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Should I get an MBA? Part Two: Nothing lies like numbers

In our continuing series about whether entrepreneurs, small business owners, and solopreneurs should consider an MBA degree, I’m not trying to talk anyone into an MBA. It costs a great deal of money and eats up huge chunks of your time. But rather than discuss the downside, I want to focus on the deficits you face in the day-to-day running of your business. In my last post on the subject. I talked about how an MBA gives you tools to fully realize the value of your business. I’m mainly talking about traditional programs, not necessarily those accredited online MBA programs out there. Here, I’m going to talk about numbers. That’s right. Numbers. And all the things you can do with them.

Because, as your business grows and your responsibilities change, your business is going to look a lot more like spreadsheets and charts. That’s what life looks like at the top of a successful company. In fact, if you’re looking for investors at the start-up stage, then that’s what your company looks like now.

And the thing about numbers is that they lie. I had a finance professor who on a regular basis would boom out some version of the following sentence:

“If someone shows you a spreadsheet, they’re lying to you.”
“If someone shows you prospective financials that have decimal places, they’re lying to you.”

And so on. Since numbers impact everything you do in a business, understanding what those numbers mean is vital to making good business decisions. More after the break.

I’ve probably told this story here once or twice, but, since Oscar Wilde said that if something is worth saying once, it’s worth saying a thousand times, I’ll repeat it.

Many moons ago, when I worked at a small marketing communications firm, we were brought to a huge vendor hoe-down by one of our clients, Iomega, who then was riding the downside of the wave to ultimate bankruptcy. Sponsored by the director of marketing, all of us gathered in the outskirts of Salt Lake City to hear a presentation on their new advertising campaign. We all got to see the new print ads, the new Internet ads, and we were all handed a thick sheaf of papers covered in charts and numbers.

This sheaf of papers was all the focus group research on their new ad campaign. Now, to be honest, as the ads made their rounds (there were about 40 of us in the room), people mumbled sotto voce about how crummy they were. Why? Because the creative was at best uninspiring. Seated next to me was a direct marketing guru who had a regular, prominent column in Direct Marketing News, the premier trade publication in the industry (who cares, right?). He looked at me and said, “Wow. These are lousy.” “Totally mundane,” I replied, “a step down.” Which, indeed, they were, because this new ad campaign was replacing a vibrant, colorful, in-your-face meet-my-attitude type of campaign.

So out come the stapled sheaf of papers with all the “results’ of the focus group and Iomega’s director of marketing waltzes us page-by-page through these results. All the numbers, of course, prove that the Iomega ad is more effective than competing ads: more memorable, more persuasive, more effective, greater taste, less filling. I wasn’t a numbers guy at the time, but things didn’t look quite right. But the focus group responded better to the Iomega ad, sure.

Well, I won’t go into all the methodology problems that I now know because of my MBA courses in marketing research and all my post-MBA work in marketing research. Just focus on the numbers. The difference between the pre-MBA me and the post-MBA me is that the pre-MBA me sat with crossed-eyes and knitted brow wondering why these good results were underwhelming. The post-MBA me would have whipped out a calculator and started punching out chi-squares to determine if the numbers had any statistical validity. I have done so many hundreds of thousands of chi-squares over the past few years that I can practically do them in my sleep.

Well, the post-MBA me actually did run chi-squares on the numbers in the sheaf of papers. Today, about two hours ago. And with only a couple exceptions, those chi-squares showed that there was no difference between the numbers generated by the focus group and the numbers you would expect if you generated them totally randomly. In other words, in just about every category that showed that the Iomega ads were somehow more effective than competing ads, the numbers had no statistical validity. They were no more valid than if you asked the focus group to wear blindfolds and randomly pick one of five ads sitting in front of them.

Is it any surprise, then, that the campaign was a failure? Complete, total, face-plant-on-the-sidewalk failure. If anyone had bothered to run statistical tests on those focus group numbers, maybe they would have had the gumption to ask the ad company to go back to their whiteboards and try again.

Okay, okay, I know what you’re thinking. So what? Since when will I ever have to deal with focus group numbers? Actually, the answer is sooner than you think. You’re going to see “results” all the time that look like you’re losing or look like you’re winning, but they have no validity. For instance, a sometime client just sent me the click-through results of his Google text ads. There was a high performer (0.22%) and a low performer (0.08%). When I ran statistical tests on all the text ads, sure enough, the test showed statistical validity. When I performed statistical tests on the text ad results minus the low-performing ad, it showed they were statistically equal. So the numbers weren’t sufficient enough to ground a decision as to which ads were better. Even though the numbers argued otherwise.

