychologist, etc.). This data could be very helpful to analyze which products in the store sell more often and which make more money. With this information, m anagers and stakeholders can make decisions on which products to stock more or less, and which products they should market more eggressively. The marital status and income of their customers can also help them stock items that fit their budget and lifestyle better, resulting in higher sales. The recency of visit can also show just how loyal their customers are, and incorporate new strategies to have customers shop there more often. The Profile of a Customer To receive full credit, please include the R code and the output from R Studio. 2) Using the variable ‘Income’, calculate the mean income. $ 52 , 247.25 mean(cpa$Income, na.rm=TRUE) 3) Using the variable ‘Income’, calculate the median income. What does the difference in the mean and median incomes suggest about the skewness of the distribution of income? $ 51 , 381. 5 median(cpa$Income, na.rm=TRUE) Since the mean and median are very close to each other, t his suggests that the distribution is not significantly skewed in either direction 4) Using the variable ‘Year_Birth’, generate a density plot. Customers from around which year most frequently represented in this sample? How might this information benefit the owner of the business? If the store owner knew the year of birth of frequent customers, what action(s) might the store owner be able to take? > x <- na.omit(cpa$Year_Birth) > d <- density(x) > plot(d, + main = "Density Plot of Birth Year", + xlab="Quantity of Customers Born in Each Year", + ylab="Count") Density

STAT E-100 FA 2023 Mission 2: Consumer Psychology Knowing the birth year of frequent customers can help store owners adjust their stock, decoration, and business practices to better fit t heir customers age groups. 5) Using the variable ‘Marital_Status, generate a table listing the percentage of marital statuses. If the store owner knew the distribution of marital status across customers, what action(s) might the store owner be able to take? Marital_Status Absurd Alone Divorced Married Single Together Widow 0.08928571 0.13392857 10.35714286 38.57142857 21.42857143 25.89285714 3.43750000 YOLO Total 0.08928571 100.00000000 If a store owner had this information, they could adjust their stock and sal e s te chniques appropriately to increase sales. > tally(~Marital_Status, format = "percent", data = cpa, margins=TRUE) Purchasing Behavior 6) Using the variable ‘MntWines’, generate the mean and standard deviation of amount spent on wine by a customer. > mean(cpa$MntWines, na.rm=TRUE) $ 303.9357 > sd(cpa$MntWines, na.rm=TRUE) $ 336.5974 7) If you compare the average purchases from the web (NumWebPurchases), catalogs (NumCatalogPurchases), and in-store (NumStorePurchases), which is the highest? If the store owner knew where the orders were coming in from, what action(s) might the store owner be able to take? > mean(cpa$NumWebPurchases, na.rm=TRUE) $ 4.084821 > mean(cpa$NumCatalogPurchases, na.rm=TRUE) $ 2.662054 > mean(cpa$NumStorePurchases, na.rm=TRUE) $ 5.790179 If the store owner knows where purchase come from, then they can push catalog and web purchases more and make them more accessible so they can increase sales in that side. Density

STAT E-100 FA 2023 Mission 2: Consumer Psychology 8) What is the 95 th percentile for MntGoldProds? Explain the interpretation of this value in context to somebody who is not familiar with percentiles. > quantile(cpa$MntGoldProds, probs = c(.95), na.rm = TRUE) 95% $165.05 95th percentile means that $165.05 is a number higher than what 95% of people spend on gold products . 9) Compare the standard deviation of MntMeatProducts and MntGoldProds. Explain the interpretation of these values in context to somebody who is not familiar with the idea of a standard deviation. > sd(cpa$MntWines, na.rm=TRUE) $ 336.5974 > sd(cpa$MntGoldProds, na.rm=TRUE) $ 52.16744 T he standard deviation means that the typical gold purchase is $52 more or less than the mean. Since the standard deviation is so much higher for wine than gold, that means that people who buy wine spend wildly different amounts of money, while most people who buy gold spend similar amounts of money. 10)Using the variable ‘Recency’, generate a boxplot. Indicate the actual or approximate value of Q1 (25 th percentile), Q2 (50 th percentile), and Q3 (75 th percentile). Interpret in context what these values mean for the business. > my_boxplot <- boxplot(cpa$Recency, + main = "Last Visit Across Customers", + xlab = "Recency", + col = "lightgreen", + border = "darkgreen", + horizontal = TRUE, + notch = TRUE)

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STAT E-100 FA 2023 Mission 2: Consumer Psychology 25 th : 24 50 th :49 75 th :74 Since the median is also the 50 th percentile, both being 49 days means that most customers visit this store less than once every 7 weeks , which is not good for a business that sells daily essentials like groceries.