How To Run A/B Tests On Keyword Match Types: Stop Guessing And Start Proving What Works

You're three weeks into a Google Ads campaign that's burning through $3,000 per month. The clicks are coming in, the impressions look healthy, but something feels off. When you finally dig into the search terms report, you discover that half your budget is going to searches you never intended to target—all because you chose broad match instead of phrase match for your core keywords.

Sound familiar?

Here's the thing: most advertisers pick keyword match types based on gut feeling, inherited account settings, or what they read in a blog post five years ago. They set it and forget it, never questioning whether broad match actually outperforms phrase match for their specific business, or whether exact match might be leaving money on the table by limiting reach too aggressively.

The reality is that match type selection directly impacts three critical metrics: how many people see your ads, how relevant those people are to your business, and how much you pay to acquire each customer. Get it right, and you're fishing with the perfect net—catching exactly the prospects you want at a price that makes sense. Get it wrong, and you're either casting too wide (wasting budget on irrelevant clicks) or too narrow (missing valuable opportunities).

But here's what most Google Ads managers don't realize: you don't have to guess. Match type selection isn't an art—it's a science. With systematic A/B testing, you can replace assumptions with data and make match type decisions based on actual performance in your account, for your keywords, with your audience.

This guide walks you through the complete process of running statistically valid A/B tests on keyword match types. You'll learn how to identify which keywords deserve testing, design experiments that produce reliable results, implement tests without disrupting campaign performance, and interpret data to make confident scaling decisions. By the end, you'll have a repeatable framework for optimizing match types across your entire account—not based on best practices or industry benchmarks, but on real performance data from your own campaigns.

Let's eliminate the guesswork and build a testing system that turns match type optimization into a competitive advantage.

Step 1: Analyze Your Current Match Type Performance

Before you start testing anything, you need to understand what's actually happening in your account right now. Most advertisers skip this step and jump straight into experimentation, which is like trying to improve a recipe without tasting the original dish first.

Start by pulling performance data for the last 90 days. Why 90 days? Because you need enough data to identify patterns, but not so much that you're including outdated information from seasonal shifts or major campaign changes. If your account is newer or has lower volume, you might need to extend this to 180 days to get statistically meaningful data.

Here's what you're looking for in your baseline analysis:

First, segment your keywords by current match type. Create separate views for broad match, phrase match, and exact match keywords. For each segment, calculate these core metrics: total impressions, click-through rate (CTR), cost per click (CPC), conversion rate, and cost per acquisition (CPA). Don't just look at averages—look at the distribution. Are your broad match keywords consistently expensive, or do you have a few outliers skewing the data?

Second, examine your search terms report with a critical eye. This is where you'll discover the real story behind your match types. For each keyword, look at what actual searches triggered your ads. Are your phrase match keywords staying reasonably close to your intended meaning, or are they drifting into irrelevant territory? Are your exact match keywords missing obvious variations that could be valuable? When you how to choose keywords for testing, this search terms data becomes your most valuable input.

Third, identify your high-volume keywords—these are your testing candidates. You need keywords with enough traffic to reach statistical significance within a reasonable timeframe. As a general rule, look for keywords that generate at least 100 clicks per month. Below that threshold, your tests will take too long to produce reliable results.

Create a spreadsheet with these columns: Keyword, Current Match Type, Monthly Clicks, CTR, CPC, Conversions, Conversion Rate, CPA. Sort by monthly clicks descending. Your top 20-30 keywords are likely your best testing candidates, assuming they're actually important to your business goals.

Now here's the critical part that most guides skip: you need to understand why your current match types were chosen in the first place. Were they set deliberately based on strategy, or did someone just use broad match for everything because it was the default? Understanding the reasoning (or lack thereof) behind current settings helps you identify which keywords are most likely to benefit from testing.

Look for red flags in your data. Keywords with high impression volume but low CTR might be too broad. Keywords with great CTR and conversion rates but limited impression volume might be too restrictive. These discrepancies are your testing opportunities. The process of learning how to add negative keywords often reveals match type issues that need systematic testing.

