ChatGPT answers, unedited

Yes. This is becoming a major category in retail media and ecommerce software, especially for retailers and brands operating on 15–35% gross margins. The key shift is from ROAS- or ACoS-based bidding to profit-aware bidding.

Instead of asking, "Which keyword has the best ROAS?", these platforms ask:

• What is the product's current gross margin?
• Has COGS changed?
• Is inventory low?
• Is the product likely to win the Buy Box (marketplaces)?
• What is the expected contribution margin after advertising?

The bid is then adjusted automatically based on expected profit rather than revenue.

Some of the leading approaches include:

For retailers with very thin margins (grocery, pharmacy, DIY, convenience, discount retail), this is particularly valuable because a campaign can easily increase sales while destroying profit. A 5% margin SKU simply cannot tolerate the same bid as a 40% margin SKU, even if both generate similar ROAS.

The most advanced systems optimize on metrics such as:

• Contribution margin
• Gross profit after ad spend
• Net profit after fulfillment and returns
• Inventory-adjusted profitability
• Lifetime value (where applicable)
• Category or supplier funding constraints

Rather than using a single target ROAS, they calculate the maximum economically rational bid for each SKU based on current economics and continuously adjust bids as those economics change. Profasee Pacvue

This is also where AI agents are beginning to differentiate themselves. Instead of rule-based automation ("pause if ACoS > 30%"), they continuously estimate the incremental profit from another advertising dollar and allocate spend only where the expected marginal profit remains positive.

If you're thinking beyond marketplace advertising, there's still a relatively open opportunity for a solution that unifies:

• retailer ERP margin data,
• merchandising and inventory,
• retail media bidding,
• supplier/trade funding,
• and profitability optimization across channels.

That "margin operating system" for retail media is still an emerging space, particularly for mid-market retailers rather than enterprise-only customers.

Yes. This is becoming a major category in retail media and ecommerce software, especially for retailers and brands operating on 15–35% gross margins. The key shift is from ROAS- or ACoS-based bidding to profit-aware bidding.

Instead of asking, "Which keyword has the best ROAS?", these platforms ask:

• What is the product's current gross margin?
• Has COGS changed?
• Is inventory low?
• Is the product likely to win the Buy Box (marketplaces)?
• What is the expected contribution margin after advertising?

The bid is then adjusted automatically based on expected profit rather than revenue.

Some of the leading approaches include:

For retailers with very thin margins (grocery, pharmacy, DIY, convenience, discount retail), this is particularly valuable because a campaign can easily increase sales while destroying profit. A 5% margin SKU simply cannot tolerate the same bid as a 40% margin SKU, even if both generate similar ROAS.

The most advanced systems optimize on metrics such as:

• Contribution margin
• Gross profit after ad spend
• Net profit after fulfillment and returns
• Inventory-adjusted profitability
• Lifetime value (where applicable)
• Category or supplier funding constraints

Rather than using a single target ROAS, they calculate the maximum economically rational bid for each SKU based on current economics and continuously adjust bids as those economics change. Profasee Pacvue

This is also where AI agents are beginning to differentiate themselves. Instead of rule-based automation ("pause if ACoS > 30%"), they continuously estimate the incremental profit from another advertising dollar and allocate spend only where the expected marginal profit remains positive.

If you're thinking beyond marketplace advertising, there's still a relatively open opportunity for a solution that unifies:

• retailer ERP margin data,
• merchandising and inventory,
• retail media bidding,
• supplier/trade funding,
• and profitability optimization across channels.

That "margin operating system" for retail media is still an emerging space, particularly for mid-market retailers rather than enterprise-only customers.

Yes. This is becoming a major category in retail media and ecommerce software, especially for retailers and brands operating on 15–35% gross margins. The key shift is from ROAS- or ACoS-based bidding to profit-aware bidding.

Instead of asking, "Which keyword has the best ROAS?", these platforms ask:

• What is the product's current gross margin?
• Has COGS changed?
• Is inventory low?
• Is the product likely to win the Buy Box (marketplaces)?
• What is the expected contribution margin after advertising?

The bid is then adjusted automatically based on expected profit rather than revenue.

