The Ozempic Effect Retail Didn’t Plan For
Your size curve is going to be out of kilter
Every now and then, an operator or a leader says something that sticks. Not because it is presented as a big idea, but because it explains a few things that have been feeling off for a while.
A couple of weeks ago, during a conversation about changing buying behaviour, the topic drifted towards Ozempic (and other weight loss injections). Specifically, the possibility that customers might be losing weight faster than retail planning cycles are designed to handle.
We were not talking about health. We were talking about fit, size demand, and why forecasting felt harder than it should.
That conversation lingered. Not because it offered proof, but because it raised a question that felt worth asking.
The hypothesis
The hypothesis is simple.
If a meaningful number of customers are losing weight more quickly than usual, and not all at the same pace, then it is reasonable to expect some impact on how they buy clothes.
Not a sudden shift. Not a clean trend line. Subtle disruption to patterns retail relies on being stable.
Specifically around size demand, buying timing, and returns.
We do not know for certain that this is happening. But there are enough signals to suggest it might be.
Are customers harder to size than they used to be?
One possible effect is size uncertainty.
If customers are in transition, do they hesitate more before buying? Do they order multiple sizes to hedge? Do they stick to safer fits rather than experimenting?
Historic size curves may still be broadly correct, but are customers moving through them differently? And if so, does that introduce volatility that existing size planning does not account for?
These are not conclusions. They are questions prompted by behaviour that feels slightly harder to categorise than before.
Is demand being delayed, then compressed?
Another signal worth paying attention to is timing.
If customers know their body is changing, do they delay buying altogether? And when they return, do they do so in short bursts once things feel more stable?
That would create demand patterns that look inconsistent rather than lost. Quiet periods followed by spikes. Intent that disappears, then reappears all at once.
If that is happening, how well do forecasting models designed for steady replenishment cope with it?
Are returns being driven by uncertainty rather than dissatisfaction?
Returns data is often categorised in neat ways. Fit issues. Product issues. Expectation gaps.
But what if a growing share of returns is driven by something harder to label?
Not dissatisfaction.
Not quality.
Just uncertainty.
If customers are less sure of their size today than they were previously, does that naturally push returns up even when the product itself is fine? And if so, is that distinction visible in the data teams actually work with?
Why this matters for planning
None of this breaks retail overnight.
If weight loss injections are having an impact, it is likely to show up gradually. As slightly noisier size demand. As buying behaviour that feels harder to predict. As returns data that resists simple explanation.
All manageable in isolation.
More expensive when combined.
The risk is not reacting incorrectly. It is not recognising what is driving the change in the first place.
The honest position
We do not know yet.
There is no clean dataset that proves cause and effect. No benchmark that isolates weight loss injections from every other variable affecting retail right now.
But there are enough overlapping observations from operators to suggest this is more than coincidence.
At the very least, it challenges a comfortable assumption. That bodies change slowly and predictably, and that planning models can rely on that stability.
If that assumption is no longer always true, even temporarily, it is worth asking how resilient current systems really are.
What are you seeing?
This is very much a working hypothesis rather than a settled view.
If you are seeing similar patterns, or if you think we are misattributing cause and effect, leave a comment. The debate is more useful than the conclusion.