Conversion Optimization Explained: Target CPA Costs

Understanding Google's Target CPA: The Hidden Costs of Conversion Optimization

TL;DR

Google's ad auction system traditionally uses a second-price model where quality score influences ranking and pricing.
Modern conversion-optimized campaigns add user conversion probability as a pricing factor.
This creates premium pricing for high-intent users, typically only accessible through conversion-optimized campaigns.
While effective for capturing existing demand, these campaigns have natural scaling limits.
Success requires a multi-channel approach balancing high-cost, high-intent targeting with broader awareness/engagement efforts.

Introduction

Google has long been heralded as the gold standard in performance marketing thanks to the high intent of search queries. For those looking to sell a product, there’s no better way to find prospective buyers than to target search terms related to that product. It’s the main reason Google has been able to achieve astronomical growth as a business in the last 20 years.

However, this reliance on an ad revenue model only works if advertisers are seeing monetary benefits and choosing to invest more money into the platform. To this end, Google has always developed more ways to deliver value to its customers (advertisers), most of which have genuinely improved the ad experience.

Google’s evolution in advertising

From humble beginnings in the search market, Google has expanded many times over to ever improve their product. Integrating YouTube into the Google ecosystem not only offered a new channel of inventory but also a new way to deliver messaging. Expanding the Display Network into Pixel phones using the Discover tab enabled more effective mobile advertising. Incorporating ads into Google Maps has helped to better serve local businesses with smaller ambitions.

However, not all of these changes have been product-focused. Google, first and foremost, is a data company, meaning it captures and stores vast amounts of data on its user base. This wealth of data Google maintains on each user has been instrumental in developing automated bid strategies like conversion-optimized bidding.

Understanding Google’s data infrastructure

The scale of user data

Google’s data graph, or individual data points on each user, can be measured in gigabytes. To put that into perspective, you could fit roughly 100,000 pages of a Word doc into 1 GB. Depending on account history, 1 GB of data is a very conservative estimate. If you’re curious to see for yourself, you can download the data linked to your account at this link.

This level of tracking spans all Google products, including but not limited to:

Search history
Location data
Watch history
Calendar information
Contact details

Impact on advertising capabilities

In short, this means Google’s ad campaign targeting capabilities are second to none (Meta may dispute that). At the very least, Google is able to get a pretty accurate picture of who you are, what your interests are, and whether you’re thinking about buying those shoes you liked, or are at the point of having your credit card in hand.

Advertisers looking to sell products or get in front of the right decision-makers can make use of this data, for a specific cost, of course. The result is hyper-targeted ads to individuals who are searching for your products, browsing competitors, or otherwise researching/considering your product or service. Sounds pretty great, right?

Before we jump to conclusions, let’s take a step back and review how ad auctions have worked historically.

How do ad auctions work?

Second-price auctions

Ad auctions have traditionally been second-price auctions, wherein the advertiser who bids the highest pays the second-highest advertisers’ bid plus 1 cent. For example:

Advertiser A bids $2 Advertiser B bids $3 Result: Advertiser B wins but only pays $2.01, one more cent than Advertiser A’s bid.

Goodway explains automated bidding well in their blog.

Google’s Quality Score

Google takes this one step further by incorporating what they call a quality score, which is derived from three factors:

Expected click-through rate
Ad relevance
Landing page experience

This is Google's way of ensuring that it delivers the best experience to the end user. The result is a system called 'Ad Rank', which multiplies an advertiser's bid and Quality Score (rated 1-10).

Let’s look at an example:

Advertiser A: $2 bid x Quality Score 8 = Ad Rank 16

Advertiser B: $3 bid x Quality Score 4 = Ad Rank 12

In this case, Advertiser A will earn the top page result despite bidding less. Their actual cost is calculated as:

Actual CPC = (Next highest Ad Rank / Your Quality Score) + $0.01

= (12/8) + $0.01

= $1.51

Advertiser B could pay up to $3.01 depending on whether there is a third advertiser in the auction, primarily due to their below-average Quality Score. This demonstrates how Google's system rewards engaging ad campaign ads and landing pages while penalizing poor user experiences—effectively allowing advertisers to "buy" better positions through either higher bids or better quality content.

Conversion-Optimized Campaigns

Conversion-optimized models like Maximize Conversions or Target CPA transform this system by assigning a conversion probability to each user in Google’s data graph. Someone searching for new shoes may visit several retailer sites (Foot Locker, Nike, Adidas) before purchasing, causing their conversion probability to increase. Conversely, first-time shoe searchers would register a low conversion probability.

For instance:

High-intent user (multiple relevant site visits): 10% conversion probability
Low-intent user (minimal related activity): 1% conversion probability

Instead of just considering your bid and ad rank, the new formula incorporates conversion probability and the target cost per conversion.

New auction formula

Ad Rank = (Target CPA x Conversion Probability x Quality Score)

You can set your desired CPA (Target CPA), or leave it unrestricted, indicating willingness to pay whatever is necessary to win the auction within campaign budget constraints.

This fundamentally changes how the auction works. Rather than bidding for clicks, advertisers effectively bid for conversions. While the second-price auction rule still applies ($0.01 more than the next highest bid), CPC bids can vary dramatically based on conversion probability.

The result is a tiered smart bidding system where high-probability auctions become premium inventory, requiring significantly higher bids for entry. More often than not, advertisers need to use a conversion-optimized model to access them.

Strategic implications

This bid strategy model presents distinct opportunities and challenges:

Advantages:

Highly effective at capturing existing demand
Automated optimization for high-intent users
Simplified CPA bidding management

Limitations:

Not effective for demand generation
Targets only near-purchase users
Scaling challenges due to diminishing returns
Premium pricing for high-intent auctions

Recommendations

The current landscape demands a more sophisticated, multi-channel approach that:

Introduces products/services to new users before conversion attempts
Leverages different channels for different stages of the buyer journey
Balances high-cost, high-intent targeting with broader awareness efforts

Conclusion

While Google promotes conversion-optimized campaigns as the optimal solution, there is no one-size-fits-all solution to marketing across industries. Success in modern digital advertising requires a comprehensive smart bidding or CPA bidding strategy that extends beyond relying solely on Google's automated bidding systems.

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