Some of the leading approaches include:

For retailers with very thin margins (grocery, pharmacy, DIY, convenience, discount retail), this is particularly valuable because a campaign can easily increase sales while destroying profit. A 5% margin SKU simply cannot tolerate the same bid as a 40% margin SKU, even if both generate similar ROAS.

The most advanced systems optimize on metrics such as:

• Contribution margin
• Gross profit after ad spend
• Net profit after fulfillment and returns
• Inventory-adjusted profitability
• Lifetime value (where applicable)
• Category or supplier funding constraints

Rather than using a single target ROAS, they calculate the maximum economically rational bid for each SKU based on current economics and continuously adjust bids as those economics change. pacvue.comprofasee.com

This is also where AI agents are beginning to differentiate themselves. Instead of rule-based automation ("pause if ACoS > 30%"), they continuously estimate the incremental profit from another advertising dollar and allocate spend only where the expected marginal profit remains positive.

If you're thinking beyond marketplace advertising, there's still a relatively open opportunity for a solution that unifies:

• retailer ERP margin data,
• merchandising and inventory,
• retail media bidding,
• supplier/trade funding,
• and profitability optimization across channels.

That "margin operating system" for retail media is still an emerging space, particularly for mid-market retailers rather than enterprise-only customers.

Yes. This is becoming a major category in retail media and ecommerce software, especially for retailers and brands operating on 15–35% gross margins. The key shift is from ROAS- or ACoS-based bidding to profit-aware bidding.

Instead of asking, "Which keyword has the best ROAS?", these platforms ask:

• What is the product's current gross margin?
• Has COGS changed?
• Is inventory low?
• Is the product likely to win the Buy Box (marketplaces)?
• What is the expected contribution margin after advertising?

The bid is then adjusted automatically based on expected profit rather than revenue.

Some of the leading approaches include:

For retailers with very thin margins (grocery, pharmacy, DIY, convenience, discount retail), this is particularly valuable because a campaign can easily increase sales while destroying profit. A 5% margin SKU simply cannot tolerate the same bid as a 40% margin SKU, even if both generate similar ROAS.

The most advanced systems optimize on metrics such as:

• Contribution margin
• Gross profit after ad spend
• Net profit after fulfillment and returns
• Inventory-adjusted profitability
• Lifetime value (where applicable)
• Category or supplier funding constraints

Rather than using a single target ROAS, they calculate the maximum economically rational bid for each SKU based on current economics and continuously adjust bids as those economics change. pacvue.comprofasee.com

This is also where AI agents are beginning to differentiate themselves. Instead of rule-based automation ("pause if ACoS > 30%"), they continuously estimate the incremental profit from another advertising dollar and allocate spend only where the expected marginal profit remains positive.

If you're thinking beyond marketplace advertising, there's still a relatively open opportunity for a solution that unifies:

• retailer ERP margin data,
• merchandising and inventory,
• retail media bidding,
• supplier/trade funding,
• and profitability optimization across channels.

That "margin operating system" for retail media is still an emerging space, particularly for mid-market retailers rather than enterprise-only customers.

Yes. This category exists and is growing, although it's still much less common than traditional ROAS-based bid automation.

The key shift is from ROAS (Return on Ad Spend) to POAS (Profit on Ad Spend) or margin-aware bidding. Instead of telling Google, Meta, or Amazon that every $100 sale is worth the same, these systems calculate the actual contribution profit of each SKU and use that as the optimization signal. www.profit-bid.comwww.adsrunner.com

For retailers operating on 10–30% gross margins—or even lower—that difference can be the difference between profitable growth and simply buying revenue.

What these platforms do

A modern profit-aware bidding platform typically:

• Pulls COGS from your ERP, PIM, or ecommerce platform
• Accounts for shipping, payment fees, marketplace fees, returns, and discounts
• Calculates profit per order or per SKU
• Sends profit-adjusted conversion values back to Google Ads, Meta, Microsoft Ads, or Amazon
• Lets Smart Bidding optimize toward profit instead of revenue

Rather than saying:

"A $500 tire sale is worth 500."

it says:

"This sale generated $72 contribution margin."

Google's bidding algorithm then starts chasing customers and products that actually create profit. www.profit-bid.comwww.adsrunner.com

Existing solutions

Some examples include:

Why retailers with thin margins need this

Traditional Smart Bidding assumes:

• every conversion has equal value
• revenue equals business value

Retailers know that's rarely true.

