2.6 Build final data

Bringing customers, products and transactions together is the final step of generating synthetic data. This process entails 3 steps as given below.

2.6.1 Allocate customers to transactions

The allocation of transactions is achieved with the help of buildPareto function. This function takes 3 arguments as detailed below.

factor1 and factor2
These are factors to be mapped to each other. As the name suggests, they must be of data type factor.

Pareto
This defines the percentage allocation and is a numeric data type. This argument takes the form of c(x,y) where x and y are numeric and their sum is 100. If we set Pareto to c(80,20), it then allocates 80 percent of factor1 to 20 percent of factor 2. This is based on a well-known concept of Pareto principle.

Let us now allocate transactions to customers first by using the following code.

customer2transaction <- buildPareto(customers, transactions$transactionID, pareto = c(80,20))

Assign readable names to the output by using the following code.

names(customer2transaction) <- c('transactionID', 'customer')

2.6.2 Allocate products to product hierarchy

The products can be allocated to product hierarchy as follows

#First step is to ensure that the product hierarchy data frame has the same number of rows as number of products.
category <- productHierarchy$category
subcategory <- productHierarchy$subcategory
productHierarchy <- as.data.frame(cbind(category,subcategory,1:nrow(products)))
#> Warning in cbind(category, subcategory, 1:nrow(products)): number of rows of
#> result is not a multiple of vector length (arg 1)
#Randomly assign the product hierarchy to the products. Ensure that the additional unused variable towards the end is dropped.
products <- cbind(products, productHierarchy[,c("category","subcategory")])

2.6.3 Allocate products to transactions

Now, using similar step as mentioned above, allocate transactions to products using following code.

product2transaction <- buildPareto(products$SKU,transactions$transactionID,pareto = c(70,30))
names(product2transaction) <- c('transactionID', 'SKU')

2.6.4 Combine customers and transactions data

The following code brings together transactions, products and customers into one dataframe.

df1 <- merge(x = customer2transaction, y = product2transaction, by = "transactionID")
df2 <- merge(x = df1, y = transactions, by = "transactionID", all.x = TRUE)

2.6.5 Final data

We can add additional data such as customer name using the code below.

df3 <- merge(x = df2, y = customer2name, by.x = "customer", by.y = "customers", all.x = TRUE)
df4 <- merge(x = df3, y = customer2age, by.x = "customer", by.y = "customers", all.x = TRUE)
df5 <- merge(x = df4, y = customer2phone, by.x = "customer", by.y = "customers", all.x = TRUE)
df6 <- merge(x = df5, y = products, by = "SKU", all.x = TRUE)
dfFinal <- df6[,c("dayNum", "mthNum", "customer", "customerName", "customerAge", "customerPhone", "transactionID", "SKU", "Price", "category","subcategory")]

Thus, we have the final data set with transactions, customers and products.

Interpret the results
The column names of the final data frame can be interpreted as follows.

• Each row is a transaction and the data frame has all the transactions for a year i.e 365 days.
• dayNum is the day number in the year. There would be 365 unique dayNum in the data frame.
• mthNum is the month number. This ranges from 1 to 12 and represents January to December respectively.
• customer is the unique customer identifier. This is the customer who made that transaction.
• customerName is name of the customer.
• customerAge is the age of the customer.
• customerPhone is the phone number of the customer.
• transactionID is the unique identifier for that transaction.
• SKU is the product ID that was bought in that transaction.
• Price is the price of the product.
• category is the product category.
• subcategory is the product subcategory.