Harvard Business School Confidential

by Emily Chan

  The important point is to recognize that often an estimate could be derived so roughly that it has only one or two significant figures. For example, an eight-digit derived estimate like $25 million is apt to mean only that the number is somewhere between $20 million and $30 million. As a result, combinations of estimated data must not include excessive numbers of significant figures or decimal places. For example, when you encounter a number such as the $8,333,333 that came up in the calculation based on the $25 million market estimate, the appropriate interpretation is often “somewhere between $8 million and $9 million, but probably on the low side,” so it can be rounded to $8 million or $8.5 million. If further combinations (addition, multiplication, and so on), especially on a spreadsheet, are necessary, then, for simplicity, most people would choose to continue to carry the $8,333,333. This is OK as long as the final number presented is rounded appropriately. For example, you might want to know what the total would look like if the sales of $8,333,333 grew 40 percent. You could calculate $8, 333, 333 × 1.4 (that is, 140 percent) on your spreadsheet or calculator ($11,666,666) and then round the output to $12 million or a range for use in presentations and decision making.

  KEEP YOUR SENSE OF PERSPECTIVE

  As you use these detailed tools for estimating and checking data to try to get the data for strategy, it is critical to keep your perspective on what this all means.

  So What?

  I was watching television one night, and this conversation between a grown man and an eight-year-old girl really amused me:

  MAN (A FRIEND OF THE CHILD’S FATHER): Hello, Daisy. It’s nice to meet you. You look just like your mom.

  CHILD: So what?

  MAN: Oh . . . I mean you are as pretty as your mom.

  CHILD: So what?

  I believe this child will do well in data analysis.

  “So what?” is the paramount question in data analysis. Data is a means, not an end. The key skill is to be able to ask “so what” constantly, from the time you are planning your data collection to the time you are applying the data. You found out the biggest competitor has a 50 percent market share. So what? What does it mean to your hypothesis? Would your strategy be different if the share was 40 percent or 60 percent? Do you need to verify this data or is this ballpark estimate good enough? In fact, at BCG, we are not allowed to write any powerpoint presentation slides or draw any graphs without a “so what” as the title or at the bottom of the slide. The idea behind this rule is to force the thinking on “so what” for every single analysis.

  An example from my consulting work:

  A client of mine, a multinational beverage company, wanted to enter the bottled water business in Thailand. If the market turned out to be attractive, then the two options for entry were “greenfield” (start building from scratch) or “acquire a small brand and grow it.” The first phase of the project was to study the Porter Five Forces to confirm marketing attractiveness. The second phase was to compare the cost of the two options. One late night during the second phase, I finally finished assessing the cost of the greenfield option and the cost of acquiring the small brand (not including growing the brand). I was about to start evaluating the cost of growing the small brand when I realized that the estimated acquisition cost for the small brand was already more than triple the cost of greenfield. The difference was sufficiently big to force the conclusion that greenfield would be the cheaper option. Then it dawned on me that it was irrelevant to estimate the cost of growing the small brand because it would not change the conclusion. This realization saved the team and me a lot of time and effort.

  It is not always easy to tell “so what” right after you have collected (and verified) the data. For many kinds of data, benchmarking comparables could be a useful tool. For example, suppose you found out a company in the IT industry in Malaysia has a growth rate of 15 percent per year. Is this a strong or weak growth rate? In such cases, it is useful to compare this to benchmarks, such as growth of the IT industry in Malaysia, competitors in Malaysia and overseas, the company’s own history, or other companies in your portfolio.

  Sometimes, absolute numbers need to be converted into ratios for comparison. For example, a reader once asked me: “I want to invest in this company B. It has a debt of US$50 million. Is that high or low?” It is difficult to tell whether this is high or low because it depends on factors including the company’s current and future ability to repay the debt. To assess the level, the debt can be converted into ratios such as debt/equity or “times interest earned.”2 Then the ratios can be compared to benchmarks such as industry average, competitors’ ratios, the company’s own history, bank requirements, or rating agency requirements.3

  As another example, suppose you see that a company has $100 million net profit. To assess if this is sizable, you can compare it with the net profits of competitors or other companies of interest to you. You can also calculate market share or net margin (net profit divided by revenues) to assess the company’s market importance and its ability to turn each dollar of revenue into net profit.

  Decisions on what ratio to quantify and benchmark for each strategic study will depend on the hypothesis and framework you have selected.

