Optimized Broad Match and Why You Should Use It

If you missed the first part of this series, check out The Dos and Don'ts of Broad Match on Google Ads.

In the ever-evolving landscape of Google Ads, understanding the nuances of keyword matching is crucial for maximizing campaign performance. This guide delves further into what we can learn from Broad match keywords, drawing on insights from a comprehensive automation test aimed at optimizing Broad match strategies.

To paraphrase Arthur C. Clarke, advancements in technology can seem like magic when they’re first introduced. In the case of Google, Broad match was introduced with promises of smart campaigns that would be able to do the work of finding new customers for us. But as with any new technology, it takes time and testing in order to fully understand the scope of its benefits, and more often than not, its drawbacks. 

This can lead people managing campaigns for themselves, or agencies working to drive profitable performance for clients, to feel stuck at a crossroads. Do we embrace this new change, or wait until we understand it a little better, at the risk of missing that exciting “early adopter” stage of innovation implementation?

Our research to this point can do the heavy lifting and shoulder some of the challenge we face as marketers to predict the future. Of course results will differ from campaign to campaign, but through time and concrete testing structure, we’ve learned a number of things about Broad match and how it works. We’ve learned what it will do when left alone, and what kind of guidance and support Broad match requires to produce the best results possible. 

This post outlines the key findings from our journey, exploring the hierarchical dynamics of keyword types, the importance of maintaining a balanced keyword strategy, and the introduction of our Growth Efficiency Index to gauge campaign scaling efficiency. Whether you're an experienced digital marketer who’s been around for decades or just starting with Google Ads, understanding these principles will empower you to leverage Broad match keywords more effectively.

HOW DID WE GET HERE?

Here’s a link to our previous post which spurred the test conducted here. It’s not required reading in order to understand the concepts covered here, but it goes into further detail on how the initial structure of our campaigns was determined, and how you can set that up yourself. To briefly summarize, we saw Broad match wasn’t doing what google told us it would, and designed a new campaign structure in order to help it reach its true potential. 

We created the campaign structure below, and applied it to multiple Non-Brand Search Campaigns for a national retail client, spending roughly $50K over the course of 6 months. 

One of the risks inherent in this structure is that we’re going to be seeing performance come in from these Broad match keywords, and then immediately adding in negatives that prevent the next day's campaign spend from doing what it did yesterday. 

So here’s our hypothesis: Automated Negation of Search Terms delivered by Broad Match keywords that can be delivered by Phrase-Exact keywords will not stop all delivery of our Broad match cohort. 

And the results confirmed our hypothesis. We did not see all Broad match spending stop, and actually saw it grow over time. Now let’s look at the data, and see just how much spend from the Broad match campaigns was negated by our scripting. 

Initially, our campaigns faced significant challenges, with 23% of our total Broad match spend coming from overlapping terms that could cannibalize more effective campaigns. To put this into perspective, our script was adding as many as hundreds of negative Exact match keywords a day, every day. But it’s clear that frequent addition of negatives allowed us to achieve remarkable improvements over a 10-week period.

As we navigated through this transformation, we observed a significant reduction in cannibalized spend—from 23% in the first week to just 1% by week ten—while simultaneously growing our overall campaign.

Over the course of 10 weeks, we saw relative % of total spend going to these “best practice search terms” drop off steeply, stabilizing to lower levels by week 5! At the same time, we also saw consistent growth in relative and actual spend from our Broad match cohort, indicating that we did more than just restrict and box in our Broad match spending. Total spend across all keyword types grew by approximately 169.25% over 10 weeks, with an average weekly growth of about 18.81%. 

At the same time, we saw a total drop of 22% in cannibalized spend over the 10-week period, declining from 23% in week 1 to just 1% by week 10. This significant reduction highlights the effectiveness of our automation implemented using the format outlined here (link to part of previous blog post)

And this is what our test delivered, from a keyword level spend perspective over the course of 6 months, with the before period in months 1 through three using Google's recommended best practice, and months 4 through 6, using ours. 

