Customer data such as customer program accruals, various loyalty and customer value metrics.
Adding Value Through Decision Support.
The data warehouse is an enabling technology, delivering strategic decision support capabilities for specific retail business functions. Executives, marketing analysts, store managers, and others are provided with intuitive tools to distill information about corporate assets and their performance. Corporate assets may include customers, products, or services (e.g. promotions or customer loyalty initiatives). .
Some typical decision support requirements in the retail industry include:.
1. Reporting capabilities for key performance metrics such as:.
Product profitability;.
Units sold;.
Gross revenue; and.
Customer frequency and loyalty.
2. Performing complex analysis to derive measures for:.
Evaluating success, timing, and duration of promotion campaigns;.
Evaluating shopper buying patterns and products, i.e., market basket analysis;.
Determining optimal forward buying opportunities;.
Determining optimal assortment mix by category;.
Evaluating pricing and promotion strategy by category; and.
Understanding issues and measuring improvements in merchandise flow.
3. Developing statistical models that predict customer needs and behaviors.
For example, you can build models that predict a customer's likelihood to:.
Buy a new product;.
Generate high profitability;.
Respond to contacts through specific channels (e.g., direct mail, telemarketing, email, etc.); and.
Remain loyal to products in the face of variables such as price, availability, etc.
Putting Decision Support to Work.
Now that we have discussed the decision support capabilities that will be crucial to surviving in tomorrow's business landscape, let's take a look at how these capabilities align to the classic needs of the business. Consider the case of adding new products to the inventory. A new product is under consideration for introduction into a chain of retail stores.
