The athletic gear industry is known for its segmentation schemes. One of the most common is the channel/level segmentation. To those from the outside it can be a little daunting to differentiate accounts accross different channels and levels, specially when these accounts could very well live in the same shopping center or mall.

There are five main channels in this example presentation

• Sporting Goods: A door with attention to hard and soft sporting goods
• Athletic Specialty: Focus on sport fashion, specially footwear
• Category Specialty: Door focused on one specific sport (i.e. tennis)
• Digital: E-commerce
• Department Store: A door with ample categories, from furniture to shoes
• Family Footwear: A door focused just on footwear

The quality of an account in the athletic retail world is given by

1. The sales volume in a year
2. The frequency of shipments

Ideally accounts will ship often (more than 6 times a year, preferably every 45 days) and commit to certain sales volumes. Channel Managers will be interested in the level of sales withing their specific channels, while Product Managers will be more interested in the frequency and volume of sales, since it is a financially more sound valuation. But how can someone map this information on a plot, and make it easy for each function to study patterns of accounts? If done in EXCEL, one could sort by sales and frequency pivoting by channel segmentation, but the relative position in the map is then lost. If we could do this in one plot where people could identify the account by location, frequency, sales volume and channel, it would be a no brainer for managers to further the analysis.

For those interested in the source code, we provided it below

library(plotly)
library(ggplot2)
data <- read.csv("Col2015-2016Frequencies.csv")

data$sales <- data$sales_2015 + data$sales_2016
data$frequency <- data$frequency_2015 + data$frequency_2016

plot_ly(x = data$sales, y = data$frequency, mode = "markers", text = paste("ACT: ",data$account), 
        hoverinfo="text+x+y", color = data$channel, size = data$sales) %>%
   layout(title = 'Market Sales and Frequency',
         yaxis = list(title = "Frequency of Shipments"),
         xaxis = list(title = "Sales Volume USD"))

Thanks a lot!

Ariel Meilij - Panama Republic