Teppei Syokudo - Improving Store Performance: PPA Data Analysis Methodology
Data Exploration
Looking at both MW and RP’s product sales, the sale of Main - Meal has a decreasing trend, together with Main - Drink. This is likely due to the introduction of Set Menus, where customers tend to prefer purchasing sets rather than ala carte. The indirect relationship between Main - Meal and Set Menu is a lot more obvious in RP. We can see that the moment Set Menu was introduced in November, Main - Meal sales started dropping.
The most popular Main - Meal in both outlets would be the Kaisendon, which is sold 60 times and 50 to 60 times daily on average in MW and RP respectively. It’s relating set is the Seafood Feast which averages 40 and 24 times daily in MW and RP respectively.
We can also see that the sale of Main - Onigiri and Main - Fried have relatively stagnant to decreasing trends in both outlets. This means that the onigiris and fried items may not be very popular items. In order to boost sales of onigiris and fried items, Teppei Syokudo may want to consider introducing onigiri sets and fried item sets.
Data
Traditionally data related to Market Basket Analysis is three-dimensional: Customers, Orders (i.e. item sets, purchases or baskets), and Items (Beery and Linoff, 2004). A sales order is a most essential and basic piece of information representing a single purchase or transaction made by a customer. Besides main information such as the product bought, the quantity of products bought and total amount of the purchase, the store number, cashier number, type of payment or even the cashier who served is also stored in the order data. The items or rather the contents of the order is most important and founds the basis of identification of association rules. Last but not least, customer information provides a deeper level of analysis by finding associations between certain customer traits and profiles and particular items, allowing the store to carry out market segmentation. (Ting, Pan and Chou, 2010).
A market basket database typically consists of a large number of transaction records. Each record lists all items purchased during a single customer transaction. The objective of this data mining exercise is to identify if certain groups of items are usually purchased together, providing meaningful association rules.
Analysis Tool Selection
In carrying out Market Basket Analysis certain considerations have to be made. One important factor is the software or tool used to carry out Market Basket Analysis. Based on the client requirements in this project, the tool used must be one that is open-source and easy to use. While the team understands that there are far greater utility in employing paid software such as Clementine (SPSS), Enterprise Miner (SAS), GhostMiner 1.0, Quadstone or XLMiner, this requirement essentially narrows down the tools that the team is able to use (Haughton et. al., 2003). The tools that are open-source are narrowed down into 3 tools: RapidMiner, R and Tanagra.
