Three Advertising Platforms, Three Different Kinds of Trouble

Google, Microsoft, and Amazon all make digital advertising look increasingly simple.

Choose a goal. Set a budget. Supply products, keywords, or creative assets. Then let the algorithm find customers.

From a distance, the three platforms appear to be variations of the same system. They all sell access to demand. They all promise better performance through automation. They all produce dashboards filled with impressions, clicks, conversions, sales, and return on ad spend.

But after managing advertising across all three, I have come to think that each creates a different characteristic kind of trouble.

Google tempts you to surrender too much control to powerful automation. Microsoft tempts you to treat it as a smaller copy of Google. Amazon tempts you to mistake revenue for profitable growth.

The platforms are not bad at what they do. In many respects, they are remarkably capable. The problem is that each optimizes within its own system, according to its own incentives and its own definition of success.

The business owner still has to decide what success actually means.

Google: the trouble with powerful automation

Google is the most sophisticated and scalable of the three platforms.

Its search volume is enormous. Its bidding systems are advanced. Its campaigns can pursue demand across search, shopping, video, display, and other placements faster than any human manager could do manually.

That power becomes dangerous when the account is not tightly defined.

In one of our search campaigns, a switch to Maximize Conversions transformed a manageable campaign almost overnight. Average click costs rose above eight dollars, and more than two hundred dollars disappeared on twenty-four clicks before nine in the morning.

Google had not malfunctioned. It had interpreted the instruction aggressively.

We had told the system to pursue conversions, and it found auctions where it believed conversions were available. But the campaign lacked enough economic restraint. The algorithm understood the goal we gave it; it did not understand the business context surrounding that goal.

That distinction matters.

A conversion is not automatically worth any price. A click is not automatically valuable because it appears commercially relevant. And a campaign that increases volume can still make the business worse if it pays too much for the wrong opportunities.

The same problem appears in other forms. Broad matching can pull traffic into adjacent categories. Shopping and Performance Max campaigns can blur product boundaries. A core product campaign may begin attracting interest intended for pets, jewelry, biodegradable products, or other categories unless the account has strong lane control.

Google’s characteristic danger is not that the machine is weak. It is that the machine is powerful enough to exploit every ambiguity you leave behind.

That means conversion tracking, product segmentation, negatives, budgets, and campaign ownership still matter.

Automation does not eliminate structure. It makes good structure more important.

Microsoft: the trouble with assuming it is just smaller Google

Microsoft Advertising feels familiar because it resembles Google.

Its campaigns look similar. Its settings often look similar. It can import a Google Ads account with very little effort.

That convenience creates a dangerous illusion: that the campaign has not only been copied, but successfully adapted.

It has not.

In one account, Microsoft suddenly began outspending Google on a given day. That made little sense if I thought I was buying search traffic. Bing’s search audience is much smaller.

The explanation was that the campaigns were drawing far more impressions and clicks from the Audience Network than from search itself.

The imported account looked familiar, but its distribution was behaving differently.

That incident captured Microsoft’s characteristic problem. Campaign structure may transfer. Campaign behavior does not.

Microsoft operates with different search volume, audience characteristics, distribution settings, conversion density, and bidding dynamics. A strategy that works with Google’s scale and data may perform very differently in a smaller environment.

Imported settings can create trouble too. Budgets may not translate as intended. Bid strategies may be poorly suited to the available conversion volume. Network exposure can expand beyond what the advertiser assumes. Tracking may need separate verification.

And because Microsoft is usually the smaller account, it often receives less attention.

That is where the danger grows. Familiarity encourages assumption. Lower spend encourages neglect. Problems can persist because the account appears too small to deserve close scrutiny—until it suddenly is not.

Microsoft’s characteristic danger is neglect disguised as portability.

The right approach is to treat it as its own advertising ecosystem. Import structure if useful, but re-evaluate distribution, bidding, budgets, tracking, and search behavior from the ground up.

Amazon: the trouble with mistaking revenue for economics

Amazon presents a different temptation.

The shopper is already inside a marketplace. The product is visible. The purchase happens on the same platform. Compared with Google or Microsoft, the path from advertisement to sale appears much clearer.

That makes Amazon advertising feel especially measurable.

Spend a dollar. Generate a sale. Calculate return on ad spend.

But the apparent clarity can be misleading because Amazon makes revenue highly visible while many of the underlying costs remain outside the advertising dashboard.

One automatic campaign for keepsake urns illustrated this well.

At the campaign level, the results looked respectable. But the average concealed three very different kinds of traffic. Close-match targeting produced a 10.62 return on ad spend. Loose matches produced 5.11. Substitutes produced 4.17.

The platform could summarize all of that as one campaign result. The business could not afford to treat those forms of traffic as equivalent.

Close matches were highly productive. Loose matches and substitutes were materially weaker. The useful management decision was not simply whether the campaign was “working.” It was which parts deserved more investment, which terms should move into controlled campaigns, and which traffic should be restricted.

Even those ROAS figures were not the final answer.

A strong return on ad spend can still produce weak profit after product cost, marketplace fees, shipping, packaging, personalization, labor, returns, and replacements are considered.

Revenue is the platform’s favorite measure because the platform does not pay your shipping bill.

Amazon also encourages advertisers to think in averages, even though product economics differ dramatically. A heavy item, a personalized product, a small keepsake, and a higher-margin premium item should not all operate under the same advertising expectations.

Amazon’s characteristic danger is not simply overspending. It is mistaking marketplace sales for healthy business performance.

The correct starting point is contribution margin, not revenue.

Instead of asking only, “What ROAS did this campaign achieve?” the better question is, “What can this product afford to spend after all the costs of making, selling, and delivering it are included?”

Amazon advertising has to be managed from the economics backward.

Three platforms, three managerial disciplines

Each platform therefore demands a different kind of discipline.

Google requires boundaries. Its automation needs accurate measurement, sensible structure, clear product ownership, and limits tied to business economics.

Microsoft requires independent scrutiny. It may inherit useful campaign architecture from Google, but it cannot inherit sound management by import.

Amazon requires economic honesty. Sales and ROAS are not enough. Advertising decisions have to reflect margin, fees, fulfillment, shipping, and labor.

The common lesson is not that automation should be resisted.

It is that automation must be governed.

These platforms can process more auctions, identify more patterns, and adjust more bids than any human manager. But they cannot decide what kind of business you want to build. They cannot determine which customers are worth acquiring, which products deserve investment, or which apparent successes conceal poor economics.

The platform optimizes the auction.

The business owner still has to optimize the business.