Business intelligence applications |
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| 19 Apr 2007 | David Loshin, Author |
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 Editor's note: This chapter excerpt from Business Intelligence: The Savvy Manager's Guide by David Loshin focuses on the applications of business intelligence (BI). It is an excerpt of chapter two, "The Value of Business Intelligence."
It is interesting to note the different uses of data and the contexts of each use
as it pertains to the exploitation of information. For the most part, we can
break those into two areas. The first area is operational data use, and the other
is strategic use. The predominant use of information today is operational: how
data helps run the business, as opposed to strategic information use, which
helps improve the business.
Clearly these both are valuable, and without the operational use of information
a business could not survive. But it is up to the information consumer
to determine the extent of the value to be derived from the strategic use of
information as well as what strategic uses are of importance. In this section
we review some of the strategic uses of information as manifested through BI
analytics. Note that although many of these analytic applications may be categorized
within a specific business domain, many of them depend on each
other within the business context.
Customer analytics
A common, overused term is customer relationship management (CRM), which
has become a buzzword implying an all-encompassing magic bullet to turn
all contacts into customers and all customers into great customers. The magic
of CRM is actually based on a number of customer analytic functions that
together help people in a company better understand who their customers
are and how to maximize the value of each customer. The results of these
analytics can be used to enhance the customer's experience as well.
Following are different aspects of customer analytics that benefit the sales,
marketing, and service organizations as they interact with the customers.
Customer profiling: The bulk of marketing traditionally casts a wide
net and hopes to capture as many individuals as possible. Companies
are realizing that all customers are not clones of some predefined
market segment but are thinking individuals. To this end, customer
analytics encompass the continuous refinement of individual customer
profiles that incorporate demographic, psychographic, and behavioral
data about each individual.
Targeted marketing: Knowledge of a set of customer likes and dislikes
can augment a marketing campaign to target small clusters of
customers that share profiles. In fact, laser-style marketing is focused
directly at individuals as a by-product of customer analytics.
Personalization: As more business moves online, the browser acts as a
proxy for the company's first interface with the customer. Personalization,
which is the process of crafting a presentation to the customer
based on that customer's profile, is the modern-day counterpart to the
old-fashioned salesperson who remembers everything about his or her
individual "accounts." Web site personalization exploits customer profiles
to dynamically collect content designed for an individual, and it is
meant to enhance that customer's experience.
Collaborative filtering: We have all seen e-commerce Web sites that
suggest alternate or additional purchases based on other people's preferences.
In other words, the information on a Web page may suggest that
"people who have purchased product X also have purchased product Y."
These kinds of suggestions are the result of a process called collaborative
filtering, which evaluates the similarity between the preferences of
groups of customers. This kind of recommendation generation
Customer satisfaction: Another benefit of the customer profile is the
ability to provide customer information to the customer satisfaction
representatives. This can improve these representatives' ability to deal
with the customer and expedite problem resolution.
Customer lifetime value: How does a company determine who their
best customers are? The lifetime value of a customer is a measure of a
customer's profitability over the lifetime of the relationship, which
incorporates the costs associated with managing that relationship and
the revenues expected from that customer. Customer analytics incorporates
metrics for measuring customer lifetime value.
Customer loyalty: It is said that a company's best new customers are
its current customers. This means that a company's best opportunities
for new sales are with those customers that are already happy with that
company's products or services. Customer analytics help.
Human capital productivity analytics
One way to attain value internally from BI is to be able to streamline and
optimize people within the organization, including:
Call center utilization and optimization: If you have ever dawdled
while on hold, waiting for a customer service representative to pick up
the telephone, you can understand the value of analyzing call center
utilization to look for ways to improve throughput and decrease customer
waiting time. When a company's management realizes that
inbound calls are likely to be from unsatisfied customers, making them
stew on the phone is not going to improve customer satisfaction. In the
more advanced cases, quick access to customer profile information may
also affect the level of support provided to each customer (e.g.,
high level to high-value customers, minimal support to low-value
customers).
Production effectiveness: This includes evaluating on-time performance,
labor costs, production yield, etc., all as factors of how staff
members work. This information can also be integrated into an information
repository and analyzed for value.
Business productivity analytics
Another popular analytic realm involves business productivity metrics and
analysis, including:
Defect analysis: While companies struggle to improve quality production,
there may be specific factors that affect the number of defective
items produced, such as time of day, the source of raw materials used,
and even the individuals who staff a production line. These factors can
be exposed through one component of business productivity analytics.
Capacity planning and optimization: Understanding resource
utilization for all aspects of a physical plant (i.e., all aspects of the
machinery, personnel, expected throughput, raw input requirements,
warehousing, just-in-time production, etc.) through a BI analytics
process can assist management in resource planning and staffing.
Financial reporting: Stricter industry regulatory constraints may force
companies to provide documentation about their financials, especially
in a time when companies are failing due to misstated or inaccurately
stated results. In addition, financial reporting analytics provide the
means for high-level executives to take the pulse of the company and
drill down on particular areas.
Risk management: Having greater accuracy or precision in tracking
business processes and productivity allows a manager to make better
decisions about how and when to allocate resources in a way that minimizes
risk to the organization. In addition, risk analysis can be factored
into business decisions regarding the kind of arrangements that are
negotiated with partners and suppliers.
Just-in-time: The concept of just-in-time product development
revolves around the mitigation of inventory risk associated with commodity
products with high price volatility. For example, the commodity
desktop computer business is driven by successive generations of commodity
components (disk drives, CPUs, DRAM memory chips, to
name a few). Should a vendor purchase these items in large quantity
and then come up against a low-sales quarter, that vendor might be
stuck with components sitting on the shelf whose commodity value is
rapidly declining. To alleviate this, the knowledge of how quickly the
production team can assemble a product, along with sales channel
information and supplier information (see Sales Channel Analytics and
Supply Chain Analytics on page 21) can help in accurately delivering
products built to customer order within a predictable amount of
time.
Asset management and resource planning: Utilization, productivity,
and asset lifecycle information can be integrated through business analytics to provide insight into short- and long-term resource planning,
as well as exposing optimal ways to manage corporate assets to
support the resource plan.
>> Download the full chapter
Printed with permission from Morgan Kaufmann, a division of Elsevier.
Copyright 2003. Business Intelligence: The Savvy Manager's Guide by
David Loshin. For more information about this book and other similar
titles, please visit www.mkp.com.
David Loshin is president and CTO of Knowledge Integrity Inc. a technical consulting firm that helps businesses address problems arising from the collection, migration, transmission and analysis of large sets of data. He holds a master's degree in computer science from Cornell University and is the author of several books, including Enterprise Knowledge Management (2001), and High Performance Computing Demystified (1994).
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