ISSN:1058-0247    

DOI:10.1002/cir.3880070402

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1996

数据来源: WILEY

ISSN:1058-0247    

DOI:10.1002/cir.3880070403

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1996

数据来源: WILEY

摘要:

AbstractConferences, conventions, and trade shows are such common sources of information on competitors that CI professionals too often take them for granted, failing to realize that a “game plan” is necessary to take maximum advantage of intelligence gathering at these events. As a remedy, the authors have adapted the “Quarterback Technique” developed by the U.S. intelligence community. The process isn't “mission: impossible” but does make use of a tactical team headed by a “quarterback” who plans and anticipates collection opportunities, begins to link with “targets” who can address specific intelligence requirements, finds internal and (trusted) external “sources” who have potential access to specific targets, and determines which events and activities could help bring sources and targets together. This operation is best coordinated from a “war room” at the conference site, equipped with tools ranging from the simple (butcher‐board paper and colored markers) to the relatively sophisticated (electronic organizers, digital cameras, and pocket tape recorders). As sources are debriefed, CI analysts located either at the conference or at headquarters immediately review the intelligence reports and recommend follow‐up collection activities during the conference, alerting and soliciting feedback from senior management if the intelligence proves significant. Many of these same techniques can be adapted for electronic and virtual conferenc

ISSN:1058-0247    

DOI:10.1002/cir.3880070404

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1996

数据来源: WILEY

摘要:

AbstractSuccessful bidding for contract awards requires not only a detailed knowledge of competitor strengths and weaknesses but the ability to use this information to fortify your offering and neutralize competing bids. The author presents an approach for crafting a winning strategy that differentiates your firm from the competition on those attributes most important to the customer; clearly communicates why your offering best meets the customer's needs; explains why your analysis led you to reject the competition's approach in favor of your own; and otherwise identifies and discredits the competitor's strengths while exploiting every weakness. In addition, developing a competitive assessment in which your management has confidence will help your firm make more decisive competitive‐bidding investment decisions. If you are clearly going to win, additional prototypes and expensive promotion can be avoided. If headed for certain defeat, management can terminate the pursuit and cut its losses. Finally, competitive intelligence is vital to determining the “price‐to‐win” figure at which the maximum acceptable price can be bid. © 1996 John Wiley

ISSN:1058-0247    

DOI:10.1002/cir.3880070405

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1996

数据来源: WILEY

摘要:

Abstract“Data mining” and “data warehouses” are ways of applying artificial intelligence to create competitive intelligence. Induction‐based data mining software uses machine‐learning algorithms to analyze records in a firm's internal and customer databases, discovering patterns, transactional relationships, and rules that can predict future trends and indicate competitive opportunities. Raw data thus transformed is maintained in a data warehouse, providing support for a variety of analytical tasks and competitive decisions. As a result, questions that traditionally required extensive trial‐and‐error queries or statistical segmenting can be answered automatically. Since the machine‐learning algorithms can identify key intervals (ranges) and attributes (variables) in a database, they can compress a database so that only a few attributes are needed to derive predictive intelligence. Recent studies indicate data mining technology will have a major impact across a wide range of industries within the next three to five years. A table is provided of data mining products with vendor names, phone numbers, and Internet addresses. © 1996 Jo

ISSN:1058-0247    

DOI:10.1002/cir.3880070406

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1996

数据来源: WILEY

摘要:

AbstractMacroeconomics—including business cycle forecasting through the use of key economic indicators—can play an important role in competitive intelligence. Familiarity with key indicators leads to an understanding of how different parts of the economy interact. Tracking these can be as simple as taking a daily look at the front‐page of theWall Street Journal. “Elasticity”—how sensitive one variable is to a change in another variable—is a useful tool for defining markets and forecasting responses to price changes. For example, algebraic equations can be used to forecast whether a price increase (or decrease) will produce a proportionately greater decrease (or increase) in quantity demanded. “Arc elasticity” can be used to determine the elasticity over a given price range (or arc). When demand is elastic, total revenue—the price at which a given quantity is sold times that quantity—declines as prices are increased and increases as prices fall. The opposite is true of inelastic demand. Knowing the elasticity of demand for your products and your competitors' products is a key to determining pricing strategy. Income elasticity is a similar tool, with income substituted for price. Also useful for CI is determining the boundaries of an industry or market by calculating cross elasticity of demand. © 199

