In the ever-evolving retail landscape, retailers are finding that they are continuing to invest in online advertising, but with far less return. We have found that 40% of ad spend often generates absolutely no revenue for retailers – an astonishing and commercially unacceptable figure.
There are multiple reasons for this that stand outside of Google Shopping; the economic environment, increased competition in the marketing channels, increased COG’s and supply chain difficulties, all of which I have spoken about. But today I would like to focus on what is happening within Google Shopping that we can actively work on, to optimise the performance of a retailer’s inventory.
Visibility
Through working with high street names like Charles Tyrwhitt, conducting data-led audits and analyses of retailers’ performance we’ve found that up to 88% of products advertised on Google Shopping receive minimal visibility. A typical retail audit will show the top 15% of SKUs receive negligible visibility and the ROAS threshold prevents 42% of SKUs from being advertised.
Budget
On average only 55% of available paid marketing investment is spent (and imagine the impact of 40% of that spend being unprofitable)
Reactivity
eCommerce is dynamic, with every product that is advertised undergoing constant changes in their costs, their demand, and other outside influencers. However the way that they are optimised on Google Shopping is never in ‘real-time’ and is much more likely to be incorporated in retrospective reporting on entire campaigns or product lines
These are the core areas that we can work on to improve Google Shopping performance and stop leaving revenue on the table.
The limitations of Google Shopping
In 2022, Google’s ad revenue amounted to 224.47 billion U.S. dollars and one third of this comes from retail – so online retail is very important to Google.
Undoubtedly, Google knows the consumer. But optimising campaigns based on consumer data alone means there is a disconnect with the business objectives of retailers. Google doesn’t understand the commercial viability of each and every product it’s promoting or take into consideration how the inventory performs outside of Google, when deciding which products to promote.
Google assesses which parts of the inventory will achieve the ROAS target the retailer desires and works tirelessly to promote the products that will reach this goal. This leads to the ‘best-sellers’ using the majority of the ad budget, leaving opportunities un-tested and potentially under-served.
Retailers therefore, need to use their own inventory performance data and feed this knowledge into the advertising platform to better control how their entire inventory performs.
From gut instinct to intelligent advertising decision-making
Performance marketing today however, still relies on best practice and manual data analysis to make advertising decisions. Shocking as it sounds, we’ve met customers who invest over £20 million pounds annually into the Google platform, but their CFO struggles to articulate the impact of the spend and is unable to validate the contribution of Google Shopping to the business. The CMO is working in a ‘black box’ reviewing costly retrospectives and PPC teams are making changes as quickly as possible but never at the time the auction demands them.
So how does this change?
Firstly, data needs to be connected. Marketing teams need to be armed with real-time product performance data and understand margins correctly at SKU level. They need to know what it costs to manufacture and bring a product into the business and what it costs to sell a product end-to-end, including return rates, fluctuating sales rates, storage costs and so on.
Without this, advertising decisions cannot be aligned with the business. ROAS targets are, at best, close to reflecting the contribution margins needed and, at worst, correct for only a small percentage of a retailer’s inventory. PPC managers and performance marketers are currently flying blind and always working behind the curve.
And with incorrect targets, sub-optimal campaigns, and an inability to learn from testing the performance of their inventory confident decision making cannot be made – and critically Google is continually learning from bad data.
Retailers are aware of these issues and have a wealth of reporting tools, and they attempt to do it through reporting systems such as Tableau. But to date, it’s been aligning the data and reliably automating advertising decisions to act on demand (or the lack of it) and being able to act on it quickly, which has been really challenging.
By connecting all the inventory performance data and acting on this constantly in Google Shopping, marketing teams will finally have what they need to succeed.
Scale is where the advertising problem becomes exacerbated
If a retailer has twenty products, a human can micromanage every single SKU quite easily with the right product performance data. But most retailers don’t look like that; they have thousands of products that they’re trying to manage at any one time, and it is a complex, constantly moving problem. What they need is technology that not only reports on advertising performance at SKU level, but also acts on that data in real-time, creating an automated workforce that analyses billions of data points and processes thousands of micro-optimisations every single day. This is why Upp. was conceived and is what is game-changing for our clients, who see both a reduction in the total cost of their operations alongside a more effective, profitable and scalable Google Shopping channel.
One of our clients has 36 million products in their inventory, which is impossible for any team to manage effectively without the right technology. The complexity of how each advertising decision affects another can only be analysed and effectually actioned using decisioning software. Online retail is moving all the time – competition is always changing, seasonality is always on the move and global economic factors create a complex trading environment. Static reports age almost as soon as they’re generated – meaning decisions are taken too late. Actioning informed advertising decisions automatically using retail AI and ML, is the only way to scale effectively and maintain control of net margin.
Change is possible
A recent project we worked on saw a client targeting Google with an ROI of 400% on products sold – so for every pound they spent, they wanted four back. When we looked at their intake margins, costs of goods, handling fees and what their true contribution needed to be – they were actually targeting Google to do a 200% over-achievement than the business actually required. That meant Google simply wasn’t advertising the majority of the products, as the ROI target was too high.
