Google Shopping ROI can vary hugely based on your products, search volumes, data quality, and of course, consumer demand.
Google Shopping measures ROI by ROAS, which is short for ‘return on advertising spend’. This is simply the total sales revenue resulting from ad clicks divided by the amount spent on Google Shopping ads.
But even though this is an industry standard, it doesn’t always give the most complete picture. For that, you’ll want a more comprehensive and intelligent metric that works on an SKU-level.
Regardless of which metric you use, success depends on the wide range of factors we’ve touched on already, as well as the margin of each product. A successful Google Shopping campaign could have a ROAS of 2 or 10 – it all depends on the factors listed above, as well as the margin on the product.
But there are ways for retailers to give themselves a better chance of success. So in this article, we’re taking a closer look at the mechanics of Google Shopping and how to maximise your ROI within it.
Knowing what, where, when, and how to advertise
Being able to see the future would be a great first step. But until we figure that out, it’s better to look for the best information and insights available, and incorporate them into your campaigns.
Starting with your best-selling products is often a safe bet – but they might not always hold the highest-value. Instead, look for high-margin, low-velocity products and experiment with boosting their advertising spend to get a higher return.
Typically, retailers and brands will dedicate a low level of spend to a large range of products, and then optimise the ones that deliver the best results.
This approach is an okay starting point – and can usually provide a decent ROAS – but it does fail to account for seasonality or differences in the quality of data across your product range. As a result, this method doesn’t reliably deliver maximised returns.
That’s why access to consistent, comprehensive product data from your own business and the wider market is vital to optimising Google Shopping campaigns. This is true across the board in retail, but particularly so in Google’s data-driven world.
If you can slice up that information by product type, margin, sales velocity, and the like, then you’re halfway to seeing the future as far as Google Shopping is concerned.
Quality data gives you quality results
Product data is quite an abstract term, so let’s get specific. Google, like any search engine (including Amazon, for example), always aims to deliver relevant results to search queries.
This is true for paid ad placements as well as organic results. And it makes more relevant products more likely to be displayed as ads in response to a specific search. This in turn means that the bids required to display those ads are lower than for a less relevant product.
Since the launch of Performance Max, the challenge is giving Google the information it needs to fully understand all the potential searches your product is relevant for.
To do this, you need key attributes like colour, material, size, dimensions, and weight to be included in the data feed that you use as part of your Google Shopping Performance Max optimisation.
This is something that too many brands don’t do. As a result, Google Shopping pulls whatever information it can find – sometimes directly from their websites.
That leads to less relevancy, less exposure, and higher bids – all hurting key ecommerce metrics like cost per acquisition and, ultimately, ROAS.
The bottom line here is that with poor-quality data, the return on your Google Shopping campaigns will always be limited.
It’s not just Google that reads your listings though – it’s also your customers.
You’ll need high-quality titles (of up to 70 characters before Google cuts them off) and descriptions with relevant keywords (up to 5,000 characters). Product images are also vital. The better they are, the more likely a customer is to make a purchase.
Optimising Google Shopping/Performance Max campaigns sounds like a lot of work – and an impossible task for retailers with thousands of products. And it is, without the right tools.
That’s why retailers are increasingly using Google Shopping management software equipped with AI and machine learning capabilities that allow them to maintain a ‘single source of truth’ data set.
With a platform like Upp.AI, this can then be automatically optimised for Google Shopping to deliver the same results with none of the fuss.
Step back and see the bigger picture
The theme running through all of this is that without a clear idea of how your Shopping campaign ties into the rest of the business, it’s hard to realistically claim a strong return on investment. eCommerce teams need their actions to be fully informed by relevant data.
How many Google Shopping campaigns are optimised to avoid promoting products with low margins and high return rates? To avoid such errors and earn a strong ROI, it is crucial to learn how to optimise Google Shopping campaigns.
Chasing impressive-seeming returns on ad spend is tempting, but being able to prove value to the wider business through increasing the profitability of online sales is probably better in the long run.
The key is to constantly focus on bottom-line affecting metrics like return rates, profit margin and to focus your efforts where they will make the most difference.