This video represents part two in my Market Basket Analysis series.
The steps in the post were inspired by the book Tableau Unlimited written by former co-worker of mine, Chandraish Sinha. I wasn’t planning to construct another market basket analysis video but when I saw the approach outlined in his book, I felt like it warranted sharing with my readers and followers.
In this version we’ll use default Tableau Superstore data to show the relationship between sub-categories on an Order; all without using a self table join. The visualization and analysis is driven by a user selection parameter.
Once the user selects a sub-category, the bar chart visualization updates to reflect the number of associated sub-category items on the same order.
Watch the video and as always get out there and do some great things with your data!
Feel free to also check out Part 1 here where we create a simpler correlation matrix version that shows all the sub-category relationships in one visual.
A favored analysis technique employed by retailers to help them understand the purchase behavior of their customers is the market basket analysis. When you log on to Amazon, most likely you’ve noticed the “Frequently Bought Together” section where Jeff Bezos and company would like to cross-sell you additional products based upon the purchase history of other people who have purchased the same item.
Market Basket Analysis influences how retailers institute sales promotions, loyalty programs, cross-selling/up-selling and even store layouts.
If a retailer observes that most people who purchase Coca-Cola also purchase a package of Doritos (I know they’re competing companies), then it may not make sense to discount both items at once as the consumer might have purchased the associated item at full price anyhow. Understanding the correlation between products is powerful information.
In this video, we’ll use Tableau Superstore data to perform a simple market basket analysis.
Watch the video and as always get out there and do some great things with your data.
Feel free to also check out Part 2 here where we’ll create an analysis driven by a user selection parameter.