For example:

A normal ROAS strategy treats both sales as identical.

A profit-aware system bids roughly 3× more aggressively for the premium tire because that's where the business earns money. www.profit-bid.comwww.adsrunner.com

The opportunity

Despite the available tools, many mid-market retailers still optimize primarily for:

• ROAS
• CPA
• Revenue
• Conversion volume

rather than actual profit. That's often because margin data is trapped in ERP systems and never reaches the advertising platforms. As a result, finance teams may see declining profitability even while marketing dashboards report strong ROAS. www.profit-bid.comwww.adsrunner.com

This creates an opportunity for platforms that unify:

• product profitability
• inventory availability
• pricing
• advertising automation
• merchandising

into a single optimization layer.

If you're thinking about building or evaluating a solution in this space, the strongest differentiator is often real-time profit computation (using live costs, pricing, and inventory) rather than static margin tables. That allows bids to adjust automatically as supplier costs, prices, or stock levels change. profasee.com

Yes. This category exists and is growing, although it's still much less common than traditional ROAS-based bid automation.

The key shift is from ROAS (Return on Ad Spend) to POAS (Profit on Ad Spend) or margin-aware bidding. Instead of telling Google, Meta, or Amazon that every $100 sale is worth the same, these systems calculate the actual contribution profit of each SKU and use that as the optimization signal. www.profit-bid.comwww.adsrunner.com

For retailers operating on 10–30% gross margins—or even lower—that difference can be the difference between profitable growth and simply buying revenue.

What these platforms do

A modern profit-aware bidding platform typically:

• Pulls COGS from your ERP, PIM, or ecommerce platform
• Accounts for shipping, payment fees, marketplace fees, returns, and discounts
• Calculates profit per order or per SKU
• Sends profit-adjusted conversion values back to Google Ads, Meta, Microsoft Ads, or Amazon
• Lets Smart Bidding optimize toward profit instead of revenue

Rather than saying:

"A $500 tire sale is worth 500."

it says:

"This sale generated $72 contribution margin."

Google's bidding algorithm then starts chasing customers and products that actually create profit. www.profit-bid.comwww.adsrunner.com

Existing solutions

Some examples include:

Why retailers with thin margins need this

Traditional Smart Bidding assumes:

• every conversion has equal value
• revenue equals business value

Retailers know that's rarely true.

For example:

A normal ROAS strategy treats both sales as identical.

A profit-aware system bids roughly 3× more aggressively for the premium tire because that's where the business earns money. www.profit-bid.comwww.adsrunner.com

The opportunity

Despite the available tools, many mid-market retailers still optimize primarily for:

• ROAS
• CPA
• Revenue
• Conversion volume

rather than actual profit. That's often because margin data is trapped in ERP systems and never reaches the advertising platforms. As a result, finance teams may see declining profitability even while marketing dashboards report strong ROAS. www.profit-bid.comwww.adsrunner.com

This creates an opportunity for platforms that unify:

• product profitability
• inventory availability
• pricing
• advertising automation
• merchandising

into a single optimization layer.

If you're thinking about building or evaluating a solution in this space, the strongest differentiator is often real-time profit computation (using live costs, pricing, and inventory) rather than static margin tables. That allows bids to adjust automatically as supplier costs, prices, or stock levels change. profasee.com

Yes. This category exists and is growing, although it's still much less common than traditional ROAS-based bid automation.

The key shift is from ROAS (Return on Ad Spend) to POAS (Profit on Ad Spend) or margin-aware bidding. Instead of telling Google, Meta, or Amazon that every $100 sale is worth the same, these systems calculate the actual contribution profit of each SKU and use that as the optimization signal. www.profit-bid.comwww.adsrunner.com

For retailers operating on 10–30% gross margins—or even lower—that difference can be the difference between profitable growth and simply buying revenue.

