How to sharpen your analytical thinking—even if you’re "not a numbers person"

Analytical skills didn’t come easily for me. Luckily, I found my way through. Here are 11 concepts to sharpen your analytical thinking.

👋 Hey, it’s Wes. Welcome to my weekly newsletter on managing up, driving growth, and standing out as a high performer.

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In this week’s newsletter, you’ll learn principles on how to become more analytical:

1. Patterns & pattern breaks

2. Absolute numbers & percentages

3. Variance

4. Expected vs actual

5. Percent contribution to whole

6. Zoom out to a broader scale

7. Check profit margins

8. Consider incentives

9. Figure out the range

10. Investigate why—and what to do about it

11. Poke holes in your own logic

I originally published a version of this essay in July 2020. I’ve expanded the post. If you find it helpful, please share with friends and coworkers. Enjoy.

Read time: 9 minutes

Analytical skills didn’t come easily for me.

In college, my least favorite classes were quantitative. It was a miracle I even scraped by.

Which is why I was shocked when Gap Inc wanted to hire me, upon graduation, to be an analyst. The job was 100% about numbers.

Logically, this seemed like the worst possible idea.

I thought, “They want me to manage millions of dollars of product and optimize profitability? Using real money?”

Once I got over the surprise, I knew I could do it—I would just have to work harder than everyone else.

It’s been 15 years since I worked at the Gap headquarters in San Francisco. And still, every week I use the analytical skills I learned when I was there. My career is in marketing and entrepreneurship, but I still use these skills weekly. I’m fortunate that I got to benefit from 40+ years of Gap’s best internal L&D training on a skill that’s rarely taught directly.

When I started, I’d look at numbers and not know what to do. I couldn’t see the “story” the numbers were telling me.

But over time, I learned.

A manager would hand me a spreadsheet with size 8 font and never-ending rows of metrics, and I could make sense of it. It was empowering. It was like a new world opened up for me.

If I can learn it, you can too.

Being analytical isn’t only about numbers

It’s shocking to me that we’re not taught how to be more analytical, critical thinkers in school. Some of us are lucky enough to learn on the job—sometimes with internal training programs, but usually thanks to patient coworkers. Some folks teach themselves over time.

Here’s what I’ve come to realize:

Analytical doesn’t necessarily mean quantitative. I think most of us, when we hear analytical, immediately think of metrics, spreadsheets, dashboards, etc. To be clear, being quantitatively-minded is absolutely useful. But the underlying analytical mindset is separate from learning technical tools, or from working with spreadsheets.

Most of us can benefit from reviewing a few concepts that are helpful even if—especially if—you don’t want to go super deep on quant. For example, I have no desire to take a course on data science—it would be overkill for what I do. But I’m incredibly thankful that I have a first principles-based foundation of analytical thinking.

I believe being analytical isn’t about numbers—it’s about having a healthy sense of skepticism. It’s about getting curious about the “why.” It’s about logical reasoning. It’s about validating your hypotheses—and having hypotheses in the first place. It’s about looking at multiple data points (qualitative and quantitative) to get closer to the truth.

The benefit of developing an analytical mindset is two-fold:

1. It will help you make more informed decisions

2. It will give you a competitive edge among your peers

After my role at Gap Inc, I got hired for a competitive position in brand marketing in the beauty industry because I was able to demonstrate stronger analytical ability than other marketers. This informed my point of view and helped me stand out.

The concepts below are ones I come back to again and again in my own work. Let’s dive in.

1. Patterns & pattern breaks

One of the first things to do is get a lay of the land. See the norm, what sticks out, what needs a second look. You want to start putting together your own point of view. To do that, you need to notice patterns.

• What’s “normal” around here?

• What sticks out?

• Why is it sticking out?

• How much does it stick out compared to everything else?

As you answer these questions, you’ll start to piece together the puzzle.

2. Absolute numbers & percentages

Look at both absolute numbers and percentages. If you only look at absolute numbers, big numbers seem good and small numbers seem bad. If you look at percentages, you’ll see the relationship between the parts and the whole.

3. Variance

Variance is about change: change from the baseline and changes over time.

• How much did this change month over month?

• This month versus this month last year?

• Was the variance in line with our projections or industry’s growth—or did we outpace or lag comparatively?

4. Expected vs actual

“We saw 17% growth” might seem like it’s worth celebrating—until you realize you forecasted 30% growth.

Let’s say you’re reviewing sales data for cashmere sweaters at J.Crew. You invested in improving the product quality of the sweater and ran multiple marketing promotions for this new cashmere. So you built a higher forecast to account for these investments.

This is where looking at expected versus actualized numbers is useful. Even if you’re handed a spreadsheet with sales data without any context, you can spot that the expected vs actual had a big difference.

Then you can verify your hypothesis by looking at the cost of goods sold (COGS), to see that the cashmere cost was double that of previous years.

5. Percent contribution to whole

This helps you see the relative importance. Is X a tiny sliver of the pie, or the majority of the pie? You can look at percent contribution from the perspective of variables including:

• Dollars: “X accounts for 55% of total dollars.”

• Volume: “This accounts for a quarter of units sold.”

• Top hits: “These five pieces of content drove 80% of all new visitors.”

6. Zoom out to a broader scale

At Gap Inc, we looked at trends in the near term and over a 3-5 year period to see peaks and valleys.

For example, if white t-shirts have never sold more than X units in the past 5 years, what makes you think they will sell more this season?