But the benefits of our new structure extend far beyond adding large numbers of negative keywords frequently through automation. We Broadened our spend across keyword types, particularly increasing spend on Phrase and Exact keywords vs the time period when Broad match was increasing at the behest of its counterparts. 

The primary metrics we will be looking at here will be the ability of our campaign to generate traffic, and to improve our ROAS (Return on Ad Spend). What we want to see is growth in ROAS, and more efficient CPC’s both in aggregate and at the keyword level. 

There is a negative correlation between performance and spend while scaling up Google Ads Campaigns. Thus it is expected that we will see a slight reduction in metrics like ROAS as we increase spend. In order to measure the impact of our new strategy on our ability to scale, we will repurpose a metric more commonly used to predict whether a SaaS company has a profitable model for generating revenue; the Growth Efficiency Index. 

GROWTH EFFICIENCY INDEX

The Growth Efficiency Index (GEI) is a straightforward metric for understanding how well your campaign is scaling. When you push budget higher, it’s natural for ROAS to dip; GEI steps in to measure whether that spend increase is pulling in enough revenue to make the trade-off worth it. By calculating incremental revenue efficiency—revenue growth as a proportion of spend growth—and adjusting for the ROAS decline, GEI provides a clear picture of whether your campaign is scaling efficiently.

A positive GEI score means you’re pulling in strong incremental revenue despite the ROAS dip, indicating efficient scaling. If it’s negative, it’s a signal to revisit strategy before spending more. GEI is designed to give you a realistic, data-driven way to evaluate growth so you can maximize performance while scaling up. For our campaigns, a benchmark of 0 would be the minimum we would like to see. 

With this in mind, our test yielded a GEI of .4, and we saw even stronger growth at the keyword level for Phrase match keywords, which generated a GEI of 2.51 in the experiment. 

Exact Match Keywords yielded a GEI of 0, indicating that there wasn’t much more room to grow, but this is also factoring in the fact that we saw a massive growth in Exact match spend once we stopped Broad match cannibalization, with 185% increase in Exact Keyword Spend vs Prior Period. Thus we can conclude that we let Exact match do Exactly what it needed to do, to its fullest potential. 

Broad Match yielded a GEI of .16, which while positive was relatively low, aligning with the nature of Broad matches' limited niche and specific purpose. 

We took Broad match, which when added to existing best practice campaigns had hardly any spend or conversion value, and doubled our Broad match ST spend without major losses to ROAS. 

To recap one of the defining elements of how search terms and keyword match types interact with each other, we’ve included below the previously identified hierarchy provided by Google in order to understand how they match search terms to corresponding keywords within our campaigns. 

Here’s Google's official hierarchy: 

1st Priority: Exact-Match keywords that are identical to the search (For the search term "skydiving license", the identical Exact keyword [skydiving license] is prioritized over any other keyword.)

2nd Priority: Phrase- and Broad-Match keywords that are identical to the search. For the search term "skydiving license", the identical Phrase keyword “skydiving license” is prioritized over the Phrase keyword “skydiving”. 

3rd Priority: AI (artificial intelligence) based keyword prioritization The search term "skydiving certifications near me" could match to keywords in several ad groups, some of which may be more relevant, such as “skydiving license,” and others may be less relevant, such as “skydiving courses for beginners”. In this case, only the most relevant Broad-Match keywords from the most relevant ad groups are considered.

There’s a glaring point missing from the second priority here. The description states that priority is given to “Phrase and Broad match keywords that are identical to the search.” This raises the question, what happens when there is both a Phrase and a Broad match keyword present? Shouldn't the Phrase match keyword get the traffic first?

The basis for justification under Google's official hierarchy is that when Exact, Phrase, and Broad match keywords are all present in an ad group, traffic will naturally be delivered to Phrase and Exact matches first, with Broad match keywords taking up the least spend. We tested this, and the results we saw were quite different. 

