|
"Intelligence" is a term that is increasingly
applied to a range of external business functions. It may be used
synonymously with information or news. It may describe an actual
business process. Or the "intelligence" label may simply serve as a way
of re-packaging existing functions, activities, or products that are
devoid of true intelligence practices - or intelligence. In an
environment in which the term "intelligence" holds value and promises
great results, it’s useful to look to the practice of competitive
intelligence to understand what intelligence can, and should, provide
us and how we can maximize its application.
While collecting business information and deriving insights about the
commercial environment have been in practice since the dawn of commerce
and competition, intelligence as a formal discipline began to take
shape in 1980’s. Increasing competition, globalization and technology
were beginning to dramatically alter traditional business practices,
perspectives and stakes. Businesses sought ways to harness increasing
amounts of data and information about customers, competitors and
industry. Government intelligence models and practices were introduced
and adapted to the commercial sector. Arising out of these
developments, competitive intelligence (CI) is widely practiced today.
Despite this, however, there is still some misunderstanding about what
CI is, how it is practiced, and what it can achieve.
CI Defined
Much more than a catchphrase or a business trend, CI is the practice of
examining the external competitive environment, including direct
rivals, customers, suppliers, economic/regulatory issues and more - to
support the development of more resilient, robust strategies and
tactics. The outcome of intelligence should be meaningful, actionable,
and, according to CI guru Ben Gilad, should provide "an insight about
change and future developments and their implications to the company."
The practice of CI follows established laws and ethics, and applies
business and industry analysis rather than corporate espionage in
generating intelligence and insights. The Society of Competitive
Intelligence (SCIP) has a recognized
code of ethics.
Some look upon CI as a luxury or a discretionary practice, but the
value of CI is linked to business success - even survival. By
definition, intelligence is intended to support decisions and actions.
The insights generated from good intelligence enable decision-makers to
minimize the risks and/or maximize the opportunities surrounding future
actions. Intelligence can support even the most experienced and
knowledgeable industry professional by providing additional insights
about emerging or future developments.
It is important to note how competitive intelligence is distinct from
related concepts and practices like business intelligence, market
intelligence and competitive information gathering. While each of these
terms have been used synonymously with CI and while there are
overlapping tools, functions, and areas of research, each is
specialized. Business intelligence (BI), as it is most often applied
today, describes data mining and processing large amounts of business
data and information to identify patterns and insights in a range of
internal and external business functions. These functions may or may
not be related to the examination of the external competitive
environment or more directly competitive issues.
Market intelligence (MI) and competitive intelligence are complementary
and can overlap in analyzing customers and markets. For example, CI and
MI may each conduct or use positioning research to better understand
how customers view products, services and brands compared to the
competition. For the most part, however, the toolkits and applications
for CI and MI are distinct. MI is concerned with understanding markets
and customers in order to support decisions relating to market
opportunities, new market development and market penetration
strategies. To accomplish this, MI research and analysis tends to be
based on market research techniques, including focus group research,
segmentation research, price elasticity testing and demand estimation.
CI, while considering customers and markets, tends to treat these as
two of a number of forces that impact competitiveness and
decision-making, and rarely focuses primarily on these factors.
Industry rivals or broader pictures tend to take precedent.
Finally, competitive information gathering (or competitive research) is
the component of CI that leads up to analysis, but lacks meaning and
insights. Often, firms that simply conduct competitive information
gathering mistake this practice for true CI. Organizations may gather
news and other information about competitors or their industry - even
generate informational products like alerts or newsletters. Without
applying formal or informal analysis, as well as aligning the activity
with a decision need, however, this practice stops short of
intelligence.
So, having defined competitive intelligence, how is CI practiced?
The Intelligence Functions
CI should be established according to the specific intelligence needs
of the organization and its decision-makers. Good CI and useful
insights rely on good intelligence questions, quality information,
appropriate analytical tools and experienced practitioners. While there
are other critical success factors that should be in place (adequate
resources, management support, a culture receptive to honest discussion
and insights, sufficient timeline and response time, etc.), CI
practices tend to rely on the Intelligence Process (Intelligence
Cycle), a framework rooted in a government intelligence model that was
adapted and popularized in the commercial sector by CI guru Jan Herring.
 |
The
Intelligence Process (Click
image for larger view)
The Intelligence Cycle defines specific functions for establishing a
full-service intelligence unit. It begins by identifying the
intelligence needs of the intelligence users (i.e. decision-makers).