Finally, document your current negative keyword strategy. Your negative keyword lists interact directly with match type performance. A broad match keyword with aggressive negative keyword filtering might actually perform more like a phrase match keyword. You need to understand this baseline before you start testing, or you'll be measuring the wrong thing.

By the end of this analysis phase, you should have a clear picture of your current match type landscape and a prioritized list of keywords that deserve testing. You should also have specific hypotheses about which match types might perform better for which keywords. These hypotheses will guide your test design in the next step.

Step 2: Design Your Match Type Test Structure

Now that you know which keywords to test, it's time to design experiments that will actually produce reliable, actionable results. This is where most A/B tests fail—not because of poor execution, but because of poor design from the start.

The fundamental principle of match type testing is isolation. You need to test one variable at a time while keeping everything else constant. That means same ad copy, same landing pages, same bid strategy, same audience targeting, same everything—except the match type itself.

Here's how to structure your test properly:

Create separate campaigns for each match type you're testing. Don't try to test match types within the same campaign or ad group. Why? Because Google's auction system doesn't treat keywords equally even within the same ad group. If you have both broad match and phrase match versions of the same keyword in one ad group, they'll compete with each other in unpredictable ways, contaminating your results.

Let's say you want to test broad match versus phrase match for the keyword "project management software." You'll create two campaigns: Campaign A with [project management software] in broad match, and Campaign B with "project management software" in phrase match. Everything else about these campaigns should be identical—same daily budget, same ad copy, same landing page, same bid amount.

Set equal budgets for each test campaign. This is crucial. If Campaign A gets $50/day and Campaign B gets $100/day, you're not running a fair test. The campaign with more budget will naturally generate more data, but that doesn't tell you which match type performs better—it just tells you that more money generates more results. When you how to find best keywords for your campaigns, equal budget allocation ensures fair testing conditions.

Determine your sample size requirements before you start. This is basic statistics, but it's shocking how many advertisers skip this step. You need to know how much data you need to collect before you can make a confident decision. Use a sample size calculator (there are free ones online) and input your current conversion rate and the minimum difference you want to detect. For most Google Ads tests, you'll need at least 100 conversions per variation to reach statistical significance.

Here's a practical example: If your current conversion rate is 3% and you want to detect a 20% improvement (from 3% to 3.6%), you'll need approximately 3,300 clicks per variation. At 100 clicks per day, that's 33 days of testing. If you can't wait that long, you either need to increase your budget or choose higher-volume keywords to test.

Set a predetermined test duration. Don't fall into the trap of "let's just run it for a week and see what happens." A week might not be enough time to account for day-of-week variations in performance. A good rule of thumb is to run tests for at least two full weeks (to capture two complete weekly cycles) or until you reach your required sample size, whichever comes first. Understanding how to find negative keywords during your test period helps maintain clean data by excluding irrelevant traffic.

Define your success metrics upfront. What does "better" mean for your business? Lower CPA? Higher conversion rate? More total conversions even if CPA is slightly higher? Different businesses have different goals, and you need to define yours before you start testing. Write down your primary success metric and your secondary metrics. For example: Primary metric = CPA below $50. Secondary metrics = conversion rate above 3%, CTR above 2%.

Plan for statistical significance testing. Don't just eyeball the results and declare a winner. Use proper statistical tests (chi-square test for conversion rates, t-test for continuous metrics like CPA) to determine whether observed differences are real or just random variation. Most A/B testing calculators will do this for you—just input your data and they'll tell you if the difference is statistically significant.

Document everything. Create a test plan document that includes: test hypothesis, keywords being tested, match types being compared, campaign structure, budget allocation, success metrics, required sample size, planned duration, and decision criteria. This documentation serves two purposes: it keeps you honest (no changing the rules mid-test), and it creates a record you can reference for future tests.

One more critical consideration: negative keyword coordination. If you're testing broad match versus phrase match, you need to ensure that both campaigns use the same negative keyword lists. Otherwise, you're not testing match types—you're testing match types plus different negative keyword strategies, which confounds your results.

