There are a lot of definitions of Competitive Intelligence (CI), or CI as we will call it. This is the one we prefer: Competitive Intelligence (CI) involves the use of public sources to develop data on
competition, competitors, and the market environment. It then transforms, by analysis, that data into [intelligence]. Public, in CI, means all information you can legally and ethically identify, locate, and then access (McGonagle and Vella 2002, p. 3).
CI is also called by a lot of other names: competitor intelligence, business intelligence, strategic intelligence, marketing intelligence, competitive technical intelligence, technology intelligence, and technical intelligence. The most common difference among them is that the targets of the intelligence gathering differ. However, what those who are developing it all do is essentially the same:
• They identify the information that a decision-maker needs on the competition, or the competitive environment;
• They collect raw data, using legal and ethical means, from public sources;
• They analyze that data, using any one of a wide variety of tools, converting it into intelligence, on which someone can take action (‘‘actionable’’); and
• They communicate the finished intelligence to the decision-maker(s) for their
use.
To understand CI, you must first clearly understand what is meant by ‘‘public’’, that is, where the raw data you will need is located. The term is to be taken in its very broadest sense–it encompasses much more than studies that the US Department of Commerce releases or what you can find reported in The Chicago Tribune. ‘‘Public’’ in CI is not equivalent to published; it is a significantly broader concept.
In CI, public encompasses all information you can legally and ethically identify, locate, and then access. It ranges from documents filed by a competitor as a part of a local zoning application to the text of a press release issued by a competitor’s marketing consultant describing its client’s proposed marketing strategy, where the marketing firm also extols the virtues of its contributions to the design of a new product and the related opening of a new plant. It includes the web-cast discussions between senior management and securities analysts, as well as the call notes created by your own sales force.
Please keep in mind that CI is not just aggregating the results of a Google.com online search. That is collecting data. Admittedly, you do have to determine what you are searching for before you start, but using Google (or any other search engine—we are not playing favorites here) is just a way of picking out potentially interesting bits of data to look at from billions of available bits of data. The vast production of search engines is a classic illustration of the fact that data is not the same as intelligence. Intelligence is refined from data and is actionable. It all too often gets lost in a blizzard of raw data.
The CI process is usually formally divided by CI professionals into five basic phases, each linked to the others by a feedback loop. We are describing them to you because some, but not all, of what you will do includes some of these phases. Also, when you read further about CI, you will often find authors referring to the ‘‘CI Cycle’’. These phases, making up what CI professionals call the CI cycle, are—
• Establishing the CI needs. This means both recognizing the need for CI and defining what kind of CI the end-user needs. It entails considering what type of issue (strategic, tactical, marketing, etc.) is motivating the assignment, what questions the end-user wants to answer with the CI, who else may also be using the CI, and how, by whom and when the CI will ultimately be used.
• Collecting the raw data. First, a CI professional translates the end-user’s needs into an action plan, either formally or informally. This usually involves identifying what questions need to be answered, and then where it is likely that he/ she can collect the data needed to generate the answers these questions. The CI professional has to have a realistic understanding of all significant constraints, such as time, financial, organizational, informational, and legal. Then he/she can identify the optimal data sources, that is, those that are most likely to produce reliable and useful data, given the goal and the constraints. From there, the collection begins, both of secondary and primary data.
• Evaluating and analyzing the raw data. In this phase, the data that was collected is evaluated and analyzed, and is transformed into useful CI. That may be done by the person doing the collection or by a separate CI analyst. In practice, there are always two ways in which analysis is used in the entire process. The first is the use of analysis to make a selection, such as deciding which of a dozen news articles is most important to read. The second is the use of analysis to add value to one or more pieces of data. That would mean, for example, adding a statement to a summary of an article indicating why and how its contents are important to the end-user. While CI analysts provide both types of analysis, endusers most frequently only regard the latter process as really being analysis.Of course this is not true. If you do not use some analysis during the collection process, you will waste hours of time collecting useless information that takesyou nowhere.