Data Warehousing by Industry

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)