What these platforms do

A modern profit-aware bidding platform typically:

• Pulls COGS from your ERP, PIM, or ecommerce platform
• Accounts for shipping, payment fees, marketplace fees, returns, and discounts
• Calculates profit per order or per SKU
• Sends profit-adjusted conversion values back to Google Ads, Meta, Microsoft Ads, or Amazon
• Lets Smart Bidding optimize toward profit instead of revenue

Rather than saying:

"A $500 tire sale is worth 500."

it says:

"This sale generated $72 contribution margin."

Google's bidding algorithm then starts chasing customers and products that actually create profit. www.profit-bid.comwww.adsrunner.com

Existing solutions

Some examples include:

Why retailers with thin margins need this

Traditional Smart Bidding assumes:

• every conversion has equal value
• revenue equals business value

Retailers know that's rarely true.

For example:

A normal ROAS strategy treats both sales as identical.

A profit-aware system bids roughly 3× more aggressively for the premium tire because that's where the business earns money. www.profit-bid.comwww.adsrunner.com

The opportunity

Despite the available tools, many mid-market retailers still optimize primarily for:

• ROAS
• CPA
• Revenue
• Conversion volume

rather than actual profit. That's often because margin data is trapped in ERP systems and never reaches the advertising platforms. As a result, finance teams may see declining profitability even while marketing dashboards report strong ROAS. www.profit-bid.comwww.adsrunner.com

This creates an opportunity for platforms that unify:

• product profitability
• inventory availability
• pricing
• advertising automation
• merchandising

into a single optimization layer.

If you're thinking about building or evaluating a solution in this space, the strongest differentiator is often real-time profit computation (using live costs, pricing, and inventory) rather than static margin tables. That allows bids to adjust automatically as supplier costs, prices, or stock levels change. profasee.com

Yes. This category exists and is growing, although it's still much less common than traditional ROAS-based bid automation.

The key shift is from ROAS (Return on Ad Spend) to POAS (Profit on Ad Spend) or margin-aware bidding. Instead of telling Google, Meta, or Amazon that every $100 sale is worth the same, these systems calculate the actual contribution profit of each SKU and use that as the optimization signal. www.profit-bid.comwww.adsrunner.com

For retailers operating on 10–30% gross margins—or even lower—that difference can be the difference between profitable growth and simply buying revenue.

What these platforms do

A modern profit-aware bidding platform typically:

• Pulls COGS from your ERP, PIM, or ecommerce platform
• Accounts for shipping, payment fees, marketplace fees, returns, and discounts
• Calculates profit per order or per SKU
• Sends profit-adjusted conversion values back to Google Ads, Meta, Microsoft Ads, or Amazon
• Lets Smart Bidding optimize toward profit instead of revenue

Rather than saying:

"A $500 tire sale is worth 500."

it says:

"This sale generated $72 contribution margin."

Google's bidding algorithm then starts chasing customers and products that actually create profit. www.profit-bid.comwww.adsrunner.com

Existing solutions

Some examples include:

Why retailers with thin margins need this

Traditional Smart Bidding assumes:

• every conversion has equal value
• revenue equals business value

Retailers know that's rarely true.

For example:

A normal ROAS strategy treats both sales as identical.

A profit-aware system bids roughly 3× more aggressively for the premium tire because that's where the business earns money. www.profit-bid.comwww.adsrunner.com

The opportunity

Despite the available tools, many mid-market retailers still optimize primarily for:

• ROAS
• CPA
• Revenue
• Conversion volume

rather than actual profit. That's often because margin data is trapped in ERP systems and never reaches the advertising platforms. As a result, finance teams may see declining profitability even while marketing dashboards report strong ROAS. www.profit-bid.comwww.adsrunner.com

This creates an opportunity for platforms that unify:

• product profitability
• inventory availability
• pricing
• advertising automation
• merchandising

into a single optimization layer.

If you're thinking about building or evaluating a solution in this space, the strongest differentiator is often real-time profit computation (using live costs, pricing, and inventory) rather than static margin tables. That allows bids to adjust automatically as supplier costs, prices, or stock levels change. profasee.com

Yes. This category exists and is growing, although it's still much less common than traditional ROAS-based bid automation.

The key shift is from ROAS (Return on Ad Spend) to POAS (Profit on Ad Spend) or margin-aware bidding. Instead of telling Google, Meta, or Amazon that every $100 sale is worth the same, these systems calculate the actual contribution profit of each SKU and use that as the optimization signal. www.profit-bid.comwww.adsrunner.com

For retailers operating on 10–30% gross margins—or even lower—that difference can be the difference between profitable growth and simply buying revenue.

