Competitor Data as a Commodity

Instead of looking at data sources at the beginning of your CI research, approach the task by thinking of the data on a competitor that you are seeking as a commodity, a tangible product. When you do that, then ask and answer the following questions:

Who produces the raw data I want?
Who collects the raw data I want?
Where is the raw data that I want transferred, and why?
Who uses the raw data I want?
Who accumulates the raw data that I want?
Who else has an interest in the raw data I want?

By answering these questions, you will quickly begin to narrow down where you can begin to look for the raw data you need to develop the CI for your end users.

Producers of Raw Data

Take, for example, a case where you are seeking company-level or even divisional-level data. That would seem to mean that a targeted competitor actually produces it. But, regardless of that first impression, you should inquire further. Specifically, who at the competitor is most likely to produce this raw data? For example, is marketing strategy determined by the marketing department, dictated by a strategic plan prepared by the planning department, or the result of input from each? If the answer is that the marketing strategy is a blend of marketing and planning department inputs, when you are, for example, interviewing personnel formerly with a target firm, make sure you get people from the appropriate department involved in the process. ^[1]

Data Collectors

The person or department you identify here may not be the same person or department that actually produced the data. The key concept here is transmission: when data is being transmitted, it is first assembled and sometimes analyzed, but always moved. One key to locating raw data is to determine where the data is moving so you can try to intercept it, in a figurative sense only. That is why the next question also becomes important.

Data Transfers

Assume that, for any number of reasons, your firm wants detailed, but apparently unavailable, data on the performance of the U.S. gambling operations of a casino partially owned or managed by a competitor. Perhaps your end user wants to determine whether the casino is so profitable that your competitor can expand into other lines or thinks that it is losing so much money that the competitor will have to delay expansion plans in the other areas in which it competes head to head with you.

You should at least check with the Casino Control Commissions for the state or states in which the competitor has casinos. This is because, as regulators, these commissions have a number of areas of interest in that same subject, one of which may be the profitability, or at least dollar volume, of the casino. And because these are public agencies, data the commissions require casinos to provide may be considered as non-proprietary and therefore can be made public and available to you.

Data Users

To determine who uses the kind of raw data you want to locate, you seek out individuals, such as securities analysts, who want data from companies to generate company and/or industry forecasts. For you, what the analyst has to offer is, first of all, his or her analysis and conclusions. However, of potentially greater importance may be the raw data—whether numeric or narrative—provided by the target competitor to the analyst. That data may, in turn, be disclosed in the analyst's reports. If not, you may be able to get it by direct contact with the analyst. If not, you may still be able to extract it from the reports by carefully reviewing them.

Data Accumulators

To reach the handiest repository of accumulated data, contact government regulatory bodies and, in the case of publicly traded companies, investment analysts and trade associations. Each of these sources has data of differing types and quality. The type and quality of that data depends on why each collected the data in the first place. For example, investment analysts are often privy to information that differs from that made available to the press, including the financial press.

Organizations such as the Census Bureau and trade associations generally (but not always) work with aggregated data, as opposed to data at the company or lower level. Using disaggregation may enable you to isolate significant data that you and the providers of the data both assume to be masked by the aggregation efforts of those providing the data.

Others with Interests in the Data

To learn what other organizations and people may have already collected some or all of the data you are seeking, your focus will move to a relatively wide range of potential resources. These can range from the advertising departments of trade publications to academic research centers. Trade publications' advertising departments may collect or even generate data similar to what you are seeking in an effort to show that their publication represents the audience an advertiser wants to reach. To do that, the advertising department may choose to collect data or even commission new surveys to educate potential advertisers about the industry and about key participants that can be reached by advertising there.

Academic centers can be a useful resource because their access to data is sometimes much freer and broader than that of trade associations and the like. This may be because those associated with the centers may be able to interview or research your competitors. Moreover, the companies they deal with will sometimes assume that giving academic researchers access to the data is "harmless," in a competitive sense. Of course, some competitors are savvy enough to insist that the academics who are given access to competitively sensitive data do not release any of the data, except in some aggregated form. However, there are often ways around such blocks. For example, if a researcher is operating under such a limit, in interviewing him or her you may be able to discern some of the underlying data by a close discussion of the conclusions he or she has drawn.

^[1]Such interviews are subject, of course, to the legal and ethical limits set forth in Chapter 8.

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Competitor Data as a Commodity