You might ask yourself, “why does it matter which keyword gets the traffic? Isn’t it all the same at the end of the day, within the same campaign?” The answer to this is, it matters because within campaigns that don’t have unlimited funds, it’s important to put our money where we know we will generate the greatest return. Sometimes, this is in Broad match keywords, but not always. If we remove Phrase match and Exact match from the equation, and rely on Broad match to do all of the work, we’re missing out on a few things. 

How Do We Even The Playing Field? A New Hierarchy 

Through testing, we determined that Broad-Match Keywords sit above Phrase-Match keywords in the hierarchy, and that Phrase-Match search terms are just as, if not more likely to deliver for Broad-match keywords than Phrase-Match keywords when both are present in a campaign. 

The table below highlights our initial approximated hierarchy within Google's best practice recommendation, based on data from tests we’ve made at our agency.

The biggest problem with this structure is that it pushes our Broad match keywords to deliver Phrase match search terms before our Phrase match keywords, from a hierarchical standpoint, and our test results confirm this, as you can see below

Before (3 Months) (Googles Best Practice)

This is what we saw at the search term / keyword level when we added Broad match to our existing ad groups for a period of 3 months.           
In the past, Google's best practice was to incorporate a mix of Phrase and Exact keywords within campaigns. This was the case before Broad match was introduced of course, and this is absolutely not to say “be resistant to change”.

But there is virtue to the idea of “If it ain’t broke, don’t fix it”. 

Phrase match keywords are proven to deliver Phrase match search terms that generate -21% lower CPC’s than Phrase match search terms from Broad match keywords (Based on a time period of 6 months). 

So yes, this confirms the hypothesis that if we move all of our Phrase traffic to Broad match keywords, we will likely see that drop in quality. And our new structure for campaigns safeguards against this. But what’s even more important is that our results confirmed that Broad match can be an essential element to a well structured search campaign.

Broad match keywords spent a lot over the course of our entire test. Spending 31% of total budget over the course of 6 months, the second most of all three keyword types.

But there was a clear decline in efficiency over time, and by the third month of testing Google’s recommended structure, this was what the search term keyword hierarchy looked like

In the "before" period, we saw a significantly different spend allocation and growth pattern:

1. Broad Match Growth: Initially, Broad match spend started low but grew sharply over the three months, from $552 in Month 1 to $3,388 in Month 3. This increase took a larger share of the budget, from 11% in Month 1 to 53% in Month 3
2. Total Spend vs. Average: In the "before" period, total spend fluctuated around the average. Month 1 was 19.44% below average, while Months 2 and 3 went above average by 11.91% and 7.53%, respectively. However, the incremental increases were more modest than in the "after" period, when Broad match really took off.
3. Exact and Phrase Match Cannibalization: Broad match spending in the "before" period appeared to "cannibalize" Phrase match spend, as the Broad allocation rose consistently over time while the Phrase percentage fell from 36% to 18%. Exact match saw a less dramatic decline in allocation, but still dropped as Broad rose. 

However, at month three we made the pivot to the new structure, and the results by Month 5 (September) confirmed our theories, and displayed a stronger, more efficient spend hierarchy than what existed before. As you can see, there’s a significant growth in size from “-Broad” result volume between the two periods. 

The most distinct shift is that by Month 5, we have Exact match search terms almost entirely isolated to Exact match keywords, with a small volume coming from Phrase and Broad. 

We also have a clear priority on Phrase match search terms coming from Phrase match keywords. However, we continue to see a lot of Phrase match coming from our Broad match keywords, but in this case, we know that these Broad match Phrase search terms are distinct from those delivered by Phrase match keywords. 

The difference is indicated by the distinctions in CPC, and ROAS between Phrase search terms coming from each type of keyword, and at the aggregate level, we can see improvements in efficiency, visualized below with GEI alongside Month over Month Spend Growth

These are a lot of metrics and numbers, but the real question is, what happens to each keyword-search term level result, and what are these campaigns actually delivering. Below we can dig in at this more granular level, and highlight some of the most valuable situations we encountered over the course of our test. 