  Stop Fooling Yourself

  As a trained engineer, I used to think a good strategist should be able to give a definite answer with confidence: “This is what the data says and this is what we should do.” No one told me otherwise until I overheard a senior consulting partner saying, “Vic (a starting consultant like me) is great. He knows to use the word seem and the phrase the data seem to show that even on day one. He has the right perspective on data. I can’t say the same about Emily.” A light bulb went on in my head. I realized that instead of pretending that the data is perfect and will give a definite answer, I should acknowledge that the data is imperfect and is most often only ballpark and directional. Using “seem” and “seem to indicate,” both in thought and in discussion, provides a constant reminder of reality and also stimulates more brainstorming, inviting constructive challenges from others that can increase the validity of the strategy.

  Don’t Work Your Data Too Hard

  If you massage the data enough, they will say what you want. This third point is very much related to the two that precede it. As discussed, you have to use data to deduce “so what.” But the data is imperfect—it includes all kinds of estimates. This will mean that very often, data can be deliberately manipulated to drive a certain direction or decision. Think of an eight-ounce glass containing four ounces of water: it can be described as “half full” or “half empty,” which can lead to very different “so what” conclusions. Or say the glass is 75 percent full of water. It can be rounded down to 50 percent full or rounded up to 100 percent full, which again can lead to very different answers to “so what.” Hence, it is important when you are analyzing data or when you are presented with analysis to keep this maxim in mind: “If you massage your data enough, they will say anything you want.”

  Notes

  1. Lord Kelvin (William Thompson) was an Irish mathematical physicist and engineer widely known for developing the Kelvin scale of absolute temperature measurement.

  2. These are just examples of ratios used in accounting and finance. Debt/equity = total liabilities of the company divided by total shareholders equity. It measures what part of a company’s resources is obtained from borrowing and what part is from owners’ investments. The calculation of times interest earned = (pretax income + interest expense)/interest expense. It measures a company’s ability to make the interest payment.

  3. Rating agencies such as Standard & Poor’s Corporation or Moody’s rate bonds issued by companies on their creditworthiness, such as AAA, AA, A . . . B and so on. Their ratings are guided by lists of objective requirements including the range or thresholds for various debt ratios.

  13

  “PLANS ARE NOTHING. PLANNING IS EVERYTHING”

  BE A STORYTELLER

  One way to look at the overall pl
anning process is that of a quest for “the story.” Frameworks list factors. Data help assess and analyze each of these factors. Outcomes of assessment and analysis organized logically tell the story of each factor. The overall planning goal is to organize the stories of the key factors into one overall story.

  For the “how” of constructing of a story, see “‘A’ for Articulate” in Chapter 3. This chapter takes up the “when” of constructing the story and then the fitting together of “how” and “when” in the discipline “elevator pitch.” Finally, it illustrates this concept with an example.

  When to Construct the Story

  If possible, start a story as soon as you have some initial data. The story at that point is fiction based on limited data. Officially, this fiction is called “hypothesis.” The framework and data collection are then designed to focus on proving or disproving the hypothesis. As you collect data, update the hypothesis and adjust the framework and data collection plan accordingly.

  Figure 13.1 Hypothesis to Strategy

  The iterative process is depicted in Figure 13.1.

  When I learned this technique during my first days as a strategy consultant, I did not believe in it. It bothered me that the initial hypothesis is often generated very quickly after only very superficial data research, and that once an initial hypothesis is generated, the framework and data collection are very much biased toward proving it. I felt it was just a way for strategy consultants to get to an answer (not necessarily “the right” answer) quickly. But I soon noticed the benefits of using a hypothesis:

  More often than not, the hypothesis based on limited data is directionally correct—it won’t be precisely true, but it is apt to point toward the final answer. This could be another manifestation of the 80/20 rule I discussed earlier.

  Even when the hypothesis is not correct, the process is iterative, so hypotheses, frameworks, and data collection are all quickly adjusted.

  The resulting focus is effective in facilitating and prioritizing the data collection process.

  Elevator Pitch

  The best strategists are experts at the “elevator pitch.” That is, imagine you found yourself unexpectedly sharing an elevator with the big boss or a potential investor; you should be able to tell the summarized version of the story during the ride. You should be finished by the time the elevator doors open again. The big boss or potential investor should be able to understand the logic and conclusion. The elevator pitch helps drive storytelling, because with such limited time, the logic must be tight and convincing. It also drives hypothesis iteration from the start. Since you are expected to have an “elevator pitch” ready at any time, you must have a hypothesis on hand at any time.

  To illustrate the concept of storytelling and hypothesis, I return to my strategic question, “Can I make money writing a book about HBS?” Say after some initial discussions with author friends and some Internet research, I find good market potential for a business book like this one. The easiest market to enter is China with its state-owned presses. The most difficult one is the United States, since it is a very sophisticated market. However, John Wiley and Sons, a major U.S. publisher, has a Singapore office, which may make it easier to reach that market. Authors are paid a percentage of the sales. The percentage is quite standard for new authors, but it is enough to be lucrative as I can write the book when I have time. It will not interfere with my other work or personal life. Hence the opportunity cost of writing the book is insignificant.