Let me tell you another story, one that I mention briefly in the book. I was hired as a consultant to whip up a municipal proposal and a business plan for a new business to be located on the Santa Monica pier. I was in charge of the writing, I had a copywriter working under me, and an accountant was in charge of all the financials. At the end of the process, I and the president of the fledgling business flogged the final business plan into shape.

That’s when I got my first gander at the financials. I had just come off an MBA, so the first thing I did was draw my calculator from its holster and fire away at the numbers. So I ran a bunch of numbers on the investment and the earnings and calculated a net present value of the investment these guys were looking for (about 2.5 million dollars) and got a return of . . . 2 percent. None of the standard numbers you run on prospective financials — payback period, average accounting return, internal rate of return, net present value — yielded anything but the most anemic investment prospect you can imagine.

Understand that calculating things like payback period, IRRs, AARs, in NPVs is something that investors do all the time. They unholster their hundred-and-fifty-buck financial calculators and buzz away at these numbers whenever any schmoe hands them a spreadsheet swarming with year-over-year expenses and revenues.

Nobody in their right mind — unless you’re talking about investing in a film — would greenlight a 2 million dollar investment in a highly risky start-up for a return of 2 percent. Passbook savings give you a better return.

So I returned the financials to the president and said that no matter what I did on the business plan, every investor he talked to would show him the door before even letting him in. He thought showing a return and a payback was enough; he had never heard of NPV’s or IRR’s.

Here’s the rub: practically every investor you’ll talk to has heard of NPV’s and IRR’s and they can calculate them better and faster than you can figure out the tip at a restaurant.

So I sat down with his spreadsheet and, good little MBA that I was, populated his spreadsheet with formulas. One column said payback period, one said IRR, and another said NPV. All he had to do was mix and match the numbers in his spreadsheet and TA-DA! all the measures of returns on the investor’s investment would show up. He could make any change he wanted to: he could change revenues, expenses, original investment amount, ownership percentage, anything, and his spreadsheet would tell him where he was at in terms of making money for his investor.

I cannot count the number of start-ups I dealt with in my pre-MBA days where, when I go over their financials, are bloodless in their returns, which explains why they bit the dust unfunded.

Of all the things I learned in my MBA program, the ability to work with numbers intelligently and tease out the truths and the lies they contain is the most valuable. In most every class and on most every day, I would say to myself, “Boy, I wish I had known that two years ago.” Or three years ago, or four years ago.

And that realization hit me most when we were working with numbers: statistics, statistical tests, chi-squares, marginal cost analysis, indifference curves, everything and anything to do with finance, queue theory, six sigma, and the list goes on.

Numbers are not truth, but a source of truth. Like everything else in life, they only produce value when you work at them.

In many ways, the Madoff scandal is a giant neon sign about the capacity of numbers to deceive. The most startling aspect of the scandal is that every investor or investment organization that had formal systems of due diligence not only didn’t invest with Madoff, it took them minutes or hours to figure out the numbers were wrong. It’s all the schlemiels who either didn’t or couldn’t subject Madoff’s numbers to scrutiny that lost their shirts. And pants. And shoes. And frilly underthings.

That’s the difference I’m talking about.

I have said in other places that “truth” is not something you’re given: it doesn’t come out of the sky, it isn’t published in the newspaper, it isn’t broadcast on television or radio, and it doesn’t come in the form of a PowerPoint or a spreadsheet. Truth is a discipline, not a thing. It is hard work and requires training. It is the method, the system, and the sweat you bring to every fact and opinion you’re exposed to.

And if business is the science and art of making good decisions, then good decisions are always rooted in the truth, and the truth in all the numbers you have to deal with can only be gotten at through training and discipline. Otherwise, they’re just numbers.

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3 Responses to “Should I get an MBA? Part Two: Nothing lies like numbers”

  1. Don Kassner says:

    Excellent post.

    Often time, when students encounter some of the more challenging MBA courses – like economic methos & analysis or managerial finance – they often wonder when they will ever use them. They lament about the challenge of “doing the math”. I always tell them you never know….you may face a situation in your career where you will need to at least understand what the numbers mean.

    Don Kassner
    Andrew Jackson University

  2. Darrell Ross says:

    You do a great job to describe that mental training one goes through in learning finance and statistical analysis. One learns to formulate assumptions and then test them rigorously using a battery of techniques. When I was an undergrad pursuing a BBA, I switched to an emerging field called Decision Sciences, which trained (dare i say “warped”) my brain in a similar fashion. These have become some of the most useful skills during the course of my career.

    Darrell Ross
    Enablus LLC

  3. Dabert says:

    This is right here, in the present, not the future.


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