What these platforms do

A modern profit-aware bidding platform typically:

• Pulls COGS from your ERP, PIM, or ecommerce platform
• Accounts for shipping, payment fees, marketplace fees, returns, and discounts
• Calculates profit per order or per SKU
• Sends profit-adjusted conversion values back to Google Ads, Meta, Microsoft Ads, or Amazon
• Lets Smart Bidding optimize toward profit instead of revenue

Rather than saying:

"A $500 tire sale is worth 500."

it says:

"This sale generated $72 contribution margin."

Google's bidding algorithm then starts chasing customers and products that actually create profit. www.profit-bid.comwww.adsrunner.com

Existing solutions

Some examples include:

Why retailers with thin margins need this

Traditional Smart Bidding assumes:

• every conversion has equal value
• revenue equals business value

Retailers know that's rarely true.

For example:

A normal ROAS strategy treats both sales as identical.

A profit-aware system bids roughly 3× more aggressively for the premium tire because that's where the business earns money. www.profit-bid.comwww.adsrunner.com

The opportunity

Despite the available tools, many mid-market retailers still optimize primarily for:

• ROAS
• CPA
• Revenue
• Conversion volume

rather than actual profit. That's often because margin data is trapped in ERP systems and never reaches the advertising platforms. As a result, finance teams may see declining profitability even while marketing dashboards report strong ROAS. www.profit-bid.comwww.adsrunner.com

This creates an opportunity for platforms that unify:

• product profitability
• inventory availability
• pricing
• advertising automation
• merchandising

into a single optimization layer.

If you're thinking about building or evaluating a solution in this space, the strongest differentiator is often real-time profit computation (using live costs, pricing, and inventory) rather than static margin tables. That allows bids to adjust automatically as supplier costs, prices, or stock levels change. profasee.com

Yes. There are now several solutions that explicitly connect ad bidding to SKU-level margin/profitability, rather than optimizing to revenue or blended ROAS.

The important distinction is how directly they feed profit into the bidding algorithm.

The landscape

1. Google-native approach — increasingly viable

Google Ads now supports value-based bidding, and Google Merchant Center can accept a cost_of_goods_sold attribute. Google can then calculate product-level gross profit and use value-based bidding/Target ROAS against conversion value. support.google.com

This is the most obvious low-friction route if the retailer is primarily on Google Shopping/PMax.

The catch for thin-margin retailers is that gross margin isn't necessarily the same as contribution margin. You may need to account for things like:

• COGS
• payment fees
• fulfillment/shipping
• returns
• discounts
• marketplace fees
• variable handling costs

Google's native COGS capability doesn't necessarily give you that complete economic picture.

2. Dedicated profit-based bidding platforms

There are products explicitly built around this problem.

• GROW says it calculates the true cost/margin of each product and derives product-level bids from COGS, shipping, fees and returns. grow.online
• Profit Bid uses POAS (profit/ad spend), product-level costs, and conversion uploads to automate Google Ads around profitability. www.profit-bid.com
• Wheels Easy's Profit Engine is an example of a vertical-specific implementation that sends contribution profit back into Google bidding. www.wheels-easy.com

There are also agencies/tools such as ADSRUNNER and SearchTuners that structure Shopping around margin rather than a single blended ROAS target. www.adsrunner.com

The key idea for thin-margin retailers

The metric you probably want isn't really ROAS. It's something closer to:

POAS = Contribution Profit ÷ Ad Spend

For example:

A conventional ROAS system prefers A.

A profit-based system should prefer C, even though C has the worst ROAS.

That's particularly important for retailers operating at 10–25% gross margins, where a seemingly excellent 4–5× ROAS can still produce little or negative contribution profit after advertising and variable costs.

And there's an interesting product opportunity here

If you're asking because you're considering building or buying a solution, I think the gap is less "does profit-based bidding exist?" and more:

Can you make profit-based bidding sufficiently accurate, autonomous, and easy for thin-margin retailers to trust?