When we actually grouped their products correctly (based on understanding each SKU’s performance potential) and applied the correct ROAS targets, we increased the retailer’s exposure on Google to advertise 85% of their products. So not only did they achieve the business’ ROI requirements, but they also drove 30% more of their inventory effectively through Google. And their account is continuing to scale, with more products being sold profitably – revenue is up, and so too is net margin, thanks to less unsold stock.
Similarly, when we first worked with Charles Tyrwhitt, they were profitable on Google Shopping, but only advertising a shocking 7% of their inventory successfully. Why? Because Google works on a reinforcement learning program – it will quickly test and validate which products in a group are most likely to be successful and then keep promoting them, leaving the others behind. PPC teams don’t have a lot of other data to play with, so they were stuck in a status quo scenario, unable to progress and scale.
What we did with Charles Tyrwhitt was analyse how their inventory was performing at scale through all retail channels. We then restructured their Google Shopping campaigns to align with the performance of their overall inventory (currently impossible without Upp.) and our decision intelligence began automating advertising decisions based on their connected real-time data
Continued ROI in their Google Shopping has allowed the brand to increase their spend by 1000%. As a result, they’ve seen growth in the US of 64% over a 12-month period, in the UK a growth rate of 43%, and they’ve also seen their cost per acquisition of new customers go from £103 per customer to £48 per customer. They were able to do this because we’re able to educate Google about how the inventory should perform, what products to push and what level to push them at.
Charles Tyrwhitt now confidently spends close to £1 million a month in Google advertising, vs £90k a month when we met them.
It’s time to stop wasting money and get smarter
It’s clear that the status quo in online advertising is broken, and the industry needs to do better. Think about it – we live in an age where data and technology have transformed the way we do business. We have access to more data than ever before, and we have tools that can help us analyse that data in real-time. Yet despite all this, we still have a situation where almost half of the money we spend on advertising is essentially wasted.
We need to be more data-driven, more targeted, and more strategic in our approach. We need to use the right tools at our disposal to understand our customers better, identify the most effective channels for reaching them, and create advertising campaigns that truly resonate with them.
It’s more important than ever to make informed decisions that drive business success. There is increasing competition in the retail industry, and the cost of living crisis is a stark contrast to the Covid boom. When cash is tight, it’s crucial to understand where your ad spend is going, what products are selling and why, and how to make the most of your inventory.
No retailer can afford to waste 40% of ad spend on Google Shopping – it’s time for change…
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You are in: Home » Guest Comment » GUEST COMMENT How 40% of ad spend on Google Shopping generates zero revenue – and what to do about it
GUEST COMMENT How 40% of ad spend on Google Shopping generates zero revenue – and what to do about it
by Paul Skeldon
In the ever-evolving retail landscape, retailers are finding that they are continuing to invest in online advertising, but with far less return. We have found that 40% of ad spend often generates absolutely no revenue for retailers – an astonishing and commercially unacceptable figure.
There are multiple reasons for this that stand outside of Google Shopping; the economic environment, increased competition in the marketing channels, increased COG’s and supply chain difficulties, all of which I have spoken about. But today I would like to focus on what is happening within Google Shopping that we can actively work on, to optimise the performance of a retailer’s inventory.
Through working with high street names like Charles Tyrwhitt, conducting data-led audits and analyses of retailers’ performance we’ve found that up to 88% of products advertised on Google Shopping receive minimal visibility. A typical retail audit will show the top 15% of SKUs receive negligible visibility and the ROAS threshold prevents 42% of SKUs from being advertised.
On average only 55% of available paid marketing investment is spent (and imagine the impact of 40% of that spend being unprofitable)
eCommerce is dynamic, with every product that is advertised undergoing constant changes in their costs, their demand, and other outside influencers. However the way that they are optimised on Google Shopping is never in ‘real-time’ and is much more likely to be incorporated in retrospective reporting on entire campaigns or product lines
These are the core areas that we can work on to improve Google Shopping performance and stop leaving revenue on the table.
The limitations of Google Shopping
In 2022, Google’s ad revenue amounted to 224.47 billion U.S. dollars and one third of this comes from retail – so online retail is very important to Google.
Undoubtedly, Google knows the consumer. But optimising campaigns based on consumer data alone means there is a disconnect with the business objectives of retailers. Google doesn’t understand the commercial viability of each and every product it’s promoting or take into consideration how the inventory performs outside of Google, when deciding which products to promote.
Google assesses which parts of the inventory will achieve the ROAS target the retailer desires and works tirelessly to promote the products that will reach this goal. This leads to the ‘best-sellers’ using the majority of the ad budget, leaving opportunities un-tested and potentially under-served.
Retailers therefore, need to use their own inventory performance data and feed this knowledge into the advertising platform to better control how their entire inventory performs.
From gut instinct to intelligent advertising decision-making
Performance marketing today however, still relies on best practice and manual data analysis to make advertising decisions. Shocking as it sounds, we’ve met customers who invest over £20 million pounds annually into the Google platform, but their CFO struggles to articulate the impact of the spend and is unable to validate the contribution of Google Shopping to the business. The CMO is working in a ‘black box’ reviewing costly retrospectives and PPC teams are making changes as quickly as possible but never at the time the auction demands them.
