This article was originally published in the Intelligence Insights May 2010 newsletter of the Special Libraries Association – Competitive Intelligence Division.

I remember standing next to my fellow student as we both stared at the teacher in the front of the classroom. Fourth grade rarely got as intense as when we were competing to give the right answer to the teacher’s flash card question. The teacher would wait until we ready and then quickly display the card with a math problem. What was the answer to “8 times 7?” We rushed to raise our hands. It was not a small matter to master multiplication and we were proud when we did (especially when we won the competition).

In math (as in most subjects), we learn the basics first. Complex problems remain a mystery until a solid foundation of principles and techniques is established. We first learn to add, subtract, multiple and divide. Later, we learn about fractions, percentages, geometric shapes and trigonometry. Each topic builds on established foundations and represents significant learning.

About the time that we were satisfied with what we knew, our math teachers introduced a new challenge – word problems.

Bill owned a company that grew and sold tomatoes and other vegetables. Each pound of tomatoes cost $1.75 to produce and was sold for $3. The average margin of the other vegetable products was $2 but the production risks (i.e., pests, weather damage) were twenty percent higher than for tomatoes. Bill had to repay a $7000 farm loan after the current growing season and had $10,000 in cash to invest. How much should he invest in tomatoes and the other vegetables? Why?

The bane of many math students is word problems.  Why? Because word problems force them to deal with the messiness of real situations. Extra information is often included which means they have to sort out what is important versus what is extraneous. Moreover, unlike many of the early math drills when we knew the “shape” (units) of the answer, word problems give less information about the expected answer. In addition, word problems often require the student to use multiple techniques or approaches to solve the problem. They must state assumptions and employ risk measures to produce the needed solutions.  Real life problems are complex and require a different level of thinking.

Competitive intelligence is a lot like math. Early on, we have to understand the basics. For instance, a common way to think about competitors is to summarize their strengths and weaknesses and compare them to our own. This understanding opens the way for capitalizing on the differences. Often we use the Strength-Weaknesses-Opportunities-Threats (SWOT) analytic technique for this problem.

Another example is a simple product or service comparison. In this case, we learn to identify key factors that characterize the product or service and then show how two or more offerings fare in each comparative category. The significant differences (positive or negative) indicate a competitive advantage for some company. This is a relatively simple and effective technique.

There are other important fundamentals to learn in competitive intelligence. For example, there are good things to know about collecting information from primary sources. Knowledge management professionals are especially well aware of excellent methods to find, organize and use information from secondary sources. There are established ways to define the scope of the competitive intelligence project with key intelligence topics. A list of fundamental competitive intelligence skills is long and every competitive intelligence practitioner should learn these basic techniques. Avoiding or shortcutting this learning is unwise and professionally limiting.

However, is it enough to master the fundamentals? That is, do the fundamentals alone make competitive intelligence effective? Would a more complex view of competitive intelligence problem-solving yield richer answers?

The simple answer to the last question is “yes” because effective competitive intelligence deals with valuable and complex questions. Competitive intelligence is like, well, a word problem.

By that, I mean that competitive intelligence is a messy set of activities involving difficult information sourcing, complex analyses and, importantly, an unpredictable mix of people and politics. Someone that understands and applies only the fundamentals risks oversimplifying the competitive intelligence challenge. Competitive intelligence “word problems” represent the complexity of real life.

Mastering “word problems” requires moving from a technique focus to a solution focus. Since good competitive intelligence solutions can affect important decisions, they are immensely valuable to the customer. However, it takes practice to provide such solutions. How would you proceed if you were “Tim” in the following situation?

Janet Simpson gazed at the stacks of paper on her desk. It was a little overwhelming to imagine that as the new Vice President of Strategy for the Bluesin Technology Company she was expected to recommend a growth strategy at the next Board of Directors meeting. After all, she had only been on the job for two months. Reflecting on the harried set of meetings, reports and discussions from the last eight weeks, Janet realized that the job was bigger than she thought. Maybe the right approach, some within Bluesin argued, was to refocus on higher margin products and abandon the segments that were under pressure from the new competitors. Others maintained that Bluesin should fight the competitors for every bit of business at existing competitors. The third predominant group said that moving to an entirely new business model was the best course of action. Janet’s analysis and recommendation were intended to indicate decisively the best direction. Early on, Janet decided to engage with Tim Sanders, a competitive intelligence consultant that she had worked with before.

One thing that you would not likely do is to list for Janet all of the competitive intelligence techniques that you had mastered. A more successful approach is probably for you to listen intently to Janet’s challenges, hopes and biases. Using that information, you might construct a customized, nuanced solution for Janet. Janet (like most competitive intelligence customers) needs and wants that kind of help. Babette Bensoussan, a well-known competitive intelligence consultant and author from Australia offered the following (summarized) solution.