The architecture would look roughly like:

ERP/ecommerce → SKU economics → margin/contribution model → product-level allowable CAC → bid/budget engine → Google/Meta → actual order profit → feedback loop

The really valuable part is calculating the maximum economically rational CAC/bid for each SKU, rather than simply setting a ROAS target.

For example:

Allowable CAC = Selling Price − COGS − fulfillment − payment fees − expected returns − desired profit

Then incorporate conversion probability to derive the maximum CPC/bid.

That lets the system answer a much more useful question than "What's my ROAS?":

"Given this product's actual economics and probability of conversion, how much should I pay for this click right now?"

That's a particularly compelling proposition for thin-margin retailers because a few percentage points of margin leakage can matter more than a large increase in revenue.

If you're evaluating this as a software/business opportunity, I can also map out the existing vendors, their pricing/positioning, and where the whitespace appears to be.

Yes. There are now several solutions that explicitly connect bid automation to SKU-level margin/contribution profit, rather than optimizing only to ROAS or revenue.

The important distinction is whether you want ecommerce paid-media bidding or marketplace/retail-media bidding.

Strongest fits

• Profit Bid — probably the closest match if you're a Shopify/WooCommerce-style retailer. It calculates product/order-level profit including COGS, shipping, fees, VAT, etc., then sends profit-weighted conversion values back to Google, Meta, Microsoft and other ad platforms. It also automates product segmentation based on POAS. www.profit-bid.com

• Profasee — particularly interesting for Amazon. Its bidding incorporates COGS, fees, price and inventory depth rather than simply targeting ACoS. profasee.com

• Pacvue — more of an enterprise commerce/retail-media platform. It combines advertising controls with retail signals and can protect margins by pausing spend on low-margin, out-of-stock or Buy Box–lost products. pacvue.com

• Eva / Orbit — Amazon-focused and quite explicitly profit-oriented. It combines COGS, fees, fulfillment and ad spend into SKU contribution margin and can prevent scaling when a margin floor would be breached. eva.guru

• Zitcha — more relevant if you're talking about a retailer operating a retail-media network, rather than an individual retailer buying Google/Amazon ads. Its Margin Manager decides which products/brands/channels have the margin to support promotion before activation. www.zitcha.com

There's also a very important development from Google itself: Merchant Center now supports a cost_of_goods_sold attribute, allowing Google to calculate product-level profit, and Google explicitly recommends using high-margin products to inform bidding/feed segmentation. support.google.com

The basic idea

For a thin-margin retailer, you don't really want:

Bid = f(revenue, conversion rate, ROAS)

You want something closer to:

Maximum bid = expected contribution profit × allowable ad-spend percentage

For example:

A conventional ROAS optimizer can treat all three $100 sales similarly. A margin-aware system knows SKU A can tolerate dramatically less bidding pressure than SKU C.

That's especially powerful when margins vary by brand, SKU, supplier, promotion, shipping cost, returns, or inventory position.

If you're thinking about this as a product opportunity

I think there's still an interesting gap.

Most of the existing solutions fall into one of two camps:

1. Profit analytics → feed profit back to ad platforms

Profit Bid / Google-style COGS conversion value.

2. Marketplace PPC optimization with profitability guardrails

Profasee / Eva / Pacvue.

The potentially compelling product is a real-time "profit engine" sitting between merchandising and ad platforms:

ERP/PIM + supplier costs + pricing + inventory + returns → SKU contribution margin → allowable CAC → bid/budget automation

The key would be calculating true incremental contribution margin, not merely gross margin. For a thin-margin retailer, fulfillment, payment fees, discounts, returns, shipping subsidies, marketplace fees and inventory constraints can completely change the economically correct bid. FiveX, for example, explicitly frames marketplace profitability around contribution margin plus fees, fulfillment, returns, pricing and Buy Box conditions. www.fivex.com

So yes—the category exists, but it's fragmented, and there's room depending on exactly which retailer/channel you're targeting.

If you tell me whether you're thinking Google Shopping/PMax, Amazon, Walmart, Meta, or a retailer's own retail-media network, I can map out the competitive landscape and identify where the biggest product gap is.

Yes. There’s now a fairly mature category of profit-based / margin-aware bid automation for exactly this problem.

