I wanted to save my largest Tableau visualizations for my final project, so I wanted to start small when creating my first data visualizations.
I had two sets of data to work with. One was a 200 row data set containing (almost) all sneaker releases from Nike and Adidas from Q1 2018. The other data set was a ~50,000 line data set containing StockX resale data for each of these shoes that were released in Q1. I have the brand, the shoe SKU, the shoe description, the original selling price, the resale price, and the resale size.
I was lucky as Tableau makes it easy to create a join on your data, so I was able to quick merge my two data sets on my sneaker SKU to combine my data sets to create a more meaningful data set.
I really did start small here. I made a line graph showing how many women’s only releases each brand had in Q1 (see below).
As you can see, this was simple: my column was the brands and the rows were a count of the sneakers. The reason I did a count of sneaker description, rather than SKU, was because I created a filter that only showed those sneaker descriptions containing “women”. This may not have been the most accurate representation, but it was the best I could do given the data I had.
I then created a pie chart to see which brand had more shoes resold. While I didn’t do a count of how many shoes each brand released in Q1, Nike clearly released more as they released almost a new shoe every day, if not more. That being said, the more original releases, the more resales. Obviously this isn’t necessarily fair, but you can see how this turned out in the pie chart below.
As you can see, 76% of sneaker resales were Nike,while only 23.5% were Adidas. I simply added brands and a count of the resales, but then did a calculation for percentage of the whole.
Once I got the hang of Tableau basics, I was able to make the visualization below. As someone who resells shoes myself, I constantly struggle when deciding which shoe size to buy at retail to resell later. I have always purchased a 10 because I assumed it was the most common men’s shoe size (this was literally a guess on my part).
I was close, but not quite! Based on the set that I sampled, 11 was actually the most frequently resold shoe size, followed by 10.5 and 10. Those smaller dots without labels are a combination of smaller mens’ sizes and womens’ sizes; as you can see, there isn’t a single women’s size large enough to be labeled! That being said, this type of analysis is probably only useful for men’s sizing, but still useful nonetheless. Now I know to go for size 11’s when buying to resell!
The only downside to the graph above is that the smaller data gets somewhat neglected, so this type of chart is really only useful if you want to see where most of your data is concentrated.
For my final project, I will analyze the resale values of each brand in comparison to their original prices and use forecasting and trendlines to see which brand will most likely come out on top.