A/B Testing For Fast Food Marketing Campaign
Stephen Situ
2022-11-26
This is a project on A/B Testing for a Fast Food Marketing Campaign. 3 Promotions were offered to different stores with varying MarketID, MarketSize, LocationID, and AgeOfStore. Sales figures were collected over 4 weeks. We first calculated the percentage increase/decrease between each weekly period. Then we defined our KPI as the average of this weekly increase/decrease. We find that Promotion 1 was the most effective in increasing sales and used an ANOVA test with alpha = 0.05 to show that there was no significant statistical evidence to suggest that the means/variance of the 3 groups were different.
Original DataSet: https://www.kaggle.com/datasets/chebotinaa/fast-food-marketing-campaign-ab-test
Install Packages
install.packages('tidyverse', repos = "http://cran.us.r-project.org")## Installing package into 'C:/Users/Steve/AppData/Local/R/win-library/4.2'
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## C:\Users\Steve\AppData\Local\Temp\RtmpQD4khU\downloaded_packageslibrary(tidyverse)## ── Attaching packages
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## ✔ tidyr 1.2.1 ✔ stringr 1.4.1
## ✔ readr 2.1.3 ✔ forcats 0.5.2
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## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag() masks stats::lag()install.packages('ggplot2', repos = "http://cran.us.r-project.org")## Warning: package 'ggplot2' is in use and will not be installedlibrary(ggplot2)Read CSV
ff_df <- read.csv('WA_Marketing-Campaign.csv')Structure
summary(ff_df)## MarketID MarketSize LocationID AgeOfStore
## Min. : 1.000 Length:548 Min. : 1.0 Min. : 1.000
## 1st Qu.: 3.000 Class :character 1st Qu.:216.0 1st Qu.: 4.000
## Median : 6.000 Mode :character Median :504.0 Median : 7.000
## Mean : 5.715 Mean :479.7 Mean : 8.504
## 3rd Qu.: 8.000 3rd Qu.:708.0 3rd Qu.:12.000
## Max. :10.000 Max. :920.0 Max. :28.000
## Promotion week SalesInThousands
## Min. :1.000 Min. :1.00 Min. :17.34
## 1st Qu.:1.000 1st Qu.:1.75 1st Qu.:42.55
## Median :2.000 Median :2.50 Median :50.20
## Mean :2.029 Mean :2.50 Mean :53.47
## 3rd Qu.:3.000 3rd Qu.:3.25 3rd Qu.:60.48
## Max. :3.000 Max. :4.00 Max. :99.65str(ff_df)## 'data.frame': 548 obs. of 7 variables:
## $ MarketID : int 1 1 1 1 1 1 1 1 1 1 ...
## $ MarketSize : chr "Medium" "Medium" "Medium" "Medium" ...
## $ LocationID : int 1 1 1 1 2 2 2 2 3 3 ...
## $ AgeOfStore : int 4 4 4 4 5 5 5 5 12 12 ...
## $ Promotion : int 3 3 3 3 2 2 2 2 1 1 ...
## $ week : int 1 2 3 4 1 2 3 4 1 2 ...
## $ SalesInThousands: num 33.7 35.7 29 39.2 27.8 ...We Convert Promotion, Market Size, and Market ID to factors
ff_df_true = ff_df
ff_df_true$Promotions = as.factor(ff_df_true$Promotion)
ff_df_true$MarketSizes = as.factor(ff_df_true$MarketSize)
ff_df_true$MarketIDs = as.factor(ff_df_true$MarketID)
ff_df_true1 = ff_df_true %>% select(-1,-2,-5)
ff_df_true1## LocationID AgeOfStore week SalesInThousands Promotions MarketSizes
## 1 1 4 1 33.73 3 Medium
## 2 1 4 2 35.67 3 Medium
## 3 1 4 3 29.03 3 Medium
## 4 1 4 4 39.25 3 Medium
## 5 2 5 1 27.81 2 Medium
## 6 2 5 2 34.67 2 Medium
## 7 2 5 3 27.98 2 Medium
## 8 2 5 4 27.72 2 Medium
## 9 3 12 1 44.54 1 Medium
## 10 3 12 2 37.94 1 Medium
## 11 3 12 3 45.49 1 Medium
## 12 3 12 4 34.75 1 Medium
## 13 4 1 1 39.28 2 Medium
## 14 4 1 2 39.80 2 Medium
## 15 4 1 3 24.77 2 Medium
## 16 4 1 4 30.98 2 Medium
## 17 5 10 1 30.37 2 Medium
## 18 5 10 2 24.82 2 Medium
## 19 5 10 3 37.47 2 Medium
## 20 5 10 4 23.35 2 Medium
## 21 6 10 1 32.90 3 Medium
## 22 6 10 2 22.18 3 Medium
## 23 6 10 3 42.98 3 