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Market-Basket-Analysis

"Customers who purchased this product also viewed or purchased these products...."

Look familiar? This type of data science is called Market Basket Analysis, which requires a deep investigation of apriori product rules that can help lead to online cross-selling and recommender systems. Not only do I find product associations in this analysis, but I also translate findings to actionable insights for an online retailer, applicability of which is often lacking in other published market basket analyses and tutorials.

  • Please see Market-Basket-Analysis.md for full analysis, scroll down for full business insights
  • Also available on R Studio's RPubs at https://rpubs.com/brosnahj/MarketBasket
  • See 'Market Basket Report.pdf' for business insight report provided to company

Background

We have been asked by Blackwell Electronics to conduct a Market Basket Analysis on 30 days worth of transactions. Based on this market basket analysis, we will provide insights and purchasing patterns in order to provide product recommendations for cross-selling and recommender opportunities.

Objective

The purpose of this project is to conduct a Market Basket Analysis to gain insights on customer electronic transactions and find patterns or product relationships. Not only will we apply rules and gain insights, but we will also provide key business insights related to our findings. The following questions will be addressed:

  • Are there any interesting patterns or item relationships within Blackwell Electronics transactions?
  • Based on your market basket analysis, what product recommendations do you have for Blackwell?

Data Description

The dataset contains 30 days’ worth of Blackwell Electronics online transactions. Each row is a transaction, which contains only names of items purchased. There are no other variables, numeric or otherwise.

Analysis plan

We will use basket formatting and apply the apriori algorithm to determine relationships between products.

Actionable Insights for Blackwell:

  • Initiate recommender systems for the following products listed within these item sets frequently bought together:
    • Acer Aspire, ViewSonic Monitor, HP Laptop
    • Dell Desktop, ViewSonic Monitor, HP Laptop
    • CYBERPOWER Gamer Desktop, ViewSonic Monitor, HP Laptop
    • ASUS Monitor, HP Laptop, iMac
    • ASUS 2 Monitor, HP Laptop, iMac
    • HP Laptop, Microsoft Office Home and Student 2016, iMac
    • CYBERPOWER Gamer Desktop, ViewSonic Monitor, iMac
    • HP Laptop, HP Monitor, iMac
  • Send promotional emails to customers who buy specific products within any of the above item sets bulleted above, informing them of products likely to be interesting to them.
  • Recommend ViewSonic and ASUS 2 Monitors as items customers also view each time a desktop computer is viewed or added to cart.
  • Investigate returns of desktops purchased alongside other desktop brands. If customers are more often keeping all desktops, then initiate recommender systems and email promotions as items customers frequently purchase together.
  • Discount the 20 lowest selling electronic products alongside other items recommended to customers in an effort to liquidate products not selling.

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