Spatially enabling high-end business applications
Xavier R. Lopez
Ph.D.
Sr. Product Manager
Oracle Corporation
USA
Introduction:
Organizations have long been data rich and information poor, resembling information
wastelands incapable of providing useful business intelligence to management either
because of incompatible datasets and dysfunctional networks or as a result of
ambiguity of definitions leading to irrelevant data. The ability to now store spatial
data in enterprise heralds the emergence of a new capability for spatially enabling
high-end business applications like: customer care, data warehousing, ERP,
financial, and marketing. In short, we are now moving toward a strategy of
empowering end-users by giving them the information they need and when they
need it. This paper will look at the server-based capabilities necessary for carrying
this out.
Demand for Business Intelligence Tools
Understanding and responding to customer demands for products and services is an
important business driver in the telecommunications and utilities space. Until
recently, telecommunication and utilities focused on gaining market share through
mass marketing of products and services. This value-proposition was predicated on
increased advertising and marketing costs while discounting products and services.
While this has, in some cases, led to increased market share, customer loyalty is not
guaranteed. With recent changes in deregulation, customers are no longer locked
into a single utility provider. They now have more power in the market to switch
providers in response to alternative services or competitor discounts. Since the
switching costs associated with a competitor's alternative are relatively low,
traditional service providers are watching their disconnect rates soar as customers,
and hence, profits hemorrhage.
In response, organizations are looking to utilize their information and technological
assets to reduce churn and increase customer loyalty. Ana/ytica/ information
systems that generates business intelligence about customers, who they are, where
they live, usage patterns, elasticity to demand, and some knowledge of what kinds of
services they expect are key to increasing customer care. Deregulating utilities are
now expending large efforts to sustain their customer base, while new start-ups
aggressively market products and services to capture and retain new customers. In
this environment, organizations must anticipate and meet very high customer
expectations for unique products and services.
Data warehousing provides the underlying technology for supporting such analytical
business functions. The decision support functions of successful business in today's
hyper-competitive environment demands that organizations leverage their
organizational information systems to meet analytical and strategic objectives. Some
common decision support and analysis functions are listed in Table 1.
Table 1. Decision Support Functions
targeted marketing
customer complaint analysis
network utilization
proximity analysis
management reports
service area analysis |
market trend analysis
competitive territory analysis
competitor location analysis
pricing strategy/promotions
customer profiling
customer penetration and profitability
customer location analysis |
Nearly all of the reporting and analysis activities noted in Table 1 have a spatial
element. Spatially enabled data and tools greatly enhance decision support. For
example, customer penetration and profitability can be spatially enabled through a
simple map graphic depicting clustered point data. Likewise, customer complaint
analysis can be analyzed with a network infrastructure and network utilization to
investigate whether reported service problems are resulting from localized
infrastructure problems or temporal capacity constraints. Other examples include
the generation of statistical models to examine a customer's likelihood to:
-
respond to service packaging bundles
-
respond to pricing and advertising promotions
-
sustain product or service loyalty
-
default on payments
Such analysis is key to the bottom line of an organization. It helps segment a
customer base and differentiate service delivery -- all key elements to increasing
customer retention and profitability. Common analytical requirements may demand
analysis of product sales by various dimensions - month, year, city, region, lifestyle
profile, etc. Joining such large tables will generally overwhelm on-line transaction
processing systems (OLTP). Data warehousing has emerged as the key analytical
technology for generating business intelligence necessary for anticipating and
responding to changing customers needs and the marketplace directions.