The key distinction is ROAS vs. profit-based bidding:

• ROAS bidding: “Spend $1 to generate $X of revenue.”
• Profit/POAS bidding: “Spend $1 only when the resulting order generates enough gross profit to justify the spend.”

Google itself supports value-based bidding, but its native conversion value is typically based on the value you feed it; it doesn't inherently know your SKU-level COGS, shipping, payment fees, discounts, etc. support.google.comwww.profit-bid.comwww.datafeedwatch.com

Solutions worth looking at

ProfitMetrics is probably the closest match to what you're describing. It imports product costs and other order-level costs, calculates gross profit/POAS, and feeds profit signals back into Google Ads. It can also segment products and direct Shopping/PMax spend toward more profitable products. profitmetrics.io

Profit Bid is another purpose-built option. It combines COGS/product profitability with POAS-based rules, product labeling, campaign automation, and conversion uploads to Google. www.profit-bid.com

There are also more "build it yourself" approaches. For example, DataFeedWatch can calculate product margin and use margin-based labels to segment Shopping campaigns. www.datafeedwatch.com

For a thin-margin retailer, the important architecture is

I'd want the system to know something like:

Revenue − discounts − COGS − shipping − payment fees − fulfillment costs = contribution profit

Then:

POAS = contribution profit ÷ ad spend

So a $100 product with a 10% contribution margin shouldn't receive the same bid as a $100 product with a 40% margin.

For example:

That's particularly powerful for retailers where revenue-based ROAS can make a product look successful while the retailer is actually losing money. ProfitMetrics explicitly addresses this issue by incorporating COGS, shipping, payment costs, discounts and handling into its profit calculation. profitmetrics.io

One caveat

I wouldn't simply replace ROAS with a single POAS target across the entire catalog. For thin-margin retailers, I'd generally want SKU-level economics + product segmentation + bid/budget automation. For example:

• High-margin / high-converting: bid aggressively
• Moderate margin: maintain efficiency target
• Low margin: restrict bids heavily
• Negative contribution margin: exclude or only advertise under a deliberate strategic rule
• Clearance / inventory liquidation: allow a different profitability target
• New products: separate exploration budget

ProfitMetrics' Shopping Booster, for example, dynamically segments products according to profitability/performance and can put them into different campaign strategies. profitmetrics.io

If you're asking because you're evaluating this as a product/business opportunity: yes, there is competition, but there's still an interesting gap between “POAS analytics” and a genuinely autonomous retail margin operating system that incorporates inventory, supplier costs, markdowns, returns, fulfillment economics and channel-specific CAC into the bid decision. That's a substantially more ambitious product than simply feeding gross profit into Google Ads.

Yes. This category exists, although it is still evolving. The general idea is profit-based bidding or margin-aware bid automation: instead of optimizing ads toward revenue metrics like ROAS or ACOS alone, the system uses SKU-level economics (margin, COGS, fees, inventory, returns, etc.) to decide how much a click is worth. www.profit-bid.comprofasee.com

For thin-margin retailers, the key difference is:

Traditional bid automation:

• “This product converts well → increase bids”
• “ROAS is above target → spend more”
• Can accidentally scale products that lose money

Margin-aware automation:

• “This product makes $8 contribution profit after COGS, fees, shipping, and returns → max CPC is X”
• “This SKU’s margin dropped → reduce bids”
• “This item is out of stock or low margin → stop advertising it”
• “This high-margin product can tolerate higher CAC → compete more aggressively”

A typical architecture looks like:

Product profitability engine
→ SKU-level contribution margin:

• Selling price
• COGS
• Marketplace fees
• Payment fees
• Fulfillment/shipping
• Returns allowance
• Promotions/discounts

↓

Advertising optimization layer

• Google Shopping / Performance Max
• Amazon Ads
• Walmart Connect
• Retail media networks
• Meta/catalog ads

↓

Automated bidding decisions

• Bid caps
• Budget allocation
• Product exclusions
• Campaign restructuring

Some examples of vendors in this space:

• Profit Bid focuses on POAS (profit on ad spend), syncing product costs and profit data into ad bidding decisions. www.profit-bid.com
• Pacvue provides retail media automation with controls that can consider product profitability, inventory, and retail signals. pacvue.com
• FiveX ties marketplace advertising decisions to margin and stock data. www.fivex.com
• Profasee positions its bidding around contribution margin rather than simple ACOS targets. www.profit-bid.comprofasee.com