Medium
## 24 6 10 4 26.68 3 Medium
## 25 7 15 1 42.92 1 Medium
## 26 7 15 2 42.16 1 Medium
## 27 7 15 3 51.72 1 Medium
## 28 7 15 4 36.17 1 Medium
## 29 8 10 1 30.08 2 Medium
## 30 8 10 2 28.62 2 Medium
## 31 8 10 3 25.40 2 Medium
## 32 8 10 4 27.26 2 Medium
## 33 9 6 1 37.41 1 Medium
## 34 9 6 2 38.64 1 Medium
## 35 9 6 3 40.90 1 Medium
## 36 9 6 4 41.11 1 Medium
## 37 10 5 1 34.27 2 Medium
## 38 10 5 2 29.30 2 Medium
## 39 10 5 3 23.93 2 Medium
## 40 10 5 4 35.16 2 Medium
## 41 11 5 1 33.42 3 Medium
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## 548 10We pivot week column up and create new columns filled with sales value
ff_df_1 <- ff_df_true1 %>% pivot_wider(id_cols = c('MarketIDs','MarketSizes','LocationID','AgeOfStore','Promotions') , names_from = week , values_from = SalesInThousands)
ff_df_2 <- ff_df_1 %>% rename("Week_1" = "1", "Week_2" = "2", "Week_3" = "3", "Week_4" = "4")
ff_df_2## # A tibble: 137 × 9
## MarketIDs MarketSizes LocationID AgeOfS…¹ Promo…² Week_1 Week_2 Week_3 Week_4
## <fct> <fct> <int> <int> <fct> <dbl> <dbl> <dbl> <dbl>
## 1 1 Medium 1 4 3 33.7 35.7 29.0 39.2
## 2 1 Medium 2 5 2 27.8 34.7 28.0 27.7
## 3 1 Medium 3 12 1 44.5 37.9 45.5 34.8
## 4 1 Medium 4 1 2 39.3 39.8 24.8 31.0
## 5 1 Medium 5 10 2 30.4 24.8 37.5 23.4
## 6 1 Medium 6 10 3 32.9 22.2 43.0 26.7
## 7 1 Medium 7 15 1 42.9 42.2 51.7 36.2
## 8 1 Medium 8 10 2 30.1 28.6 25.4 27.3
## 9 1 Medium 9 6 1 37.4 38.6 40.9 41.1
## 10 1 Medium 10 5 2 34.3 29.3 23.9 35.2
## # … with 127 more rows, and abbreviated variable names ¹AgeOfStore, ²PromotionsWe Calculate the increase in sales percentage From week to week and then average over the 3 week period to get our KPI
Key Performance Indicator = Average Increase in Sales Percentage / week
ff_df_3 <- mutate(ff_df_2,
week1_2 = ((Week_2 - Week_1)/Week_1)*100,
week2_3 = ((Week_3 - Week_2)/Week_2)*100,
week3_4 = ((Week_4 - Week_3)/Week_3)*100)
ff_df_4 <- mutate(ff_df_3, KPI = (week1_2 + week2_3 + week3_4 )/3)
ff_df_4 %>% select(Promotions,KPI)## # A tibble: 137 × 2
## Promotions KPI
## <fct> <dbl>
## 1 3 7.45
## 2 2 1.48
## 3 1 -6.18
## 4 2 -3.79
## 5 2 -1.66
## 6 3 7.76
## 7 1 -3.05
## 8 2 -2.93
## 9 1 3.22
## 10 2 4.70
## # … with 127 more rowsWe Create a boxplot of this KPI by promotion
ggplot(data=ff_df_4) + geom_boxplot(mapping=aes(x=Promotions, y=KPI)) + ggtitle("Box Plot of KPI vs Promotions") + theme(plot.title = element_text(hjust = 0.5))
We notice 3 outlier points and filter them out
ff_df_5 <- ff_df_4 %>% filter(KPI <= 15 & KPI>-15)
ggplot(data=ff_df_5) + geom_boxplot(mapping=aes(x=Promotions, y=KPI)) + ggtitle("Box Plot of KPI vs Promotions removing outliers") + theme(plot.title = element_text(hjust = 0.5))
Group By Promotion
ff_df_6 <- ff_df_5 %>% group_by(Promotions) %>% summarise( KPIs = mean(KPI))
ff_df_6## # A tibble: 3 × 2
## Promotions KPIs
## <fct> <dbl>
## 1 1 1.23
## 2 2 0.478
## 3 3 0.975We find that Promotion 1 had a 1.23% increase in sales, Promotion 2 had a 0.47% increase in sales, and Promotion 3 had a 0.97% increase in sales across
all categories (all increases are per week). Therefore, we recommend to use Promotion 1 for the greatest increase in sales.
Now we conduct an ANOVA test for multi-group variance
Null Hypothesis = There is no difference in the mean between groups of of a population
Alternative Hypothesis = At least 2 group means differ from each other in the population
We conduct ANOVA test
promotion_aov <- aov(KPI ~ Promotions, data=ff_df_5)
summary(promotion_aov)## Df Sum Sq Mean Sq F value Pr(>F)
## Promotions 2 13 6.504 0.309 0.735
## Residuals 131 2756 21.034