By the end of this design phase, you should have a complete test plan that specifies exactly what you're testing, how you're testing it, how long you'll test it, and how you'll determine the winner. This upfront investment in proper test design is what separates meaningful experiments from random data collection.

Step 3: Implement and Monitor Your A/B Test

You've analyzed your baseline performance and designed a solid test structure. Now comes the execution phase—where careful implementation and vigilant monitoring determine whether your test produces reliable results or garbage data.

Start by creating your test campaigns in Google Ads. Follow your test plan exactly. Create Campaign A with your first match type variation, then duplicate it to create Campaign B with your second match type variation. Change only the match type—everything else should be identical.

Here's a critical implementation detail that trips up many advertisers: make sure your campaigns aren't competing with each other for the same searches. If you're testing broad match versus phrase match, add the phrase match keyword as a negative keyword to your broad match campaign. This prevents the broad match campaign from showing ads for searches that exactly match your phrase match keyword, which would contaminate your test by having both campaigns compete for the same traffic.

For example, if you're testing [project management software] in broad match versus "project management software" in phrase match, add "project management software" as a negative phrase match keyword to your broad match campaign. This ensures that searches for "project management software" and close variations only trigger your phrase match campaign, while other related searches trigger your broad match campaign. When you how to find profitable keywords through testing, this separation ensures clean data collection.

Set your campaigns live simultaneously. Don't launch Campaign A on Monday and Campaign B on Wednesday. Launch them at the same time so they're exposed to the same market conditions, day-of-week effects, and competitive landscape.

Now begins the monitoring phase. Check your campaigns daily, but resist the urge to make changes. Your job during the test period is to watch, not to optimize. You're collecting data, not managing performance.

Here's what to monitor each day:

First, verify that both campaigns are spending their budgets evenly. If Campaign A is spending $100/day while Campaign B is only spending $40/day, something's wrong. This usually indicates a bid issue (one campaign's bids are too low to compete) or a match type issue (one match type isn't generating enough eligible impressions). If you see significant spending discrepancies, you may need to adjust bids to ensure both campaigns get equal exposure.

Second, watch for data quality issues. Check your search terms reports daily to ensure you're not wasting budget on completely irrelevant searches. If your broad match campaign is showing ads for searches that have nothing to do with your product, add those terms as negative keywords. But—and this is important—add the same negative keywords to both campaigns. You're not trying to optimize one campaign better than the other; you're trying to create equal conditions for a fair test.

Third, monitor for external factors that might invalidate your test. Did a competitor launch a major promotion that's affecting your auction dynamics? Did you make changes to your landing page? Did your website go down for two hours? These external factors can skew your results. Document them in your test log so you can account for them when analyzing results. Understanding how to calculate cost per acquisition accurately during testing requires accounting for these external variables.

Fourth, track your progress toward statistical significance. Most A/B testing calculators let you input your current results to see if you've reached significance yet. Check this every few days. If you're not making progress toward your required sample size, you might need to increase budgets or extend your test duration.

Here's what NOT to do during the test period: Don't pause campaigns because one is performing worse. Don't change bids to "help" the underperforming campaign. Don't modify ad copy. Don't adjust budgets unevenly. Don't add new keywords. Every change you make introduces a new variable that confounds your results. The whole point of A/B testing is to isolate one variable (match type) while keeping everything else constant.

The only acceptable changes during testing are: adding negative keywords (to both campaigns equally), fixing technical issues (broken tracking, disapproved ads), and adjusting bids proportionally (if both campaigns need bid increases to maintain impression share).

Set calendar reminders to check your test progress. At the one-week mark, do a preliminary analysis to ensure everything is working correctly. At the two-week mark, check if you've reached statistical significance. If not, decide whether to extend the test or increase budgets to accelerate data collection.

Keep a test log. Every day, record key metrics: impressions, clicks, conversions, cost, CTR, conversion rate, and CPA for each campaign. This daily log serves two purposes: it helps you spot trends and anomalies quickly, and it gives you a complete data record if you need to troubleshoot issues later.

