Three pumpkins with different faces, representing 3 ways to categorize using MECE
iStock/CatLane

Marie Kondo, the queen of organizing and downsizing, has famously said, “The best way to find out what you really need is to get rid of what you don’t.” In Kondo’s case, this often means discarding things you don’t use—all those old shirts, children’s toys, and books you’ll never read (or read again). But in business, categorizing focuses less on the “don’t need” and more on the “really need.”

Categorizing data, particularly in today’s data-driven world, can make or break your business. Are you keeping up with competitors? Over-spending on inventory? Targeting the wrong customers? Data can tell you all of this—but only if you know how to apply critical thinking to interpret it correctly.

That’s where the MECE principle comes in.

The best way to find out what you really need is to get rid of what you don’t.

Marie Kondo

MECE (pronounced “mee-see”) stands for mutually exclusive, collectively exhaustive. In laymen’s terms, MECE categories have no gaps or duplicates—all shirts, toys, books, inventory items, customers, etc. are counted. And they’re only counted once.

If you’re not segmenting data according to a MECE breakdown, you’ll find it’s easy to overlook or count something twice. That sounds simple enough, but avoiding those gaps and duplicates is often easier said than done.

There are three common approaches to MECE data segmentation: classifications, formulas, and processes.

MECE Method 1: Classify

Think about how data is usually categorized in your company, field, or industry.

For example, if you’re a produce seller, you might one day find yourself wondering why pumpkin sales are down. To find the answer, you could look for patterns in age groups of pumpkin lovers (elderly French chefs?), seasonal peaks and valleys (did Halloween just pass?), or distribution channels (is a competitor courting your big supermarket partner?).

These classifications—age, seasons, distribution channels—each offer a different perspective and break down sales without any gaps or overlaps.

MECE Method 2: Use Formulas

For the second grouping option, look to formulas. Now, if you’re groaning over the need to use math (algebra, actually), there are two points of good news here.

  1. There are several easy formulas to choose from that you probably know already (like the very simple “price x quantity = sales”).
  2. It’ll be worth it—most business formulas will give you a fundamentally MECE answer.

So if you’re wondering about low pumpkin sales, you could multiply the amount spent per customer by the number of customers—that’s your sales. Once you’ve got that number, compare your data with last year and ask yourself some questions: Are customers buying fewer pumpkins overall since last year? Are there fewer customers? Or perhaps both?

MECE Method 3: Process the Process

A third MECE method starts with putting yourself in the customer’s shoes.

Imagine the customer journey of a pumpkin buyer. What makes someone decide to buy a pumpkin? Perhaps that ad you posted on social media attracted some people. How many users saw your Twitter posts about fresh pumpkins? Of those, how many clicked the link to go to your website or find your location? How many then actually came to the pumpkin patch? How many made it all the way to the register?

Each step in this process is separate—like math, naturally mutually exclusive. And if you cover every step in the customer journey, the steps also become collectively exhaustive.

The Value and Pitfalls of MECE

MECE breakdowns help with more than categorizing. Depending on the path you take to get your segmentation, you might also discover things about your customers. Do people really go to Twitter to buy pumpkins? Maybe consider another marketing channel. If you still find yourself drifting toward guessing, MECE can also be used with other critical thinking frameworks (like logic trees) for more systematic analysis.

Bear in mind, however, that MECE breakdowns aren’t fool proof. Categorize too specifically, and the resulting data may prove useless. Categorize too little, and you’ll be no better off than when you had a lump of raw data.

But for the most part, the MECE principle is helpful when analyzing data, sharing findings or proposals, and making important decisions. You can use it to help find out what you really need—and what you don’t.

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