How to Optimize Match Types Using Search Terms Report: A Step-by-Step Guide

The search terms report shows you exactly what people typed before clicking your ads—and it's the key to making smarter match type decisions. By analyzing this data, you can tighten broad match keywords that are bleeding budget, expand exact match where you're missing opportunities, and find the sweet spot with phrase match for scalable targeting.

This guide walks you through the complete process of using your search terms report to optimize match types, reduce wasted spend, and improve campaign performance. Whether you're managing a single account or juggling dozens of clients, these steps will help you make data-driven match type decisions instead of guessing.

Here's what we'll cover: pulling the right data from your search terms report, identifying performance patterns by match type, segmenting queries by intent quality, adjusting your match type strategy based on real behavior, building negative keyword lists, and setting up a weekly review process that keeps your campaigns sharp.

Step 1: Pull Your Search Terms Report with the Right Date Range

First things first—you need to access the actual search terms report. In Google Ads, navigate to Insights & Reports in the left sidebar, then click Search terms. This report shows every query that triggered your ads and resulted in at least one impression.

The date range matters more than most people realize. Too short and you're making decisions on incomplete data. Too long and you're looking at outdated behavior that no longer reflects current performance.

For most accounts, 30 days provides a solid baseline. If you're working with lower-volume campaigns—maybe a local service business spending a few hundred dollars monthly—extend it to 60 or 90 days to capture enough conversions for meaningful analysis. High-spend accounts with thousands of clicks weekly can work with shorter windows and still have statistical significance.

What usually happens here is advertisers pull the report, get overwhelmed by the volume, and either give up or make random changes. Don't do that.

Instead, use the campaign and ad group filters at the top of the report to narrow your focus. Start with your highest-spend campaign—the one where optimization will have the biggest impact. You can always come back for the others once you've built the habit.

Now you have a choice: export to spreadsheet or work directly in the interface. The spreadsheet route gives you more flexibility for sorting and analyzing patterns, especially if you're managing multiple accounts and want to batch your work. The in-interface approach is faster for immediate action—you can add negatives, create new keywords, and adjust bids without switching tabs.

In most accounts I audit, the search terms report hasn't been checked in weeks or months. That's leaving money on the table. Pull this report with the right date range, and you're already ahead of most advertisers.

Step 2: Identify Match Type Performance Patterns

Now that you're looking at search terms data, it's time to spot the patterns that reveal what's actually working and what's burning budget.

Start by sorting the report by different metrics. Click the "Cost" column header to sort by spend—this shows you where your budget is going. Then sort by "Conversions" to see which queries are actually driving results. Finally, check "Conv. rate" to identify efficiency gaps.

Here's what you're looking for: broad match keywords with high spend but low conversion rates. These are the budget bleeders. You'll typically see search terms that are tangentially related to your keyword but miss the mark on intent.

Let's say you're bidding on "PPC management software" as a broad match keyword. You might find search terms like "free PPC tools," "PPC management jobs," or "what is PPC management"—all triggered by your broad match keyword, but none representing buying intent.

On the flip side, look for exact match terms with strong performance that could handle more volume. These are your winners. If you have an exact match keyword generating conversions at a profitable cost per acquisition, but it's only getting 50 clicks per month, there's room to scale. You might add phrase match variations or increase bids to capture more of that high-intent traffic.

Phrase match sits in the middle, and that's where things get interesting. You'll find phrase match terms that are either too restrictive—missing obvious variations that could work—or too loose, triggering irrelevant queries that should have been caught by negatives. Understanding how match types affect search term targeting helps you make smarter decisions here.

In most accounts I review, the mistake most agencies make is treating all match types the same. They bid the same amount on broad, phrase, and exact match versions of the same keyword. That's lazy. The data in your search terms report will show you that broad match needs lower bids and tighter negative keyword lists, while exact match can often handle higher bids because you're controlling exactly what triggers the ad.

Create a simple mental framework as you review: Which match types are giving me control and efficiency? Which ones are giving me reach but wasting money? The answers are right there in the search terms report.

Step 3: Segment Search Terms by Intent Quality

Not all search terms are created equal, and lumping them together leads to bad optimization decisions. You need to segment queries by intent quality before you start making match type changes.

Create three buckets: high-intent converters, mid-funnel researchers, and irrelevant traffic.