So how does this change?
Firstly, data needs to be connected. Marketing teams need to be armed with real-time product performance data and understand margins correctly at SKU level. They need to know what it costs to manufacture and bring a product into the business and what it costs to sell a product end-to-end, including return rates, fluctuating sales rates, storage costs and so on.
Without this, advertising decisions cannot be aligned with the business. ROAS targets are, at best, close to reflecting the contribution margins needed and, at worst, correct for only a small percentage of a retailer’s inventory. PPC managers and performance marketers are currently flying blind and always working behind the curve.
And with incorrect targets, sub-optimal campaigns, and an inability to learn from testing the performance of their inventory confident decision making cannot be made – and critically Google is continually learning from bad data.
Retailers are aware of these issues and have a wealth of reporting tools, and they attempt to do it through reporting systems such as Tableau. But to date, it’s been aligning the data and reliably automating advertising decisions to act on demand (or the lack of it) and being able to act on it quickly, which has been really challenging.
By connecting all the inventory performance data and acting on this constantly in Google Shopping, marketing teams will finally have what they need to succeed.
Scale is where the advertising problem becomes exacerbated
If a retailer has twenty products, a human can micromanage every single SKU quite easily with the right product performance data. But most retailers don’t look like that; they have thousands of products that they’re trying to manage at any one time, and it is a complex, constantly moving problem. What they need is technology that not only reports on advertising performance at SKU level, but also acts on that data in real-time, creating an automated workforce that analyses billions of data points and processes thousands of micro-optimisations every single day. This is why Upp. was conceived and is what is game-changing for our clients, who see both a reduction in the total cost of their operations alongside a more effective, profitable and scalable Google Shopping channel.
One of our clients has 36 million products in their inventory, which is impossible for any team to manage effectively without the right technology. The complexity of how each advertising decision affects another can only be analysed and effectually actioned using decisioning software. Online retail is moving all the time – competition is always changing, seasonality is always on the move and global economic factors create a complex trading environment. Static reports age almost as soon as they’re generated – meaning decisions are taken too late. Actioning informed advertising decisions automatically using retail AI and ML, is the only way to scale effectively and maintain control of net margin.
Change is possible
A recent project we worked on saw a client targeting Google with an ROI of 400% on products sold – so for every pound they spent, they wanted four back. When we looked at their intake margins, costs of goods, handling fees and what their true contribution needed to be – they were actually targeting Google to do a 200% over-achievement than the business actually required. That meant Google simply wasn’t advertising the majority of the products, as the ROI target was too high.
When we actually grouped their products correctly (based on understanding each SKU’s performance potential) and applied the correct ROAS targets, we increased the retailer’s exposure on Google to advertise 85% of their products. So not only did they achieve the business’ ROI requirements, but they also drove 30% more of their inventory effectively through Google. And their account is continuing to scale, with more products being sold profitably – revenue is up, and so too is net margin, thanks to less unsold stock.
Similarly, when we first worked with Charles Tyrwhitt, they were profitable on Google Shopping, but only advertising a shocking 7% of their inventory successfully. Why? Because Google works on a reinforcement learning program – it will quickly test and validate which products in a group are most likely to be successful and then keep promoting them, leaving the others behind. PPC teams don’t have a lot of other data to play with, so they were stuck in a status quo scenario, unable to progress and scale.
What we did with Charles Tyrwhitt was analyse how their inventory was performing at scale through all retail channels. We then restructured their Google Shopping campaigns to align with the performance of their overall inventory (currently impossible without Upp.) and our decision intelligence began automating advertising decisions based on their connected real-time data
Continued ROI in their Google Shopping has allowed the brand to increase their spend by 1000%. As a result, they’ve seen growth in the US of 64% over a 12-month period, in the UK a growth rate of 43%, and they’ve also seen their cost per acquisition of new customers go from £103 per customer to £48 per customer. They were able to do this because we’re able to educate Google about how the inventory should perform, what products to push and what level to push them at.
Charles Tyrwhitt now confidently spends close to £1 million a month in Google advertising, vs £90k a month when we met them.
It’s time to stop wasting money and get smarter
It’s clear that the status quo in online advertising is broken, and the industry needs to do better. Think about it – we live in an age where data and technology have transformed the way we do business. We have access to more data than ever before, and we have tools that can help us analyse that data in real-time. Yet despite all this, we still have a situation where almost half of the money we spend on advertising is essentially wasted.
We need to be more data-driven, more targeted, and more strategic in our approach. We need to use the right tools at our disposal to understand our customers better, identify the most effective channels for reaching them, and create advertising campaigns that truly resonate with them.
It’s more important than ever to make informed decisions that drive business success. There is increasing competition in the retail industry, and the cost of living crisis is a stark contrast to the Covid boom. When cash is tight, it’s crucial to understand where your ad spend is going, what products are selling and why, and how to make the most of your inventory.
No retailer can afford to waste 40% of ad spend on Google Shopping – it’s time for change…
Author
Drew Smith is CEO and Co-founder of Upp.
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