For a retailer with very thin margins (e.g., 5–15%), I would look for these capabilities specifically:

1. Contribution margin bidding
   • Not gross margin only.
   • Include fulfillment, returns, and channel fees.

2. SKU-level profitability
   • The system should know Product A and Product B have different economics.

3. Profit targets instead of ROAS targets
   • Example:
     • Bad: “Maintain 400% ROAS”
     • Better: “Generate $2.50 contribution profit per $1 ad spend”

4. Inventory awareness
   • Don’t spend aggressively on products that will stock out or have weak availability.

5. New product exploration logic
   • Pure profit optimization can kill new products before they get enough data, so you need controlled testing.

The biggest limitation: many retailers do not have clean enough cost data. The technology is available, but success usually depends on having accurate SKU-level costs and a reliable definition of “profit.”

If you mean a brick-and-mortar retailer with omnichannel inventory versus an Amazon/Shopify/ecommerce retailer, the best-fit solutions are somewhat different.

Yes. There’s a growing category of profit-aware / margin-based bid automation that is specifically designed to solve the problem where a retailer can have a “good” ROAS but still lose money because margins are thin.

A few worth looking at:

• Profit Bid — Probably the closest match if you're talking about ecommerce/retail brands. It calculates SKU/order-level profit using COGS, shipping, payment costs, VAT, etc., then feeds profit-weighted conversion values back into Google Ads, Microsoft, Meta and other platforms. Its core metric is POAS (Profit on Ad Spend) rather than ROAS. www.profit-bid.com
• GROW — Focused heavily on Google Shopping. It calculates the actual cost/margin of each product and derives a product-specific bid from profitability rather than applying one ROAS target across the account. grow.onlinewww.fivex.comwww.wiseroas.comwww.adsrunner.comwww.zitcha.com
• FiveX — More marketplace-oriented. It combines advertising with SKU economics, inventory, marketplace fees, pricing and fulfillment, and offers profit-aware bid automation. www.fivex.com
• Epinium — Particularly interesting for Amazon. Its agent proposes bids/budgets against TACOS and net-margin objectives rather than simply optimizing revenue. epinium.com
• WiseROAS — Amazon/Walmart-focused profit-first optimization with automated bidding, pacing and guardrails. www.wiseroas.com

There is also an interesting distinction depending on what you mean by retailer:

If you're an ecommerce retailer buying ads

The architecture you're looking for is essentially:

SKU economics → contribution margin → allowable CAC → bid → ad platform

For example, suppose:

• Selling price = $100
• COGS = $72
• Fulfillment + payment + returns = $8
• Contribution before advertising = $20

Then the maximum economically rational CAC isn't $50 just because the campaign can achieve a 2× ROAS. It's somewhere below $20 if you want the order to remain profitable.

The sophisticated systems therefore calculate something like:

Maximum CAC = selling price − variable costs − required profit

and use that to determine the bid.

That's much more useful for thin-margin businesses than a blanket “target ROAS = 500%” rule. A 5× ROAS on a 10% margin product can be terrible, while a 3× ROAS on a 40% margin product can be excellent. www.adsrunner.com

If you mean a retailer running a retail-media network

That's a somewhat different—and potentially more interesting—category.

Zitcha has built a product called Margin Manager specifically around this problem. It incorporates product margin, inventory velocity, sell-through, vendor funding and merchant priorities into decisions about what to promote and where to allocate media. www.zitcha.com

Pacvue also has automation that can suppress spend on low-margin or out-of-stock products. pacvue.com

So yes—the solution absolutely exists, but the market is fragmented. There isn't necessarily one dominant platform that perfectly does “bid automation directly from retailer SKU-level contribution margin” across Google + Meta + Amazon + Walmart + retail media.

If you're asking because you're evaluating this as a product/business opportunity, I'd actually say there's an interesting gap here: the strongest solutions tend to be either ad-platform-centric (optimize bids using profit) or retail-analytics-centric (understand SKU profitability), rather than a universal profitability decision layer sitting between the retailer's P&L and every ad auction.