  Based on this line of reasoning, my hypothesis to answer the strategic question looks like this:

  I can make money writing a book on HBS if:

  I first enter the Chinese market with a Chinese version, followed by entering the U.S. market with an English version.

  I sign up a state-owned press for the Chinese market and then market the book to John Wiley & Sons.

  I get the standard new author deal with a time line that allows me to write at my own pace.

  Overall, this is inductive logic, as all the bullet points are parallel and all support the conclusion. Then each point can be broken down further for details. For example, the second bullet point can be broken down to further details:

  I sign up a state-owned press for the Chinese market and then market the book to John Wiley and Sons.Getting published by a Chinese state-owned press can build my book’s credibility.

  Credibility is one of the selection criteria at John Wiley and Sons.

  Therefore, getting published by a Chinese state-owned press can help me approach John Wiley and Sons.

  This is an example of deductive logic. The tree and the data collection will be designed to put more (not all) focus on proving and disapproving all the logical arguments leading to the answer of the central strategic question. Figure 13.2 shows an example of part of the resulting logic tree, with the hypothesis marked in bold italics:

  Figure 13.2 Annotated Logic Tree

  The branches of the tree should be further developed until all key details are covered. As more data is collected, the hypothesis, the framework, and data collection focus will be adjusted. A well-designed framework is collectively exhaustive. Therefore, even if the hypothesis turns out to be wrong, the framework is often still valid. It is only the focus of research that needs to be reorganized. The iteration of data gathering and fine-tuning continues until a satisfactory answer for the central strategic question is obtained.

  TIME LINE

  Storytelling is the tool to manage the content of the planning process. I find the “time line” a useful tool to manage the progress of the process itself. Figure 13.3 is an example of a time line format consultants often use:

  These are the key components of the time line:

  The timing of the project. Most strategy studies take three to six months.

  The key work modules and the timing for each. Two points to note:As explained before, data collection and hypothesis enhancement should be an iterative process rather than a sequential process. This can be seen by the overlapping timing of these work modules.

  Work almost always takes much longer than seems likely initially. Unexpected delays and unavoidable inefficiencies almost always happen. As discussed in an earlier chapter, I have a “1.5x rule.” If a planning module looks like it will take 1 month, I will put 1.5 months in the plan. If it looks like it will take 2 months, I will plan for 3 months. This is not to build buffer or slack. My experience shows that 1.5x is just barely enough time.

  This process does not include developing Key Performance Indicators for monitoring strategy execution. This is because there is usually a time lag between a report on strategy recommendation and implementation. The strategy report will need buy-in and approval from the board and top management. Then the plan may have to be finetuned before implementation planning can start. Hence the KPI for monitoring strategy execution is usually a separate follow-on effort with its own time line rather than lumped together with strategy study.

  Specialized software can be used for detailed time line planning. This kind of program can be very useful for details, but I have found Microsoft Excel sufficient for high-level planning.

  Interim and final reporting are formal communications to the stakeholders. They are key milestones within the time line. Such reporting is important to update the stakeholders on progress, get their feedback, and force the team to put the data and story on paper to help focus the rest of the work. But care must be taken, because interim reports are time-consuming to prepare. The number and timing of interim presentations should balance the cost and the benefit.

  Figure 13.3 Process Time Line

  THE BIG PICTURE

  Frameworks, data, logic, and time lines are about getting to the details. Details are critical for an effective strategy and strategy planning process. However, it is easy to get so engrossed by the details that one loses sight of the big picture. Here are a few best practices related to strategy that I have found invaluable whenever I feel overwhelmed by details:
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  Warren Buffett’s “smart people dumb people’ reality check. A friend of mine at business school told me a piece of advice he said he has heard directly from Warren Buffett. It goes something like this: Find an industry where average people can make a lot of money, not an industry where even smart people struggle to make a little money. This is indeed a major insight and a valuable sniff test. It absolutely does not replace analysis and all the details—Buffett himself has a team conducting industry analysis. But it does provide a valuable big picture reality check.

  Professor Bill Sahlman’s people test. Sahlman is one of the star professors at HBS. He taught me entrepreneurial finance there. One of the cases discussed in the class was Business Research Corp. In the case, Business Research Corp needed more capital to finance a new business opportunity entailing electronic delivery of Wall Street research to institutional investors. After the two-hour session, the class voted on whether to invest in this new opportunity. Most people in the class voted no. Professor Sahlman then said, “I might have voted no also based on all these analyses. But there is one critical point missing: the person doing this was Jeffrey Parker. Parker is not only a successful serial entrepreneur but also someone who knows the market very well. In entrepreneurial ventures when many things are uncertain, the entrepreneur makes a critical difference.”