This issue is part of a new series I’m doing where I bring in outside subject matter experts who specialize in working with fashion brands.
A few weeks ago we covered Pinterest Ads. This week I’m speaking with Kamal Razzak, who runs a media buying and creative agency that helps fashion brands scale through smart, high volume creative testing.
Kamal dropped so much knowledge that I had to make this a two-parter. In this week’s free issue he’s going to talk about common reasons that fashion brands plateau around $1-5M/year in annual sales (and how to break through the plateau).
Before we get into it, I want to thank this week's sponsor: WorkMagic. WorkMagic is a measurement platform that uses automated incrementality testing to calibrate attribution and MMM, giving brands accurate full-funnel insights based on true incremental impact.
In next week’s members-only issue, Kamal is going to share the exact creative testing strategies and campaign structures he uses with clients, as well as how and where to use AI to enhance volume of output and learnings.
The entire series is going to be packed with valuable info–Kamal has worked with brands like PerfectWhiteTee, Ripley Rader and BeyondNine–so click here to become a member to make sure you get part 2.
Here are the top five reasons that Kamal sees fashion brands plateau at $1-5M in revenue, and how you can avoid or move past these issues. This information has been condensed from a longer interview with Kamal.
Reason 1: Focusing On The Wrong Numbers
Kamal sees a lot of brands attempting to scale the wrong ads, killing potential winners prematurely or leaving potential winning ads running at sub-scale daily budgets. Why? Because brands aren’t focusing on the right metrics.
CPMs, clickthrough rate, conversion rate and video engagement metrics like hook rate are all important data points about an ad. But there are two north star metrics a brand should be focused on: 1. Account-level ROAS and 2. Ad spend on creative.
Ad spend tells you which ad(s) Meta’s algorithm thinks have the highest potential for scale. Many brands will drop 10 ads into a testing ad set, then turn off the highest spender after 24 to 48 hours if that ad doesn’t hit the brand’s ROAS target.
There are three issues with this strategy that prevent many brands from scaling:
- An ad that sucks up budget might have a below-target ROAS, but it could be lifting the account-level ROAS while enabling scale.
- Many brands’ account-level ROAS targets are based on the wrong internal KPIs, so they’re higher than they should be.
- The other ads in that testing ad set–the ones achieving higher ROAS at lower spend–will almost always see ROAS declines at higher spend levels.
Instead of using a long list of engagement benchmarks to decide if an ad is a “winner”, use this rule of thumb: the ad with the most spend is the winner. If it hits your ROAS KPIs, you can scale it up further. If not, that round of testing did not produce any true winners.
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Meta's in-platform analytics can help you understand ad performance, but they don't tell you the true impact of your ad spend.
If you're spending across multiple acquisition channels or multiple sales channels (Amazon, wholesale, etc), this is doubly true. The "meh" in-platform results you're seeing for your ads could be hiding powerful incremental impact.
There are a lot of solutions that claim to solve this problem by serving as the "source of truth" for attribution. Here's why WorkMagic stands out:
- WorkMagic is the only platform that calibrates both MTA and MMM with incrementality testing to give you the full picture.
- By running incrementality tests, WorkMagic captures the actual incremental lift of your marketing efforts across DTC, Amazon, wholesale, and retail. For example, Liquid I.V. found 62% of TikTok-driven sales actually happened on Amazon.
- They give advanced metrics like new customer incremental ROAS (NC iROAS) and incremental POAS (iPOAS), helping you optimize for profitability.
- WorkMagic also predicts ROAS at every spend level, identifying the optimal daily spend for each channel before diminishing returns kick in. Making cross-channel budget allocation simple.
Join brands like Graza, Steve Madden, Aviator Nation, True Classic and Branch Furniture who are improving MER by up to 20% by using WorkMagic.
Click here to book your demo.
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Reason 2: Creative Volume Is Too Low
Kamal will often take on clients who are producing 10 to 20 new ads per month, then wondering why they are struggling to scale past $1k/day in ad spend. And on closer inspection, those 10 to 20 ads are really four or five ads in Meta’s view, because they’re iterations of the same handful of assets.
Strategic creative testing is the fastest way to increase daily ad spend while maintaining profitability targets. This means testing hundreds of ads each month across a diversity of formats–static and video, raw and polished, different creators, different products.
The fashion category can give many brands a false sense of security. It’s one of the few categories where a compelling product will sell itself, regardless of the creative format or strategy.
But many brands plateau around $1 to $5 million a year in sales if they don’t mix up and diversify their approach to ad creative.
Reason 3: Strictly Following Meta’s Best Practices
Hot take from Kamal that I agree with: many of the “best practices” shared in the DTC community are actually best practices for Meta’s revenue, not for the brand’s profit.
The foundational best practices–using dynamic creative and broad targeting–do work. But the idea that a fashion brand can scale past $1-5M off the back of a single “banger” ad is false.
Ads fatigue faster in the fashion category due to inventory constraints (ROAS will often drop once half of your most popular sizes are sold out), trend and seasonality.
You can’t reliably plug new products into a winning format to scale a creative concept, because product is a powerful variable in the success of an ad.
And customers shop this category with emotion first, not logic. So you can’t rely on “features and benefits”. An “Us vs Them” ad probably isn’t going to work in this category, even though it often crushes in verticals like CPG and supplements.
Reason 4: Testing Cycle Is Too Slow
Some brands are able to generate enough creative output to test 20 to 30 new ads per week, but they can only afford to test six to 10 new ads with their current testing framework.
Think about it: if you are spending $2k/day on Meta ads, “best practice” is to allocate no more than 10-20% of that budget towards testing. That is $100-200 per day–enough to test about one or two ad sets worth of ads.
With these limitations, a brand can go weeks before finding its next winner–right as its prior winner has fatigued enough that it needs to be turned off. This is how many brands get stuck at a plateau, unable to get multiple winners running together.
To break out of this cycle, Kamal helps brands optimize their testing approach for volume:
- Prioritizing testing variables that are going to drive the most upside
- Selectively using features like flexible ad units to test more variables with the same budget
- Helping brands decide when to use cost controls vs highest volume based on their budget, risk appetite, and the goals of the test
Reason 5: Testing The Wrong Variables
Because fashion brands tend to have broader assortments than, say, beauty brands, the question of “what to test” becomes even more confusing. There are (almost) no “wrong” variables to test, but every test needs a strong hypothesis behind it.
Headline testing is a great example of this principle. Headline testing can be high leverage if each version speaks to a different customer segment, or hits a different emotional trigger. But if you’re simply running five iterations of the same idea (“shop our new arrivals”, “step into spring”, etc), it’s a waste of time.
Product testing is another example. Your sales data often reveals which products are going to perform best in ads (your best sellers). You could test every product in your assortment, but you’ll go further, faster with a strategy that integrates past learnings, market trends and inventory information.
What Works Instead: Building A Creative Engine
Kamal helps his clients build creative engines–a repeatable system that ships high quality, data-informed ads every week. With the right systems, brands can do this without burnout, without missed deadlines, and without just “testing to test” because you’ve run out of good ideas.
Kamal’s team builds a pipeline where ideas move quickly from research → concept → design → testing. Brands have guidance on where to focus net-new concepts and where to develop variations–up to 30 per concept, not just four or five.
We’ll dig into the other pillars of the creative engine–account structure, modular ad creative, and strategic use of AI–in next week’s members only issue. Click here to become a member.
And if you’re interested in working with Kamal, you can click here to book a call and learn more.