ROAS should work for online retail, but it isn’t – yet
Anyone who’s spent time in the world of retail will have a basic understanding of the ROAS marketing metric. Short for ‘return on ad spend’, it’s a formula designed to measure the effectiveness and impact of a retailer’s marketing efforts – by comparing how much money is spent on those efforts with how much comes back in as a result of them.
Theoretically, ROAS should be easy to work with. In the simplest possible terms, if you spend £5 marketing something, and then sell it for £50, your ROAS would be 1,000% (sometimes expressed as 10:1). Essentially, that ROAS figure is saying you’ve increased the value of your ad spend tenfold. Not every business can boast a ROAS that high, but in the most general terms, anything over 300% (3:1) is considered a positive.
It’s an important metric for retailers because ROAS doesn’t get bogged down in how much different products might be sold for, and only focuses on the cold, hard numbers. The problem though, is that the simplicity of ROAS disregards quite a few other key pieces of information, which paint a much more detailed and valuable picture of a business’s marketing success.
The problem with return on ad spend (ROAS)
While ROAS certainly has its place, the big issue with it as a KPI lies in the simple fact that retailers’ operations are vastly more complicated than simply having a product and needing to invest in marketing it.
There’s the production of those products to take into account too, on top of any raw materials needed to manufacture them. Then there’s the different costs at every point of the lifecycle – from actual production all the way through to the delivery person who takes each unit to its final destination. And then the considerations of processing fees, taxes, and discounts along the way.
With all of that in mind, it’s much more accurate to describe most forms of ROAS as ‘revenue’ on ad spend, rather than ‘return’. And that’s a problem, because while you might have a strong ROAS number for a particular campaign and product, if unseen costs are chipping away at the overall return, then you could be throwing your money away on inefficient at best or unprofitable at worst, marketing.
Additionally, ROAS does not have a number lower than zero – so it cannot provide retailers with the clarity they need to stop products from being sold unprofitably.
Ultimately, while there are lots of different metrics out there that provide a health check for retailers, profit is the number that matters most to the bottom line. If you are profitable, then everything else should fall into place. If it’s not, then it’s time to rethink everything – from the ground up.
Using a PROAS formula to put profit first
With all the different issues and unreliabilities inherent to traditional ROAS, an alternative has emerged in the form of the PROAS formula. It’s short for ‘profitable return on ad spend’, and you might also see it referred to as POAS (short for ‘profit on ad spend’), which means the same thing.
Essentially, PROAS is what ROAS should be, a number that focuses on the true, profitable return of your ad spend, taking into account all the costs along the way – long before and long after a single ad is published. It’s certainly a more complex formula, incorporating countless layers of additional data, but the processing power of modern machine learning and AI makes even the most technical of these formulas achievable at the press of a few buttons.
It’s also incredibly valuable. With PROAS figures close at hand, retailers can see the true impacts of their marketing efforts, and prioritise or deprioritise the campaigns that are having the best effects on their overall bottom lines.
Going beyond the basics with iROAS™
The one big problem with PROAS is that if you are defining your goal to be profitable it works – but there are times when a retailer’s goal is generating revenue (mitigating overstock, at the end of a product life cycle, etc.) so profitability is not necessarily the desired outcome of your advertising spend.
In an ideal world, there wouldn’t be a need for PROAS, because ROAS would already take all the data that matters into account – tracking revenue as well as, not instead of, return. And responsive marketing decisions would be automatically taken on advertising campaigns, based on an understanding of all these factors, as well as the current auction dynamics.
This is an ‘intelligent return on ad spend’. Leveraging leaps forward in technology to turn ROAS into the metric needed today. An intelligent ROAS (iROAS™) is aligned with a retailer’s goal for each product in their inventory and can dynamically change as the goals need to change. Upp.’s AI and ML is powerful enough to support iROAS™, we provide a game-changing new approach using this to automate marketing decisions for Performance Max campaigns.
The result? An improved, profitable Google Shopping channel, automatically scaled for growth that is far more efficient and effective than was previously possible.