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For example, a retail chain's finding that its same-store sales at location A for the week-long shopping rush before Christmas are greater than those at location B is a useful piece of data. That data would have been less useful if only chain-wide sales for that week were known (with all stores averaged together), or if only year-long sales ...
Some of the items sold at sample sales are from previous seasons, overstocked items, returned, or were never sold in stores. [1] Sample sales are particularly popular in the bridal industry. Bridal salons typically carry two collections per year, and so often offer their in-store sample dresses at a discount in order to make room for new bridal ...
If the business process is sales, then the corresponding fact table will typically contain columns representing both raw facts and aggregations in rows such as: $12,000, being "sales for New York store for 15-Jan-2005". $34,000, being "sales for Los Angeles store for 15-Jan-2005" $22,000, being "sales for New York store for 16-Jan-2005"
Retail reading. The final monthly retail sales report before the start of the holiday shopping season is set for release on Thursday. Economists estimate retail sales increased 0.3% over the prior ...
Same-store sales dropped 1.30%, less than the estimated 3.01% decline. ... Shares of Lowe's are up 20% year to date, compared to the S&P 500's 24% gain, according to Yahoo Finance Data. Rival Home ...
Consider a database of sales, perhaps from a store chain, classified by date, store and product. The image of the schema to the right is a star schema version of the sample schema provided in the snowflake schema article. Fact_Sales is the fact table and there are three dimension tables Dim_Date, Dim_Store and Dim_Product.
Sales data, presented in a graphic format, can provide regular sales trend information and highlight whether certain customer types need to be targeted or focused. Price information by product line, compare with competitors, can monitor market trends; analyzed by customer type, it can check price trends in customer groups.
Spatial data mining is the application of data mining methods to spatial data. The end objective of spatial data mining is to find patterns in data with respect to geography. So far, data mining and Geographic Information Systems (GIS) have existed as two separate technologies, each with its own methods, traditions, and approaches to ...