Best practices for Google Ads: The positive, negative, and balanced act

 To attain PPC greatness, it is a difficult but necessary path to challenge Google Ads' best practices. What you should know is as follows.

PPC best practices are derived from diverse sources. These are a few of the sources:

• representatives for Google Ads.
• The Support Desk.
• formal accreditations.
• Both manual and automatic recommendations.
• Ad strength suggestions.
• and even partially automated assets.

However, the results you get may differ greatly based on those sources. Thus, how can one choose whether to implement or question a "best practice"?

A lot of PPC experts can identify with this: You get a little boost of confidence when you see "Google" on your caller ID.

But when you realize that it's usually a young Google employee or even a third-party service that has to check some boxes in order to fulfill quarterly targets—boxes that aren't always specifically customized to the context of your account—reality sets in. Anything they promote as a "best practice" is dangerous because of this conflict of interest.

When the call ends, you understand that our job as advertisers is to ensure we achieve the best results on the lowest possible expenditure. And the purpose of Google Ads, or any other ad network, is to increase your spending.

Thus, it makes sense that we need to have a balanced relationship with Google Ads best practices. Let's examine the most typical problems and identify instances in which Google AdWords perform really well.

Make use of automatic bidding

It's fortunate that there are likely very few people remaining who can manage manual CPC campaigns. I know the PPC community is upset with Google for increasing ad costs; some even claim that the purpose of automated bidding systems is to force advertisers to spend more money. 

However, everyone who conducted accurate A/B tests back in the day is aware that properly configured automated bidding strategies consistently outperform manual bidding 9 out of 10 times.

I'm glad Google Ads introduced them and created such a useful service about a decade ago. You may practically always use it as a best practice.

But why do I say "almost"? It would be ideal if Google Ads could explain the best configurations (conversion density, latency, frequency, etc.). As of right moment, all we got is:

"Smart Bidding can still use query-level data beyond your bid strategy to build more accurate initial conversion rate models when you have little to no conversion data available."

-"How our algorithms for bidding learn," Aid for Google Ads

You should probably stop using manual CPC if your automated bid strategy isn't performing as well as you'd like. Otherwise, check the aforementioned parameters.

Expand using broad match terms

Similar to automated bidding, broad match, or automated targeting, is also becoming increasingly good these days. Therefore, I think Google Ads is correct to emphasize those broad match types more. 

Numerous studies demonstrate the superiority of broad match keywords over phrase match keywords. Lighter campaign structures facilitate easier campaign maintenance and expedite PPC staff training. What then is to dislike?

However, I would still advise against utilizing such a match type carelessly, much like with automated bidding. As an illustration, I strongly discourage turning on:

• the auto-apply recommendation to "Grow your Smart Bidding campaigns with broad match".
• The campaign setting for broad match keywords.

Because of what? Because Google AdWords hasn't released information on ideal setups, similar to automated bidding.

Experience has shown that, before putting your faith in Google's AI, you should use some early down funnel data (purchases) to inform your bid strategy. If not, it will yield surface-level results like any other AI since it is only interested in the top-of-funnel KPIs (pageviews, etc.).

A further recurrent theme is the information being gradually removed. As usual, Google was able to restrict search term reporting by citing privacy concerns.

Visibility isn't improving with broad match keywords, but performance is. Regretfully, search term reports were a useful tool for making more comprehensive marketing decisions.

Expanding using broad match keywords is a fascinating recommended approach. However, you ought to understand its limitations.

Upgrade to attribution based on data

You begin to see a pattern, correct? Another AI-driven Google innovation is data-driven attribution (DDA). It also has value, much like the prior best practices. but also restrictions.

The fact that DDA demonstrates to marketers that cross-channel journeys are real is perhaps most significant. Since distribution of conversions is more granular and less angular by nature, it naturally enhances overall performance across multiple audiences.

But in my opinion, Google is being incredibly opaque here (I mean, even more than for auto bidding and broad match types).

It's true that conversion paths by user cohort are hidden. The ability to compare DDA to other attribution models is no longer available.

Depending on your shopping journey, the only other option left is last-click attribution, which is infamously simplistic.

Ultimately, it's a fantastic feature, but it displays the first indications of Google's negative aspects: it disregards your context and has a negative opinion of you, the advertiser. Please understand that I enjoy processing and automating tasks whenever I can. AI is also a very useful tool.

But even in data marketing, it is a sin to believe that everything can be measured.

What happens if, for whatever reason, the data that Google Ads' algorithms use to determine your buying path doesn't accurately reflect your journey? What will be the foundation of DDA's conversion distribution? Both of our guesses are valid.

Therefore, even though I think the majority of advertisers should adhere to this best practice, I also think that everyone should be fully aware of its drawbacks and compare the results of DDA with those of other attribution or incrementality studies.

Adopt campaigns with Performance Max.

This best practice is consistent with automated bidding, DDA, and broad match keywords. However, it goes one step further: with the help of Google's AI, the ideal media mix will be found for you. Surprisingly, it is fully capable of delivering.

Why then do I have an orange traffic light next to that best practice?

Due to:

• The majority of advertisers are just not prepared to use this kind of tool.
• Too frequently, tracking is restricted to MQLs for lead generation clients and revenue for e-commerce clients; there is no sense of LTV or profits.

Data pipelines frequently contain another gap in the toolsets of advertisers:

• Too few conversions are occurring.
• Traffic managers don’t care about freshness.
• Rather than being a must, frequency is a nice-to-have.

The Performance Max algorithms of Google Ads will ultimately provide impressions in rough proportion.

Keep in mind that the quality of their output depends on the input you provide. Do you believe your system is robust enough to support a creature like that?

Thus, you should reconsider if your preferred Google Ads representative advises you to set everything to Performance Max. Do you check every box below?

• At the very least, should I feed Google Ads with revenue data?
• Is my shopping journey sufficiently brief?
• Does the majority of the traffic to my company's website come from non-branded and non-retargeting sources?
• Are I prepared to forfeit a wealth of insightful marketing knowledge?

Of course, there are a lot more questions you could ask yourself, but those seem like the most important ones. As you can see, adopting Performance Max is not a simple best practice.

Being discerning: Context versus best practices

Most of the time, but not always, best practices are effective. They can conceal absurd standard deviations, just like averaged metrics.

Thus, continue to be skeptical of best practices based on your understanding of the account. Most of the time, AI finds it difficult, if not impossible, to fully understand that context. This is where your actual worth lies.

I'll give you a couple instances. Assume you are the owner of a gift card company. You are undoubtedly aware of the market's extreme seasonality and how quickly customers move through it.

A few days before Christmas, those who are running late with their gift-giving will search for "gift card" online and make their purchase right away.

Does using DDA make sense in that scenario? Most likely not.

Does using Performance Max make sense? Most likely not, too.

Those prospects will primarily use search. That is all. That is the background information guiding your marketing plan. It's not a universal best practice that instructs you to behave irrationally.

Here's one more instance: Let's say that your company is subscription-based. Is it wise to retarget users of a website and overuse branded keywords?

Most likely not. Most of those users will already be paying clients.

So, would it make sense to combine Performance Max with a subscription-style bottom-of-funnel goal? Most likely not, as Performance Max would become extremely excited about branded and retargeting campaigns. Additionally, those wouldn't add much value.

It is imperative that advertisers evaluate PPC strategies critically. As the owner of a data marketing company, I stress that tactics shouldn't be based only on data.

It's not necessary to adhere to every best practice offered by Google Ads or other ad networks, particularly if their primary selling point is being "AI-infused."