1. Data Warehousing by Industry Chapter 4 e-Data

2. Retail Data warehousing’s early adopters Capturing data from their POS systems  POS = point-of-sale Industry analysts predict that brick-and- mortal retailers will see a slowdown in sales growth over the next several years (Silverman, 1998).

3. Typical Uses of Data Warehousing in Retail Market Basket Analysis  Refer to p. 79, Table 4-1 In-Store Placement  Use decision support to understand which items are being purchased, where they belong, and modify configurations in order to maximize the # of items in the market basket.  Retailers are able to negotiate more effectively with their suppliers Display space, product placement...

4. Typical Uses of Data Warehousing in Retail Product Pricing  Price elasticity models manipulate detailed data to determine not only the best price, but often different prices for the same product according to different variables  Permits differential pricing

5. Typical Uses of Data Warehousing in Retail Product Movement and Supply chain  Analyzing the movement of specific products and the quantity of products sold helps retailers predict when they will need to order more stock  Product sales history allows merchandisers to define which products to order, the max # of units and the frequency of reorders  Automatic replenishment with JIT delivery

6. The Good News and Bad News in Retailing Good News  Retailers are the most open to trying out new analysis techniques and adopting state of the art tools to enable discover of new information about customers, their purchases, and the most likely avenues to maximize profitability

7. The Good News and Bad News in Retailing Bad News  The lack of success measurement  Not using the data warehouse to its fullest potential Hallmark

8. Financial Services The pioneers of the data warehouse Business intelligence has become a business mandate as well as a competitive weapon. 1999 Financial Services Modernization Act  Requires financial service and insurance companies to disclose how they will use data collected from their customer

9. Uses of Data Warehousing in Financial Services Profitability analysis  Cannot know the true value of a customer without understanding how profitable that customer is  Figure 4.2: Customer Profitability Analysis (p. 87)  Used by many banks to help dictate the creation of new products or the expunging of old ones

10. Uses of Data Warehousing in Financial Services Risk Management and Fraud Prevention  DW provides a banking compnay with a scientific approach to risk management Helps pinpoint specific market or customer segment that may be higher risk than others Examines historical customer behavior to verify that no past defaults have occurred  Ever gotten a call from you credit card company asking about a recent purchase?

11. Uses of Data Warehousing in Financial Services Propensity Analysis and Event-Driven Marketing  Helps bank recognize whether a customer is likely to purchase a given product and service, and even when such a purchase might occur Example:  Loan for college tuition may mean a graduation gift or wedding in the future

12. Uses of Data Warehousing in Financial Services Response and Duration Modeling  Can tell a bank which customers are likely to respond to a given promotion and purchase the advertised product or service  How long a customer might keep a credit card and also how often the card will be used

13. Uses of Data Warehousing in Financial Services Distribution Analysis and Planning  By understanding how and where customers perform their transactions, banks can tailor certain locations to specific customer groups.  Allows banks to make decisions about branch layouts, staff increases or reductions, new technology additions or even closing or consolidating low-traffic branches

14. The Good News and Bad News in Financial Services Good News  Less of a training curve because banks have been monitoring trends and fluctuations in data long before the DW  Regular users of decision support Bad News  Deregulation, mergers, changing demographics and nontraditional competitors Royal Bank of Canada

15. Uses of Data Warehousing in Telecommunications Churn  Differentiate between the propensity to churn and actual churn  Differentiate between product church and customer churn Fraud Detection  Data mining tools can predict fraud by spotting patterns in consolidated customer information and call detail records

16. Uses of Data Warehousing in Telecommunications Product Packaging and Custom Pricing  Using knowledge discover and modeling, companies can tell which products will see well together, as well as which customers or customer segments are most likely to buy them Packaging of vertical features  Voice products such as caller ID, call waiting  Employ price elasticity models to determine the new package's optimal price

17. Uses of Data Warehousing in Telecommunications Network Feature Management  By monitoring call patterns and traffic routing, a carrier can install a switch or cell in a location where it is liable to route the maximum amount of calls Historical activity analysis can help telecommunications companies predict equipment outages before they occur

18. Uses of Data Warehousing in Telecommunications Call Detail Analysis  Analysis of specific call records  Helps provide powerful information about origin and destination patterns that could spur additional sales to important customers

19. Uses of Data Warehousing in Telecommunications Customer Satisfaction

20. The Good News and Bad News in Telecommunications Bad News  Many aren’t effectively leveraging the information from their data warehouses once they obtain it GTE (p. 103)