High-intent converters: These are search terms that include buying signals—words like "buy," "pricing," "best," "top rated," or specific product names. When someone searches "best PPC optimization tool for agencies," they're much closer to a purchase decision than someone searching "what is PPC optimization." These terms deserve exact match treatment with dedicated bids. You want tight control over when you show up and how much you're willing to pay.

Mid-funnel researchers: These queries show interest but not immediate buying intent. Think "PPC optimization strategies," "how to improve Google Ads performance," or "PPC tools comparison." These searchers are educating themselves, building consideration sets, and might convert later in their journey. Phrase match works beautifully here—you get balanced reach without the wild unpredictability of broad match.

Irrelevant traffic: This is where broad match shows you exactly where it's going off the rails. You'll find job-related searches ("PPC manager jobs," "PPC specialist salary"), DIY queries when you're selling a service ("how to optimize Google Ads yourself"), competitor brand names (depending on your strategy), and completely unrelated terms that somehow matched your keywords. Learning how to identify low intent search terms is critical for this step.

What usually happens here is advertisers see irrelevant terms and just add them as negatives without asking why they appeared in the first place. That's treating the symptom, not the disease.

If you're seeing a lot of job-related queries, it means your broad match keywords are too loose or you're missing obvious negative keywords like "job," "jobs," "career," "salary," and "hiring." If you're seeing DIY queries, you need negatives like "free," "DIY," "how to," and "tutorial."

The intent segmentation exercise reveals not just what to do with individual search terms, but where your overall match type strategy needs adjustment. If 60% of your search terms fall into the irrelevant bucket, your broad match keywords are too aggressive. If 90% are high-intent but you're barely getting any volume, your exact match strategy is too restrictive.

Document these patterns as you find them. Over time, you'll develop an intuition for which match types work best for different types of keywords in your specific accounts.

Step 4: Adjust Your Match Type Strategy Based on Data

Now comes the action phase—taking what you've learned from the search terms report and actually changing your match type strategy to improve performance.

Promote top-performing search terms to exact match with dedicated bids. When you find a search term that's converting well, don't just let it continue triggering through a broad or phrase match keyword. Create a new exact match keyword for that specific query. This gives you granular control over bids and lets you allocate more budget to what's proven to work.

For example, if "PPC optimization chrome extension" is showing up in your search terms report with a 12% conversion rate and $15 cost per acquisition, create an exact match keyword [PPC optimization chrome extension] in its own ad group. Now you can bid more aggressively on that specific term without inflating bids on less valuable variations.

Tighten underperforming broad match keywords to phrase match. If a broad match keyword is generating too much irrelevant traffic even after adding negatives, convert it to phrase match. You'll lose some reach, but you'll gain relevance and efficiency. The phrase match version will still capture close variations and related queries, just with tighter guardrails. Understanding how phrase match and exact match differ helps you make this transition effectively.

In most accounts I audit, broad match keywords that have been running for months without negative keyword maintenance are the biggest budget drains. Tightening them to phrase match immediately cuts waste while you build out your negative keyword lists.

Add negative keywords for irrelevant search terms to stop budget bleed. This is the obvious one, but it needs to be systematic, not random. When you see an irrelevant search term, ask yourself: Is this a one-off weird query, or does it represent a pattern? If it's a pattern, use phrase or broad match negatives to block entire categories of irrelevant traffic. If it's a specific bad query, use exact match negatives.

For instance, if you keep seeing job-related searches, don't just add each individual query as a negative. Add "job," "jobs," "career," and "salary" as broad match negatives at the campaign level. This blocks thousands of potential irrelevant queries with just four negatives.

Consider match type isolation—separate ad groups for each match type of the same keyword. This is an advanced tactic, but it's incredibly powerful for accounts that need clean data and precise bid control.

Instead of having one ad group with "PPC optimization tool" in broad, phrase, and exact match all mixed together, create three separate ad groups: one for [PPC optimization tool] exact match, one for "PPC optimization tool" phrase match, and one for PPC optimization tool broad match.

This structure lets you bid differently on each match type, write ad copy tailored to the intent level, and analyze performance without match types cannibalizing each other. The exact match ad group gets your highest bids because you know exactly what you're buying. The phrase match ad group gets moderate bids for balanced reach. The broad match ad group gets lower bids while you test its ability to find new high-intent queries.

The mistake most agencies make is implementing match type isolation across their entire account at once. Don't do that. Start with your top 5-10 keywords by spend, isolate their match types, and see how it performs over 30 days. If it works, roll it out further.