These needs are typically defined as key intelligence topics (KITs),
which generate key intelligence questions (KIQs). KIQs are the specific
questions on which intelligence research will be conducted. For
example, a KIT may involve a competitor’s plan to establish a new
manufacturing plant closer to a particular market. Some of the KIQs may
include:
- What is the timeline for locating a site,
building
the plant, establishing full manufacturing capabilities/capacity?
- What regulatory issues may they face?
- What is the cost of establishing the facility?
Operating costs?
- Which products will be manufactured?
- Who will be the key suppliers?
In addition to intelligence needs, other user needs
should be defined:
deliverables timeline, ways the intelligence will be used, format of
delivery, etc. Once the needs and questions are outlined and
understood, project planning takes these into account. The project
manager outlines a plan of action, resources, tools and personnel. This
includes identifying analytical models, mapping information sources and
establishing a timeline for each function and for the final delivery.
The research function for intelligence involves two types of resources:
published sources (also called secondary research or literature
research) and human sources (also called primary research). Each is
highly specialized in intelligence gathering and relies on skills
specific to its function. In published source collection, a
professional searcher or corporate information center trained in
intelligence and business research gathers content from print or
digital material. These materials range from competitor’s brochures to
government filings to news articles to photographs. Human source
collection derives information and expert insights from people
knowledgeable about the topic or issue. This function is performed by
skilled interviewers (often former journalists, industry specialists,
and trained researchers), who may speak with former employees of a
competitor, industry analysts, suppliers, customers, etc.
The analysis function is often essential to generating the intelligence
and insights that are the aim of CI practice, particularly when there
are multi-part KIQs, complex questions or a large volume of
data/information involved. Intelligence analysis can involve more than
100 different models, spanning business functions, types of operation
and industries. This function relies on intelligence information that
may be collected using published sources, human sources or both. Taking
into account the KITs/KIQs at hand, quantitative or qualitative models
may be used.
Delivery is vital to the successful use of intelligence. Intelligence
deliverables should be distributed to the right intelligence users,
address their intelligence needs, be timely and be in usable form.
These may range from scheduled reports and bulletins to products that
address ad hoc intelligence issues. It is important to point out that
news alerts may be vital information sources, but they are not
substitutes for intelligence deliverables, which are tied to particular
intelligence needs and often include analysis and recommendations.
The Intelligence Process should be considered a framework and a
starting point for CI. Each function - and the process itself -
requires customization to the specific needs and characteristics of an
organization. Moreover, organizations may apply CI by developing
selected functions internally and outsourcing other functions, as
needed. For example, businesses often conduct routine published source
collection on their own, but may outsource highly specialized research
(e.g patent or trademark searches), formal interviews of human sources
or complex analysis.
CI and LI
The relationship between CI and the emerging practices and tools that
comprise location intelligence (LI) is rich and mutually beneficial.
First, the significance of CI to LI can be found in the well defined
and well established process, practices and applications of competitive
intelligence. The term "location intelligence" is often used to
describe the tools involved with mapping and business network analysis.
This emphasis on tools (and data) over process and techniques may limit
the capacity to fully generate intelligence. The generation of location
intelligence may be more targeted, efficient and effective by modeling
CI’s use of formal frameworks, its business discipline and its focus on
decision-support. This will encourage a consistent application of
location intelligence and help ensure that LI fulfills the intelligence
needs of decision-makers.
Next, competitive intelligence may be used to gain insights on the
location-based capabilities of a competitor, partner or supplier. In
better understanding a company’s use of RFID in managing their
distribution chain, for example, formally applying CI can tie this
effort to the decision needs of the intelligence users, enhance the
quality of the intelligence and help generate more usable results.
Finally, CI itself can more consistently apply location-based tools,
technology, and applications to gather information, perform analysis
and generate insights on competitors, markets and industry. Some CI
practitioners have begun to adopt LI resources and techniques, like
mapping and visualizing collected data, applying business geographics,
gathering information from more widely available satellite maps and
applying location-based analysis techniques (e.g. retail network
analysis and Spatial Temporal Computing). Cost and complexity are
sometimes barriers, but increased awareness and education about LI will
encourage more practitioners to use these tools and enhance the
generation of intelligence.
Reference
Benjamin Gilad, Business Blindspots: Replacing Myths, Beliefs, and
Assumptions with Market Realities (Wiltshire, England: Infonortics,
Ltd., 1996), p. xviii.
Resources
Knowledge inForm
(newsletter, glossary, e-books, online seminars, training)
SLA Competitive
Intelligence Division
(bulletin, discussion list, SLA directory, online resources)
Society of Competitive Intelligence
Professionals
(publications, training, events, industry news, directories)
More about this author...
|
