The Ultimate Guide to Negatives

Learn how to effectively review your search terms and add negatives to save money and improve your targeting on Google ads.

Guide Summary

This guide covers how to set up and strategically implement an auto-negatives Ad Script for shopping and search campaigns.

You will learn:

What the benefits are of having a comprehensive setup of negatives
The modern arguments against extensive negatives
Reactive vs proactive negatives
How to review a search term report
Advice on how to approach the selection of negatives
What an auto-negatives script is, and how to use it effectively
How to research proactive negatives
Themed lists of proactive negatives
Using negatives to shape traffic
How to maintain a healthy negatives setup

🗒️ For this guide, it’s assumed that you know and understand how negative keywords work.

The benefits of a strategic negatives setup

Every account triggers for search terms that—across the board—simply don’t convert, either at all or at profit. Then, at each campaign’s level, there are more terms that don’t work for that campaign, even though they may work in other campaigns. And finally, the same at the ad group level.

Negative keywords and negative keyword lists Google’s provided tools for preventing inefficient spend on these searches. With these searches blocked, traffic quality is improved. Google’s algorithms, seeing the effects of this improvement (lower bounce rates, higher conversion rates), then ranks our ads higher, which decreases our costs.

This is the main goal of building up a comprehensive negative keyword and list setup.

We can’t rely on machine learning

Google’s machine learning is incredible. It far outpaces what humans can do, evaluating millions of factors in every auction your ads enter. Over time, their AI system will discover which search terms do and don’t work for your store.

But it needs time. More consequently, it needs budget. Your money. As amazing as its machine learning is, it still can’t spot what a human can see right away. A human can review a list of search terms and easily identify those that don’t make sense to—and never will make sense to—bid on.

This is what makes frequent and regular search term reviews vital. Unless your store has lots of time and money to simply let the machine learning chew through its millions of factors over through billions of search queries, you need to have a strategic negatives setup.

What a comprehensive, strategic setup looks like

A fully planned setup of negative keywords includes:

Negative keyword lists that are grouped and themed, applied across multiple campaigns
Campaign level negatives
Ad group level negatives

The argument against using negatives

Many respected experts in the PPC industry are now making a case against working on negatives to the extent detailed in this guide. Generally, they warn against the mindset of a “PPC traditionalist”, meaning the veteran digital marketers whose methods are rooted in an era before today’s AI technology.

It’s all a fascinating discussion, and they’re quite right; but only in the context that their advice is being given. That context is giving up control to Google’s smarter automation. Let’s explore the argument.

The negative side of negatives

The marvel of Google’s AI

Today’s newest wisdom is: Don’t constrain an AI by telling it what not to do. The principle is to get out of the way and let the machine learning do its thing. Your handcrafted heuristics and basic human biases will only dilute the path to the goal.

This has been working marvelously, especially by Google’s elite AI engineers working on remarkable, groundbreaking projects like DeepMind.

“In late 2017, we introduced AlphaZero, a single system that taught itself from scratch how to master the games of chess, shogi, and Go, beating a world-champion computer program in each case.”

— AlphaGo: The Story So Far - DeepMind.com

The same incredible AI problem-solving abilities are what’s being applied in Google Ads automated bidding and “Smart” campaigns. Google Ads’ deep neural network and algorithms involved are truly ushering us into a new age of digital advertising. Of that, there can be no doubt.

Don’t cripple the learning phase, embrace liquidity

In their paper called “Understanding Liquidity”, the International Advertising Board defined liquidity this way.

“When every dollar is allowed to flow to the most valuable impression, we call this condition “liquidity,” and it is made possible when humans take their hands off the controls and allow the system to read the terrain.”

“...planners and buyers must choose whether to restrict opportunities considered for delivery during the campaign. Setting as few restrictions as possible, thereby increasing liquidity on as many dimensions as possible, should confer the greatest benefit.”

— Understanding Liquidity - IAB.com

The counter-argument

If you’re selling red roses, and only red roses, do you want to pay for Google’s machine learning to put it together that search terms for “blue” roses won’t convert? It will finally understand that one day, and then will only test “blue roses” searches rarely to see if that remains true.

Or, you can simply prevent it from having to learn that on your budget. Sure, the AI could one day figure out that some shoppers may think they want blue but can be persuaded to purchase red, and put together a very complex dataset that increases the likelihood of conversion.

Ask yourself, at that point, how much have you spent on highly unqualified clicks to get to slightly less unqualified clicks? Do you have that kind of budget?

If not, read on.

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