“I don’t need the exact figure. Just give me the ballpark number.”

This is how I sometimes do business when I am trying to buy a new car. When I am early on in deciding which car to buy, knowing that one of the candidates is about $25,000 and the other one is about $40,000 is enough information for me. The ballpark number is a useful approximation for my initial purpose. (Later I will bargain about the exact car and sales price.)

In competitive intelligence, we are often asked to assign a number to something a competitor is doing.

For instance, our management might want to know how much research and development money has been spent on the latest product from our competitor. This isn’t a number that most companies will report publicly. So what do we do? Give up? No, rather we fall back on the article of competitive intelligence faith that there is always an ethical way to give a good answer.

We find a credible way to estimate the number.

We might start with the company wide R&D number (which typically is reported). Then, we apportion it among the product lines by tracking how many new product introductions have been made in the year. A further refinement is made by overlaying the typically product cycles (e.g., 24 months from conception to marketing). That suggests that the product introduced this year was partially funded by R&D funds from previous years. Next, knowing something about where the new product fits in the product line helps us understand if it is a wholly new product or a derivative from a previous product. (Derivative products will usually require less R&D dollars than completely new products.) It is often straightforward to estimate the percentage of new design or features in any product. Putting all of this together means that we can report a ballpark number to management about the R&D money spent on a new product.

Sometimes the method is as useful as the answer.

Working through the previous example illustrates how the method teases out information and assumptions. This clear thinking has two main benefits. First, it answers the inevitable question from management about how you got to the answer. Second, and more importantly, it lays bare the reasoning in a way that allows others to challenge and improve your assumptions. When a CI professional manages this well, the ownership of the answer passes from the CI person to management. This is how it should be once they (management) are equipped with the right information.

The method also conveys the risk in the estimate.

A recent article in BusinessWeek entitled Digging Up Amazon’s Numbers is a wonderful example of a useful approximation. According to the article, Amazon is generally unhelpful in providing information to analysts that track the company about future prospects. So, Marianne Wolk (analyst at Susquehanna Financial Group), used warehouse information reported by Amazon to create a leading indicator of their future sales. That is, if Amazon increased their warehouse square footage, then that meant they were expecting higher sales. Correspondingly, a decrease in warehouse space meant declining sales expectations. This approximation will have to be tested over time to validate its usefulness. Nevertheless, it is a clever way to link something that is known with something that one wishes to know.

Public companies are sieves of information.

One recurring realization is that public companies cannot help but signal their intentions. The challenge is to use what is public to estimate what is not public. We don’t have to arrive at exact numbers. Most of the time a useful approximation (with the methods and assumptions described) will be quite valuable to management.

Do you agree?

There is no one measure (short of public announcements) that signals the future. However there are many activities and actions which may help produce a reasonable guess about future plans. Job postings are one type of signal that may indicate a company’s future plans.

Most companies are good about publishing their job openings. Some companies even provide information about the level of opening (e.g., director, senior engineer, VP) and the division or product involved. This information can be captured at three month intervals to help identify trends. The captured listing should be sorted by location (certain activities occur at specific locations, product/division and levels). You want to know the following.

• Are job listings increasing or decreasing? (might signal changing R&D investment levels)
• What specialties are listed most? Least? (might signal turnover or expansion)
• Where are the centers of activity and how does this map to known products? (might signal what is being developed)
• What new skills (i.e., those not needed for the company’s known products) are being listed? (might signal new product type)
• How are recent acquisitions/mergers affecting job listings? (might signal plans for integrating the new company)

There are cautions to observe with this type of information. First, a job posting is not a job. That is, the company is making no promise to actually fill a position just because there is a job posting. Second, without turnover numbers, it is difficult to directly understand that a job posting (if filled) represents a staff expansion or not. Third, for companies with large campuses, it is more difficult to match postings with specific products (unless they tell you in the posting).

Mining job listings effectively over time helps the analyst map out the competitor’s future product plans, identify their development centers and understand how they expect to expand (or contract) their competencies.

My experience in competitive intelligence (CI) is that organizations have favorite techniques to interpret the competitive environment. For example, many companies love SWOT (strength, weakness, opportunity, threat) diagrams. These may be useful however they are clearly not the right approach to model or interpret all issues of the competitive environment.

When a small number of approaches are used repeatedly, it may signal what I call “analysis poverty” in the organization. Analysis poverty is the condition whereby a large variety of problems are addressed by a narrow set of analytical techniques. The impact of analysis poverty is that the organization will not likely understand the environment appropriately and they will dampen the impact (through misapplication) of the techniques that they know best.

Analysis poverty presents the competitive intelligence professional with some challenges.

1.  Education - This starts with the CI professional. It is important that he or she be regularly learning new approaches to understand the competitive landscape, model possible responses and mobilizing the organization for change. There are multiple avenues for expanding ones repertoire including the Society of Competitive Intelligence Professionals and training offered through organizations like the Academy of Competitive Intelligence.

2.  Training - By this I mean training in the organization. This is a far more subtle task that the self learning. Most senior managers have little time to test “untried” techniques for critical issues. The “accepted” techniques (even if misapplied) may be preferred to change. The CI professional must learn to introduce alternatives appropriately to this audience. Usually I have found low risk settings an excellent place to try one new approach at a time.

3. Leverage - It is a fact of life that some organizations value some types of work done by those outside of the organization (e.g. industry analysts) over that produced internally. If this is true, then the challenge for the CI professional is to find those sources that are considered highly credible. Then, using the validation of the external source, the task is to customize an organization specific example.

4. Testing - Even when new techniques have not been accepted for general use in presentations by the CI professional, it is often completely acceptable for the CI analyst to test what is new for themselves. This seems obvious but may be overlooked if the presentation of the results is thought to be the critical success factor. Actually, the derived insights will be more valued over time and if the new techniques enable such insights, then their value will be easily illustrated after the suitable testing.

There are a couple of books that I have used to stimulate my thinking about analysis techniques.

• Business and Competitive Analysis: Effective Application of New and Classic Methods (Fleisher/Bensoussan)
• Strategic and Competitive Analysis: Methods and Techniques for Analyzing Business Competition (Fleisher/Bensoussan)