Data science and machine learning are very much applicable in such huge and various fields. One of them is retail business. Imagine if you are a retail business owner who owns a retail shop that sells hundreds of items. In a single month, there are more than a hundred transactions occurring in your shop, for instance. Each transaction is usually made with more than an item to be bought. It means there are usually more than an item in a single transaction.
In the end of the month, you would like to increase the sales in your shop, so you are trying to figure out which ways is the best to create promotions or to apply discounts on particular items. You start asking your team a questions, which products should be discounted? How do we apply the discounts? Are there any patterns that occur to our sales, so that we could apply the promotion more accurately?
In this articles, we’ve done a Market Basket Analysis using an actual online retail transaction data from UK. The result of this market basket analysis could be used for a data-driven marketing strategy and decision making. In this datasets, we could generates several business insights as follows :- Item Placements. We could put ROSES REGENCY TEACUP AND SAUCER and GREEN REGENCY TEACUP AND SAUCER in a closer place, maybe in a same shelf or any other closer place.
- Products Bundling. We could put ROSES REGENCY TEACUP AND SAUCER and GREEN REGENCY TEACUP AND SAUCER as a single bundle of product with a lower price compare to each price combined. This way will attract more sales and generates more income.
- Customer Recommendation and Discounts. We could put Roses Regency Teacup and Saucer in the cashier, so that every time a customer bought Green Regency Teacup and Saucer, we could offer and recommend them to buy Roses Regency Teacup and Saucer with a lower price.