It doesn’t mean you can’t beat a previous peak. It just means you’ll want to have rationale and a logical basis for why you expect a new all-time high.

7. Check profit margins

When you see huge revenue numbers, look at the profit margin. A company can make $5 million in revenue, but spend $4.9 million. Look at gross margin compared to the industry, prior years’ history, and the company’s strategy.

Low profit margins isn’t necessarily good or bad—it simply is. If you see low profit margins, you don’t need to freak out.

For example, certain industries have lower margins as the norm. Some companies might have a strategy that’s intentionally focused on growth over profitability. Venture-backed tech startups are a good example.

8. Consider incentives

Be mindful of cherry-picked data points. I remember in one of my early roles, my manager came from McKinsey, and she asked me to make a business case for a new product launch. I was creating the slides she would present to the CMO.

I said, “The data doesn’t support going in this direction.”

She said, “The leadership already knows they want to do this. And there’s always a way to make the data support anything.”

Once she said this, I knew she was right. This was both empowering (because I could immediately think of what number to cite for my PowerPoint slides) and terrifying (because, um, there were 5x as many data points supporting the opposite narrative).

Ask yourself:

• What are the incentives of each party?

• How do they benefit from you believing this narrative?

• Where might they be cherry-picking data points to show a specific story?

For what it’s worth, now looking back, I don’t think it was necessarily a bad thing that the data didn’t support doing the product launch. When you’re building something new, there’s a fair amount of creativity, intuition, vision, and taste that goes into deciding what to create.

Data is often about what’s actualized, so it’s backward-looking. This might be helpful for forecasting the future, but also might not be, depending on the situation. As always, use your judgment.

9. Figure out the range

If you don’t know ranges, you can’t tell if something sounds too good to be true. If you know a rough range, you can get a sense of if you’re on the low, middle, or high end of the range.

Recently, an exec was walking me through their funnel and pointing out the conversion of a few steps in the product.

He said, “This step converts at 75%.”

I said, “There’s no way this converts at 75%. If it does, we should stop everything else immediately and pump as much high-quality traffic to this page as possible.”

I knew the range was off—a step like that in the product flow would maximum have like, 30% conversion.

The exec checked the math. He said, “Actually, you’re right. Good catch. It’s converting at closer to 15%.” Ah, that sounds more realistic.

I want to point out that this exec was super competent. When you work with numbers often, you’re going to make some mistakes. (As I like to say, “If you own enough plants for long enough, you’re going to deal with an insect infestation eventually.”)

The concepts in this list are useful because (a) you train your spidey sense when a number feels off and warrants a second look, and (b) you can help catch errors in your team mates’ work, so you catch it before presenting the information more widely.

10. Investigate why—and what to do about it

When you notice a pattern or pattern break, what should you do?

Figure out the cause. Figure out the impact. Keep validating or invalidating your hypothesis until you get closer to the truth. You’re looking for clues that give you insights, that then help you form an assertion on what to do next.

Let’s go back to the example in item #5 (expected vs actualized numbers):

Let’s say you’re reviewing sales data for cashmere sweaters at J.Crew. You invested in improving the product quality of the sweater and ran multiple marketing promotions for this new cashmere. You built a higher forecast to account for these investments.

When your actual performance is worse than forecast, it’s not always obvious why. There could be many potential reasons:

• Did customers not notice or care about higher quality cashmere? (If so, you might recommend going back to lower quality fibers.)

• How good were the marketing campaigns? (Maybe the campaigns were mediocre, didn’t educate the customer, or the creative was poorly executed. If so, you might want to see if more a compelling campaign drives sales.)

• What other variables might have accounted for poor sales? (Perhaps in previous years, you offered classic fit v-necks and crew necks. But this year, you only offered over-sized sweaters. This might be too fashion-forward for your customer.)

• What changes in the market might have contributed? (Maybe a new “no-label” DTC competitor like Quince and Italic entered the market with a viral $50 cashmere sweater.)

Depending on the reason, your recommended action might be completely different. I filled out the “if so, you might want to X” for the first two bullets. Try answering for the last two bullets. You’ll see the “what should we do with this information” isn’t always clear cut.

When you notice anything unexpected, ask yourself:

• Why is this happening?

• What's the impact?

• What should we do about it?

The takeaway: Dig deeper on the numbers to get as close to the truth as possible AND embrace that you’ll need to use your judgement to decide what to do next.

11. Poke holes in your own logic

I love this Charlie Munger quote:

“I never disagree with someone else's opinion unless I understand their side better than they do.”

I regularly do this when thinking about my own logic when writing my newsletter. I feel a moral responsibility to share ideas I have conviction about, so I do a lot of stress-testing in my own mind. I have a thought—and my immediate next thought is “Why might this not be true?” And I basically go back and forth with myself. (I’m really fun at parties, I swear). If I run an idea by someone as a gut check, I’ll still try to go as far as I can myself to make the best use of their time.

This is why I’m a proponent of asking myself questions like:

• What are reasons not do this?

• What might I be wrong about?

• What might I be missing?

• What are the boundaries of this idea?

By understanding the boundaries of an idea, you can make better decisions about how something applies to your situation.

You don’t want to only see in broad strokes. You want to see in finer detail where an idea applies and when it doesn’t. You want to be able to say, “This works for X situations, but not Y situations because Z.”

It’s powerful to see nuances when others only see a binary yes or no—and it helps you pattern match more accurately.

Thanks for being here, and I’ll see you next Wednesday at 8am ET.

Wes

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