Data Warehousing by Industry

Data Warehousing by IndustryChapter 4e-Data

RetailData warehousings early adopters

Capturing data from their POS systemsPOS = 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).

Typical Uses of Data Warehousing in RetailMarket Basket AnalysisRefer to p. 79, Table 4-1In-Store PlacementUse 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 suppliersDisplay space, product placement . . .

Typical Uses of Data Warehousing in RetailProduct PricingPrice elasticity models manipulate detailed data to determine not only the best price, but often different prices for the same product according to different variablesPermits differential pricing

Typical Uses of Data Warehousing in RetailProduct Movement and Supply chainAnalyzing the movement of specific products and the quantity of products sold helps retailers predict when they will need to order more stockProduct sales history allows merchandisers to define which products to order, the max # of units and the frequency of reordersAutomatic replenishment with JIT delivery

The Good News and Bad News in RetailingGood NewsRetailers 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

The Good News and Bad News in RetailingBad NewsThe lack of success measurementNot using the data warehouse to its fullest potential

Hallmark

Financial ServicesThe pioneers of the data warehouseBusiness intelligence has become a business mandate as well as a competitive weapon.1999 Financial Services Modernization ActRequires financial service and insurance companies to disclose how they will use data collected from their customer

Uses of Data Warehousing in Financial ServicesProfitability analysisCannot know the true value of a customer without understanding how profitable that customer isFigure 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 ServicesRisk Management and Fraud PreventionDW provides a banking compnay with a scientific approach to risk managementHelps pinpoint specific market or customer segment that may be higher risk than othersExamines historical customer behavior to verify that no past defaults have occurredEver gotten a call from you credit card company asking about a recent purchase?

Uses of Data Warehousing in Financial ServicesPropensity Analysis and Event-Driven MarketingHelps bank recognize whether a customer is likely to purchase a given product and service, and even when such a purchase might occurExample:Loan for college tuition may mean a graduation gift or wedding in the future

Uses of Data Warehousing in Financial ServicesResponse and Duration ModelingCan tell a bank which customers are likely to respond to a given promotion and purchase the advertised product or serviceHow long a customer might keep a credit card and also how often the card will be used

Uses of Data Warehousing in Financial ServicesDistribution Analysis and PlanningBy 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 ServicesGood NewsLess of a training curve because banks have been monitoring trends and fluctuations in data long before the DWRegular users of decision supportBad NewsDeregulation, mergers, changing demographics and nontraditional competitorsRoyal Bank of Canada

Uses of Data Warehousing in TelecommunicationsChurnDifferentiate between the propensity to churn and actual churnDifferentiate between product church and customer churnFraud DetectionData mining tools can predict fraud by spotting patterns in consolidated customer information and call detail records

Uses of Data Warehousing in TelecommunicationsProduct Packaging and Custom PricingUsing 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 themPackaging of vertical featuresVoice products such as caller ID, call waitingEmploy price elasticity models to determine the new package's optimal price

Uses of Data Warehousing in TelecommunicationsNetwork Feature ManagementBy 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 callsHistorical activity analysis can help telecommunications companies predict equipment outages before they occur

Uses of Data Warehousing in TelecommunicationsCall Detail AnalysisAnalysis of specific call recordsHelps provide powerful information about origin and destination patterns that could spur additional sales to important customers

Uses of Data Warehousing in TelecommunicationsCustomer Satisfaction

The Good News and Bad News in TelecommunicationsBad NewsMany arent effectively leveraging the information from their data warehouses once they obtain it

GTE (p. 103)