I would suggest that Janet address parallel steps in her assignment with Tim’s assistance.  The first series of steps is an analytical piece delivering a current baseline picture of the existing industry. A second series of steps relates to the stakeholders.  That is, the competitive baseline needs to be unfolded to align key decision-makers and influencers in the organization. While there are competing influencers within Bluesin, the CEO is in fact Janet’s champion and Janet needs to work with the CEO to ensure he is on board with any baseline picture, action plan, or strategy outcomes.  They need to work together to convince the Board and the CEO’s management team to adopt any identified new strategies for growth.

(The complete case study for Bluesin Technology and Ms. Bensoussan’s response is at http://blog.cicases.com.)

How does a competitive intelligence person get experience with “word problems?”

Aside from long-term practical experience, the best way is to learn through case studies. Case studies have the unique advantage of explaining realistic stories succinctly. Each story contains important and unimportant information (just like real life). In each case, someone (e.g., senior management) has a need for a solution. Note that they rarely care about specific competitive intelligence techniques or approaches. In fact, they focus on one or more key questions stated in terms of value to their job. This means that they want answers to their questions and need help from someone (e.g. the competitive intelligence practitioner) that has a sympathetic understanding of the issues. With that sympathetic understanding, the competitive intelligence person extracts and employs a variety of skills and knowledge from their “tool bag” to add value to the decision-making process. Here is a smattering of common high value questions.

• How will our new product compare to our competitors’ products?
• What key factors influence customers’ decisions to buy from our competitors or us?
• What competitive strategies are our customers using and how will each affect our success?
• What competitive factors affect our growth strategy?
• How can I train, align and motivate my company to observe, understand and respond to the competitive environment better?

What do you do when faced by one of these challenging questions?

Here are eight basic and useful steps for solving competitive intelligence problems. Each step promotes a customer-centric, solution-oriented view. Further, these steps intentionally broaden the view of the competitive intelligence professional. Importantly, case studies provide a safe environment to practice.

1. Start with the end in mind. View the competitive intelligence challenge as answering a key question for your customer rather than as a competitive intelligence technique issue. Align the success criteria for your services with the expected and needed benefit to the customer.
2. Consider the people. Perhaps the biggest determinant of success is not performing the right analysis (though, of course, analysis is important). Rather, what is most important is dealing with the people affected directly and indirectly by the competitive intelligence. They have pressures, histories, ambitions, goals and more that affect their reception and use of competitive intelligence.
3. Clarify the customer’s key questions. The key questions, clearly defined, point to the value needed by the customer. After isolating the questions, work backwards to determine what analytical techniques or solution methods are appropriate.
4. Calculate risks. By definition, the future is uncertain. Therefore, a standard practice for competitive intelligence is to define and quantify the risks associated with recommendations or conclusions. Focus on explaining risks and assumptions.
5. Cooperate with the change. Assuming that the competitive intelligence indicates a needed change, identify material, arguments and information that enable the change. It is a higher standard to provide change-enabling information versus documenting interesting (but not actionable) analysis results.
6. Practice with existing competitive intelligence case studies. Many consultants post case studies. My site for Competitive Intelligence Case Studies (www.cicases.com) has case studies and commentaries from CI professionals. You can compare your answers to theirs to improve your skill in providing solutions.
7. Find cases stated in terms of your customers’ issues. Read what your customers read. If your customer is a lawyer, read a selection of legal journals. If your customer is a financial strategist, read the Wall Street Journal. In every case, examine problem-solving stories for that profession and then deduce what competitive information helped (or could have helped) solve the problems.
8. Share and compare solutions with others. Find ways to share your stories with others so that you can learn to improve your skills. Of course, there are limits to sharing confidential details. However, by sharing and reflecting on your broad problem solving challenges and solutions, you can increase your awareness of issues and creativity for new solutions.

Case studies are an excellent way to improve competitive intelligence skills. Case studies (like math word problems) serve as proxies for the common problems in life. Though we may prefer simplicity and unambiguous problem statements, addressing complex issues is more valuable to our customers. Fortunately, building on the basics that we have learned, we can also learn how to deal with competitive intelligence “word problems.” It takes practice, sensitivity, and, most importantly, a focused perspective on solutions.

In the end, a competitive intelligence professionals gains personal credibility and improves their capabilities when they demonstrate competence in solving complex problems.

Competitive intelligence is indeed a “word problem.” We would not want it any other way.

“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)