Hard numbers can force you to rethink your inventory beliefs

This article originally appeared in the February 2015 edition of INSTORE.

business advice for jewelers from David Brown

I’ve just finished reading the excellent Stephen J. Levitt book Freakonomics. For those of you who haven’t read it, Levitt is an economist who uses data to dispel many of the myths and assumptions we have in society. He looks into the real cause of the drop in crime rates over the last 20 years and shows that it’s not just about better policing. Levitt has also used data to prove that teachers were amending student exam results to improve their own credentials and used results data to uncover everything from sports cheating to how your name can affect your ability to get a job.

His analyses got me thinking about the huge amount of data about store performance that most modern jewelers have at their fingertips and how little of this data is actually used. Not only does the average jeweler have sufficient information to tell them how profitable they are, they can also pinpoint where that profit is coming from in terms of inventory, staff, store location and even from which parts of the store the items are sold.

Sadly, without data we tend to make our decisions in the same way the people of New York came to assume their crime rate had fallen: hunch or intuition, or common beliefs. Yet most widely held beliefs are not based on evidence.

Most of your product
is new to your
customers because
they don’t see it as
often as you do.

Let’s relate this back to jewelry and the challenging task of finding new inventory. Jewelers both enjoy the buying process and dread it — as much fun as it is buying new product there is always the fear that it will sit around for months or years to come, weighing on cash flow. Unfortunately, a few common beliefs affect how jewelers make their buying decisions. They include:

My customers want to see something new. Let’s define “new.” Your reports will show you the percentage of new product you currently have in store … but that definition isn’t strictly speaking correct. It shows you what is new to your store … not what is new to your customers. In reality (and we need Mr. Levitt to prove this) most of your product is new to your customers because they don’t see it as often as you do. If a customer visits your store only once every three months then by definition an item that arrives in the day after their last visit will be new to them … and that’s assuming they see it. Most customers only see what they are interested in at that point in time. If they aren’t buying a silver bracelet they most likely won’t look at your silver bracelets. Given only 2 percent of the population is in the market at any point in time, then by the time most people get around to looking at your silver bracelets they all will seem new!

I can’t sell this to another customer — what if the first customer sees them wearing it? Let’s say you sell five versions of the same ring in a town of 20,000 people. The odds of one of the five meeting each other is 0.025 percent (5/20,000). That’s not precise but it’s simple enough for our benefit. You then need to disregard the times that both people won’t be wearing the same item when they run into each other, which, depending on the item can be quite high as well. Now again this is simplistic, there may be a higher correlation of people who shop with you knowing each other, a greater chance they live within the same block etc, etc, but I think we agree that if our starting point is 0.25 percent, no amount of data manipulation is going to turn that into a 10 percent risk.

Sifting through data can be mind-numbing. But the next time you find a business assumption springing to your lips, ask yourself, “Is this real, or do I have the data to prove otherwise.” The findings may be fascinating.

David Brown is president of the Edge Retail Academy. To learn how to complete a break-even analysis, contact This email address is being protected from spambots. You need JavaScript enabled to view it. or (877) 569-8657.

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