Data Warehousing by Industry Chapter 4 e-Data

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

Typical Uses of Data Warehousing in Retail n Market Basket Analysis ¨ Refer n 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 n Display space, product placement. . .

Typical Uses of Data Warehousing in Retail n 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

Typical Uses of Data Warehousing in Retail n 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

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

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

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

Uses of Data Warehousing in Financial Services n 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

Uses of Data Warehousing in Financial Services n 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 n Examines historical customer behavior to verify that no past defaults have occurred n ¨ Ever gotten a call from you credit card company asking about a recent purchase?

Uses of Data Warehousing in Financial Services n 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 n Example: ¨ Loan for college tuition may mean a graduation gift or wedding in the future

Uses of Data Warehousing in Financial Services n 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

Uses of Data Warehousing in Financial Services n 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

The Good News and Bad News in Financial Services n 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 n Bad News ¨ Deregulation, mergers, changing demographics and nontraditional competitors n Royal Bank of Canada

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

Uses of Data Warehousing in Telecommunications n 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 n Packaging of vertical features ¨ Voice products such as caller ID, call waiting ¨ Employ price elasticity models to determine the new package's optimal price

Uses of Data Warehousing in Telecommunications n 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 n Historical activity analysis can help telecommunications companies predict equipment outages before they occur

Uses of Data Warehousing in Telecommunications n 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

Uses of Data Warehousing in Telecommunications n Customer Satisfaction

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