Exact Match Keywords Under Our Structure

Exact match performs as it always would under any other structure, all we do is maximize it. It has been best practice for years now to utilize Exact match keywords alongside Phrase match keywords, and our data confirmed that there is no reason to break from tradition and separate our Phrase match keywords from Exact. We found that for a keyword like “Levi’s Denim Jeans” Exact match keywords delivered $300 in spend, and $1,000 in revenue over the course of our test. (Bauer hockey gloves)

The Phrase match keywords delivering Exact matches hardly make up enough spend to be significant. For context, all Phrase match keyword Exact match search term results spent $110 over the course of the experiment, without generating any conversions. This is understandable given the insignificant spend here.

However, when Broad match keywords deliver Exact results unfettered, they just do a slightly poorer job of allocating spend to the right keywords. In the initial 3 months without optimized structuring, Broad match keywords drove up spend for Exact match terms that could be used to drive innovation, through Broad-Phrase, or Broad-Broad traffic.           
In the before period, when Broad match was allowed to infringe on Exact, it delivered $4K in spend and $10K in revenue, on “Exact match search terms coming from Broad match keywords” for a ROAS of 2, while Exact match dropped $10K in spend, and 30K in revenue for a ROAS of 3. What we are considering here isn’t the strength of Broad match at delivering Exact search terms, it’s the opportunity cost. We already have Exact match, we don’t need Broad for this. 

At its core, most of the Exact search terms Broad will be delivering are ones Exact match keywords can optimize spending for better. And if they are “Exact match variants”, we see terms like “jeans denim Levi’s” for keywords like “Levi’s denim jeans”, which isn’t significant enough to focus on, or helpful from a revenue generation perspective. Post restructuring, we decreased our total Broad-Exact spending down to $12, instead of $4K. Not only did we decrease the Exact search term volume coming from Broad, we also increased our investment in other tools available to us. 

Phrase Match

For a given keyword like Levi’s Jeans, Broad match delivered $700 in spend and $1,400 in revenue, generating a 2 ROAS, while Phrase only delivered $30 in spend, but generated $200 in revenue, a stronger ROAS of 6.7. This indicates that while Broad does have the ability to generate meaningful Phrase match keywords, it isn’t going to have the same level of targeted specificity that makes Phrase valuable to our campaigns. That is why it is better to let them collaborate, and work together, instead of letting Broad do the job of Phrase.

Levi’s Jeans- delivered a Phrase-Phrase match for Levi’s Bootcut Jeans, and Broad delivered a Broad Phrase for Levi’s Bootcut Jeans. Because of our structure, Broad was informed not to deliver any more traffic for this specific search term, since Phrase is capable of delivering for it. This allowed Broad to get more creative and deliver for “Levi’s Flared Jeans Size 32”, which was able to secure conversions for the term and deliver additional impressions for traffic not covered under the best practice ad group .

Broad Match

Finally, the promise of machine learning and the keywords of the future! A lot of the first steps in this new structure, is to ensure Broad has the ability to focus on the one thing it can do, that other keywords cannot. We need Broad to do the hard work of identifying new terms and keywords that Phrase just isn't brave enough to go for. 

But this gets obscured when Broad match keywords try to do the same thing Exact match keywords do better. Additionally, as we showed above, Phrase match suffers when Broad match tries to “cheat off their work” too. Before we added our scripts, and restructured our campaigns, Google’s “best practice” Broad match additions to existing campaigns spent a measly $600 on Broad match search terms, and failed to break even from a ROAS standpoint, with a ROAS of only .50. 

That’s where our restructuring, and “Broad match redirection” comes in. Post restructuring, these search terms spent $1,200 and delivered $4,400 in revenue, for a ROAS of 3.7! These terms like “Levi’s Cone Mills Skinny Jeans” delivering for a Broad match keyword as simple as “Levi’s Jeans” (spend of $5, revenue of $1,400) or “Levi’s Sawtooth Western Top”, for “Levi’s Shirt” (spend of $5.65, revenue of $369) showcase the ability of Broad Match to take a general keyword down to the highly-specific product level, for search terms that may not be covered by a standardized campaign that doesn’t have flawless granularity down to the SKU level. 

