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How to Achieve Profit Growth for Your Brand Through Buying the Right Sizes
Michaela Wessels, CEO and founder of Style Arcade goes into the mechanics of how size curves dramatically influence your bottom line, and how to accurately calculate your ideal size ratios.
It's an astounding stat that profit margins for eCommerce business are impacted by 20-30%, just by buying the incorrect size range. There's a very specific reason why the best eCommerce stores and online retailers are obsessed with Size Availability. And, if you haven’t heard of it - or you’ve been missing out due to laborious manual reporting - it’s imperative to learn how this significant element of the buying process is impacting your profit margin.
In a past life as Director of Planning at at The Iconic, one of Australia's fastest growing ecommerce businesses, I remember clearly Chairman, Pat Schmidt asking me early on, “Michaela, why will size availability be one of the metrics you care most about in this business?”
This was where the impact of sizing truly opened my eyes. As we know, 80% of sales come from 20% of your styles, so if your weighted Size Availability on your New Arrivals page starts to drop below 90%-80%, you will start to see a significant decline in sales.
As soon as I saw products drop in availability, I knew we needed to find new deliveries and start to discount the fragmented products to prop up the sales velocity.
An unbelievable amount of profit is lost every year to broken size curves. The reason? It’s a very difficult metric to analyse with generic intelligence (BI cubes/Excel). It’s data crunching and it’s time bound. You need to be able to analyse the product performance before you sell out, before you discount, and ideally while it's having time in the limelight.
You need to be able to analyse the product performance before you sell out, before you discount, and ideally while it's having time in the limelight.
The Cost of Size Breaks
In most size curves for common products like tops, dresses, and pants there are around 5 sizes: XS-XL, size 6-14, and 24-32 for example. When a product launches online, the Size Availability is at 100%.
Then, when a size breaks in the curve, meaning when you sell out of a size, your Size Availability immediately drops to 80%. However, if the size that breaks is your most popular size for this particular product, then your Size Availability can actually equate to as low as 40%.
Your sell-through will be worse off than you think if your most popular sizes disappear soon after you’ve launched them online. Consider a weighted approach to Size Availability, and start allocating according to the size sell-through data of each category.
The Golden Rule of Size Availability
All fashion brands, whether their new arrivals drops are weekly, monthly or seasonal, need to have 100% size availability for a minimum of 2 weeks after they launch the product. If your sizes break within those two key weeks, you will rapidly start to lose profit on that item.
How Size Breaks Occur
It’s commonly thought that in order to make sales and growth, all you need to do is a) get people to your website and b) offer them great products. This is true, however, the science of success falls apart if, at this last mile of the customer journey you fall short with the wrong size curve.
That's it, you've lost them.
The acquisition budget you’ve spent luring your customer to a product and convincing them to buy, falls completely flat once they find that their size is sold out - that’s it, you’ve lost them.
Typically online customers don’t browse too much longer after their disappointment, and you’ve lost the potential ROI on your CPA.
So, as your new arrivals drop it's imperative to have 100% size availability in the first two weeks so that you capitalise on potential conversions coming to the site. It’s a costly exercise to incorrectly calculate your size curve and to lose new customers like this as the attempted acquisition becomes about 10 times more expensive than it should be.
The rule of thumb is to have your Size Availability as high as possible, for as long as possible - ideally 2-4 weeks will give you the highest chances of converting and capitalising on acquisition costs.
What Really Happens When the Curve Breaks
You're breaking sizes in your product, here’s what happens:
1. Conversion rate decreases
2. Your sales decline
3. You missed growth opportunities while not being able to capitalize on traffic
4. Your product’s performance as whole declines due to fragmented sizes
5. You run a high risk of judging products as poor performers when they're not
As soon as one size breaks, it tends to slow the sales of the other sizes. So ultimately, when it comes to reporting, you’ve muddied the water of the analysis of your range. This is because you’re not sure whether the slow sales were due to customers not liking the product, or you just applied a bad size ratio and it broke too early.
While working with a dress brand a few years ago, we found that a white bodycon dress (typically the most unforgiving kind of dress) would sell more in XS-S, much more than it did in M-L. The product didn’t perform as well on the whole, so we didn’t buy it again. We in fact realised too late that it wasn’t because the customer didn’t like the dress, we just didn’t have the right size ratio.
...it wasn’t because the customer didn’t like the dress, we just didn’t have the right size ratio.
Having super accuracy means you don’t muddy the water, you know exactly why the numbers read as they do. You want to report on how products rank due to their actual merits, not because of a size curve conundrum.
The Cost of Over and Underbuying
Overbuying means that when you get to the End of Season Sale, you’ll automatically have to discount more heavily to move overbought sizes. This can be a big profit sink for the business.
Whether you over or underbuy, one is as bad as the other.
Underbuying incurs profit on missed sales. Whether you over or underbuy, one is as bad as the other. What happens when underbuying is that you never truly understand the true sales potential of that product.
This is why so many omnichannel retailers now jump to get their store stock on hand integrated to complete their size curves. They’re seen as available on the website, and the teams are fulfilling from store, so that they never miss the opportunity with online customers.
The Opportunities of Super Size Accuracy
Further back that I care to remember, I was one of 100 merchandise planners at Truworths in South Africa, a retailer which boasts 700 department stores. We used to sense check the size curves quarterly; the system would generate a recommended size curve, and we never questioned the logic. Now looking back I would disagree with the mathematics.
I always had a gut feeling that incorrect sizing can impact profit by 20%. Imagine if you were given the opportunity to gain back up to 20% of your profit!
And that's why Style Arcade exists. Most of our customers go live with a starting size curve accuracy around 83%. So yes, a 17% inaccuracy may not seem like a lot, but it counts when your customer base is growing all the time. Getting your size curve correct can elevate your accuracy right up to 93%, the main point being you need to keep analysing, reporting and re-adjusting as you go, and as you grow.
You also have the opportunity to recognise when you can extend your size ranges and add larger or smaller sizes on either end of your curve. With a clear mathematical guide, if you look at your ratio and the end sizes are more than 15% of total sales, then there’s an opportunity for you to add in a fringe size.
How to Accurately Calculate your Ideal Size Ratios
1. Measure the sales units ONLY while sizes are in stock to ensure accurate representation of your true demand size curve
2. Measure the sales BEFORE you discount the style so you don't artificially inflate your sales with discounting
3. Measure your sales when you have a statistically significant sample data set for each product, for example when you have sold at least 30% of the total bought units
4. Measure the size curves at the most granular level possible for example by Channel, By Store, By Category, By Subcategory, By Silhouette and even By Colour. Why? Because each store has a different sizing profile, so does each colour and silhouette (mini dress vs maxi dress for eg)
Now that I know what precision logic can be achieved, it’s what led me to develop Style Arcade.
This is the first time that there is near-perfect logic out there, in the most sophisticated systems. You can accurately predict performance and start buying according to real-time data. While you may never have 100% size accuracy, you can get close.