One common question: what if one campaign is clearly losing after just a few days? Should you stop the test early? Generally, no. Early results are often misleading due to small sample sizes and random variation. Stick to your predetermined test duration unless you have a compelling reason to stop (like one campaign is literally wasting thousands of dollars on completely irrelevant traffic that you can't control with negative keywords).

By the end of your test period, you should have clean, reliable data from both campaigns, collected under equal conditions, with proper documentation of any external factors that might have affected results. This disciplined approach to implementation and monitoring is what separates valid A/B tests from wishful thinking.

Step 4: Analyze Results and Scale Your Winners

Your test has run for the predetermined duration, you've collected sufficient data, and now comes the moment of truth: analyzing results and making scaling decisions. This is where many advertisers stumble, either by declaring winners prematurely or by overcomplicating the analysis.

Start with statistical significance testing. Don't just compare the numbers and pick the one that looks better. Use a proper statistical test to determine whether the observed difference is real or just random variation. For conversion rate comparisons, use a chi-square test or proportion test. For CPA comparisons, use a t-test. Most online A/B testing calculators will do this for you—just input your data (conversions and clicks for each variation) and they'll tell you if the difference is statistically significant.

Here's what statistical significance means in practical terms: If your test shows that phrase match has a 3.5% conversion rate versus broad match's 2.8% conversion rate, and the test says this difference is statistically significant (typically at 95% confidence), it means there's only a 5% chance that this difference occurred by random luck. You can be reasonably confident that phrase match actually performs better.

But statistical significance isn't the whole story. You also need practical significance. Maybe phrase match has a statistically significantly higher conversion rate, but it generates 80% fewer impressions than broad match. Is that trade-off worth it for your business? This is where you need to consider your broader campaign goals.

Analyze your results across multiple dimensions:

First, look at efficiency metrics. Which match type delivered lower CPA? Higher conversion rate? Better return on ad spend (ROAS)? These metrics tell you which match type is more cost-effective.

Second, look at volume metrics. Which match type generated more total conversions? More impressions? More clicks? These metrics tell you which match type has more scale potential.

Third, look at quality metrics. Check your search terms reports one more time. Which match type attracted more relevant searches? Which one required more aggressive negative keyword management? Quality matters as much as quantity.

Now, make your scaling decision based on your business priorities. Here are the common scenarios:

Scenario 1: Clear winner on all metrics. One match type has better CPA, better conversion rate, and generates more total conversions. This is easy—scale the winner, pause the loser, and move on to testing your next keyword.

Scenario 2: Efficiency versus volume trade-off. One match type has better CPA but lower volume. This requires a business decision. If you're budget-constrained and need maximum efficiency, choose the lower-CPA option. If you're trying to grow and can afford slightly higher CPA for more volume, choose the higher-volume option. When you understand the principles behind negative keywords example guide 2025, you can often improve broad match efficiency while maintaining volume.

Scenario 3: No statistically significant difference. The match types performed roughly the same. In this case, default to the match type that gives you more control (usually phrase match or exact match) or the one that requires less ongoing management.

Scenario 4: Inconclusive results. You didn't collect enough data to reach statistical significance. You have two options: extend the test with higher budgets, or make a judgment call based on directional data and your risk tolerance.

Once you've identified your winner, here's how to scale it properly:

Don't just pause the losing campaign and 10x the winning campaign's budget overnight. Scale gradually. Increase the winning campaign's budget by 20-30% and monitor performance for a few days. If performance holds steady, increase another 20-30%. Rapid budget increases can destabilize campaign performance as Google's algorithm adjusts to the new budget level.

Expand your winner to similar keywords. If phrase match won for "project management software," test phrase match for related keywords like "project tracking tools" or "team collaboration software." Your test results often generalize to similar keywords in the same semantic cluster. Learning how can i use match types to improve brand protection helps you apply winning match type strategies across your brand terms.

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