On average our fashions customers spend around 23% of their topline net revenue in marketing.
It’s far and away the biggest cost item in your P&L.
So knowing what spending is actually not driving any incremental value is going to have a huge impact on your profitability!
Optimising for bottom line
For fashion brands specifically, the problems start before we even begin.
Every tool out there reports a gross revenue number.
However, in an industry with an average return rate of over 25% (and in some cases closer to 50%) that number is no good!
On top of that decline in revenue, you have additional costs to process the return. And with return rates fluctuating heavily between products, optimising based on a gross revenue number is pretty much flying in the dark.
Thus, fashion brands need to optimise their campaigns based on a Contribution Margin 2 (CM2) that is calculated bottom up (instead of using averages).
CM2 = Gross Revenue – Taxes – Returns – Costs of Goods Sold – Fulfilment Costs – Transaction Costs
By using the CM2, you are not only accounting for differences in product but at the same time can also shift from optimising for revenue (a vanity metric) to optimising for profit.
Holistic optimization with the right attribution
Marketing attribution is a notoriously complicated and often argued about topic.
Too many platforms, telling you completely different things. So what you end up doing is just “a bit of everything” without any clear understanding of what actually works.
And even if you have an external attribution tool in place. Most of these are just using static attribution models - like “first-touch” or “u-shape” - which by default are wrong.
Think about it this way. If a goal is scored during a football game, who should get the value of that goal?
Only the person that scored? No!
The first person that touched the ball? No!
Can you apply the same logic to every goal? No!
Every goal (just like every user journey) is unique and therefore the value that each touchpoint should get depends on the incremental value generated.
The good news: all that data is available.
This is what we are using in our Marketing Mix Attribution Model to tell you how much incremental value each touchpoint generated.
Example:
One fashion brand, using this Marketing Mix Attribution Model, reduced their marketing spend by 23% without any impact on their topline - leading to a 116% increase in their EBITDA. That’s a complete game changer.
Without going into too much detail… Here is how it works:
1. The model looks at when in the journey the touchpoint appeared and how much time the person spend on the site (eg. a 10 minute visits contributes more than a 5 second visit)
2. The model understands which marketing channel generated the touchpoint and how incremental that is (eg. if someone clicks on a branded paid search at there is no/low incremental value)
3. The model taps into zero-party data like discount codes and post purchase surveys to get a more complete picture of all channels that impacted the conversion.
Plus a whole bunch more.
(And while some core principles of the model are built on our dataset of all customers, the model adapts to your customers' user journey.)
Shifting from micro to macro learning
We all know by now, that top quality creatives are the key to success on paid social.
Picture the following: You have a creative that works extremely well, so you try to replicate the approach in different ways only for the new creatives to bomb.
Does this sound familiar?
I bet it does. And you’re not alone with that. Which creatives work can be random!
And thus, I suggest that you shift from only trying to generate micro learning (“Why is this creative working?”) to generating macro learning (“What tends to work best across multiple creatives?”).
The key to make this work are rigid creative naming conventions, combined with something like our creative comparison report, to understand which creatives’ traits are most likely to result in winning creatives.
Since this is a big topic itself, check out this article where we share best practices on naming conventions for eCom brands.