ISSN:1058-0247    

DOI:10.1002/cir.3880070407

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1996

数据来源: WILEY

摘要:

AbstractPatent trend analysis, or the bibliometric analysis of patenting activity over time, has been used to evaluate where technologies stand on their maturity curves and to identify players in a technical field. The tool has also been applied to assess specific companies' technology portfolios and technical strategies. The analysis is usually based on a direct count of the number of patents published annually. The author proposes, and shows with examples, that factors other than technology maturity and technology strategies can affect these trends and that extraneous factors have to be taken into account before valid conclusions can be drawn. © 1996 John Wiley&Sons, Inc

ISSN:1058-0247    

DOI:10.1002/cir.3880070408

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1996

数据来源: WILEY

摘要:

AbstractManagerial judgments are essential inputs in market structure analysis. The basic premise of this research is that by using structured elicitation methods that rely on the underlying logic of competitive market structure analysis (CMSA) models, it may be possible to effectively extract and organize managers' knowledge of competitive relationships. In this study, the authors examine how managerial judgments are influenced by two elicitation methods (perceived competitive similarity and forced choice) and three specific priming cues (brand image, features, and usage situation). The focus is on examining the learning effects of feedback and concordance of judgments across elicitation methods. The results showed significant effects of both feedback and elicitation method, but not for the primed cue. Relative to the perceptual similarity method, the forced choice method helped subjects better articulate the veridical structure of the market. Also, the forced choice method produced more concordant judgments of market structure when followed by perceptual similarity as compared to the reverse (i.e., perceived similarity followed by forced choice). This suggests that the forced choice elicitation method produces a more stable perception of market structure. © 1996 John Wiley&Sons, Inc

ISSN:1058-0247    

DOI:10.1002/cir.3880070409

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1996

数据来源: WILEY

摘要:

AbstractExperiential Modeling (EM) consists of a portfolio of tools that translates the judgment of individuals into data that can be used to predict the “know‐how” (informal, unrecorded professional experience) of competitors before they are able to use it to advance their own businesses. The authors content that knowledge has two components: data and experience. The definition of these as well as the difference between information, know‐how, knowledge, and knowledge blocks are examined. The role of intelligence professionals, the authors say, is not to gather information to pass on to decision makers, but rather to design the structure and tools required to focus the decision makers' judgment into “knowledge blocks” (concisely written problem‐solving documents, incorporating judgments based on valid experiences) that enable decision makers to solve their own “puzzle.” The essential components of the EM methodology are the design of complex templates and glossaries of terms, modeling processes that convert judgment into mathematical equations, and the skill to use these equations to solve complex puzzles. The authors provide an overview of EM as well as three case studies. © 1996 Jo

ISSN:1058-0247    

DOI:10.1002/cir.3880070410

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1996

数据来源: WILEY

摘要:

AbstractAs competitive intelligence becomes more established as a professional discipline, the need grows to devise ways of measuring and quantifying the results of intelligence gathering operations. “Intelligence asset‐building” is one way of applying effective measures to CI efforts. This process includes surveying and interviewing CI practitioners and intelligence users to determine how information is used and how well intelligence needs are being met by current systems and practices. The results, based on tabulated findings and a final report, can give tangibility to CI functions and help justify funding for CI operations. © 1996 John Wiley&Son

ISSN:1058-0247    

DOI:10.1002/cir.3880070411

出版商:Wiley Subscription Services, Inc., A Wiley Company    

年代:1996

数据来源: WILEY