Broad Match also did well to understand other elements potentially not covered by the keywords themselves, like delivering for “Levi’s Ultra Wide Skateboarding Jeans” on a “Levi’s Jeans” keyword ($15 in spend, $190 in revenue).

Now all of this is a great summary of the changes we saw at a keyword and search term level. What’s important to a lot of marketers is macro level benefits from this new campaign structure. 

To further justify the positive benefits of this new strategy in our search campaigns, we can return to GEI as an indicator of success in scaling up our campaigns. From month 3 to month 5, we generated a GEI of .9 overall, confirming that we saw strong scalability of this strategy. 

To summarize, after 3 months we saw the following results, relative to total spend, and average aggregate CPC’s and ROAS.

Why Our Strategy Works

Broad match keywords have shown a distinct impact on campaign performance when compared to best practice campaigns. At an aggregate level, Broad match keywords tend to have a higher cost per click (CPC), averaging 70% more than their best practice counterparts. This increased expenditure often comes at the expense of overall return on ad spend (ROAS), which is typically 14% lower for Broad match campaigns. These findings highlight the need for careful consideration when integrating Broad match keywords into advertising strategies.

Moreover, Broad match keywords can significantly affect the allocation of budget across different keyword types. They can consume nearly as much budget as best practice campaigns, and if not properly managed, this can lead to a detrimental decrease in spend for more precise keyword types, particularly Phrase match keywords. Our data analysis revealed a substantial 35% drop in Phrase match spend when Broad match keywords were present in the same ad group, underscoring the importance of structuring campaigns to mitigate such impacts.

Given these insights, marketers must approach the use of Broad match keywords with caution. While they can offer wider reach, the associated costs and potential negative effects on other keyword strategies cannot be overlooked. Effective campaign management practices, such as the implementation of negative keywords and a balanced keyword mix, are essential to ensure that the benefits of Broad match do not come at the expense of overall campaign efficiency and effectiveness.

• Our unique structure of campaigns allows us to improve upon Google's hierarchy. 
  • Exact Match Keywords do Exact Match things, well enough that we don’t need Phrase or Broad match to deliver Exact match at all, they have their own priorities
  • Phrase Match Keywords need to be able to do Phrase match search terms to the best of their abilities, NOT being suffocated by Broad match infringement
  • Broad Match is at its best when we know they are using googles AI to deliver creative search terms through Phrase match delivery distinct from our best practice campaign
  • Broad Match Search Terms will not be prioritized by Broad match, regardless of what structure we use. They are the bottom rung on the ladder here. With our new structure, we can scale up their delivery over time through narrowing the scope of what Broad Match can steal from our best practice campaigns, forcing innovation. When we do this we saw a +93% increase in Broad match search term spending, and a GEI of .8 for Broad match vs when it was running without our new structure. 

What Broad Match Can Teach Us

Our new strategy resulted in significant spend growth of 93% over a short period of time when looking at the first three months of delivery pre implementation, vs the three months after our new structure. We also saw Revenue grow 35% in that same period of time. This is a huge improvement vs the results we saw when implementing the recommend Google best practice of Broad Match addition to existing campaigns. In that instance, we saw spend grow only 38%, and did not see any growth in Revenue. Revenue actually decreased slightly, by 18% as we saw broad match keywords continue to cannibalize phrase and exact counterparts. This data confirms our hypothesis we posited when first implementing this new structure, and now you can leverage these learnings and utilize it yourself!

Above all, even without explicit automations implemented in your campaigns, there are a number of things we can learn from this data in order to improve our management and iterative improvements within Google Ads campaigns. Utilizing tools such as spend segmentation as we did here will allow you to control the investment in new tactics. Our hierarchy will help you further understand the behavior of the different Google Keywords and Search Terms. Finally, you can utilize the GEI in order to track performance of tests vs prior period, incorporating scale into that calculation.

Want to see how Broad Match can strengthen your campaigns? We’d be happy to dig in and discuss strategies. Reach out to us using our contact form or connect with us on LinkedIn.