Step 5: Build Your Negative Keyword List from Search Term Insights

Your search terms report is a goldmine for building negative keyword lists that protect your budget from irrelevant clicks. The key is identifying patterns, not just blocking individual bad queries.

Start by looking for recurring themes in irrelevant search terms. Common patterns include job seekers searching for employment opportunities, people looking for free alternatives when you're selling a paid product, DIY queries when you're offering a done-for-you service, wrong geographic intent for local businesses, and competitor brand names (though this depends on your strategy).

Let's say you're running ads for a PPC management agency. You might see search terms like "PPC manager jobs near me," "PPC specialist salary," "remote PPC jobs," and "PPC career path." These all represent the same pattern—job seekers, not potential clients.

Instead of adding each individual query as a negative, identify the root words causing the problem: "job," "jobs," "career," "salary," "hiring," "resume," "employment." Add these as broad match negatives at the campaign level, and you've blocked thousands of irrelevant future queries with a handful of negatives. Learning how to research negative keywords systematically makes this process much faster.

Here's where understanding negative keyword match types becomes critical. Unlike regular keywords, negative keyword match types work inversely—broad negative blocks more than exact negative.

An exact match negative like [free PPC tools] only blocks that specific query. A phrase match negative like "free PPC tools" blocks any query containing that exact phrase in that order. A broad match negative like free PPC tools blocks any query containing all those words in any order, including variations.

What usually happens here is advertisers use exact match negatives for everything, then wonder why irrelevant traffic keeps appearing. Use exact match negatives only for specific bad queries you want to block without affecting anything else. Use phrase and broad match negatives for pattern blocking. For a deeper dive, check out how negative keyword match types work.

Add negatives at the appropriate level—campaign versus ad group. If a term is irrelevant to your entire business, add it at the campaign level or create a master negative keyword list that applies account-wide. If a term is only irrelevant to a specific ad group but might be valuable elsewhere, add it at the ad group level.

For example, "free" might be a campaign-level negative if you only sell paid products. But if you have one campaign offering a free trial, you'd want "free" as an ad group-level negative in your paid product campaigns while allowing it in your free trial campaign.

Create a master negative keyword list for account-wide protection. In Google Ads, go to Tools & Settings > Shared library > Negative keyword lists. Create a list called something like "Universal Negatives" and populate it with terms that are never relevant: profanity, obviously unrelated industries, job-related terms, etc. Apply this list to all campaigns as your baseline protection.

Then create campaign-specific negative lists for patterns unique to each campaign. Your brand campaign might block competitor names. Your service campaign might block DIY terms. Your local campaign might block other cities.

In most accounts I review, negative keyword lists are either non-existent or completely outdated. Building them from actual search term data—not guesses—is what separates efficient accounts from wasteful ones.

Step 6: Monitor and Iterate Weekly

Optimizing match types isn't a one-time project—it's an ongoing process. The advertisers who check their search terms report regularly and act on what they find consistently outperform those who set and forget.

Set a recurring schedule to review search terms. For high-spend accounts—anything over $5,000 monthly—review weekly. For mid-spend accounts, bi-weekly works. For low-spend accounts, monthly is sufficient. Put it on your calendar like any other important meeting.

During each review, look for the same patterns we've covered: new irrelevant queries that need negatives, high-performing search terms that deserve exact match promotion, and match type performance trends that suggest strategic adjustments. Following Google Ads search terms best practices keeps your reviews focused and productive.

Track match type performance over time to validate your changes. Create a simple spreadsheet or use Google Ads' built-in reporting to monitor metrics by match type: impressions, clicks, conversions, cost per conversion, and conversion rate. This historical data shows whether your match type optimizations are actually improving performance or just changing it.

What usually happens here is advertisers make changes but never check if those changes worked. You tighten a broad match keyword to phrase match—did that improve efficiency or just kill volume? You promoted a search term to exact match—did that maintain the same conversion rate at higher volume? The only way to know is tracking over time. Understanding how to refine match types over time gives you a framework for continuous improvement.

Watch for new irrelevant queries as broad match continues to learn. Google's machine learning is constantly exploring new query territory, especially with broad match keywords. What was clean last month might be bleeding budget this month. Your weekly review catches these drift patterns before they cost you serious money.

Document your optimizations to build institutional knowledge. This is especially important for agencies managing multiple clients or teams with multiple people touching the same accounts. Keep a simple log: date, change made, reasoning, and expected outcome. This prevents you from undoing good work or repeating mistakes.

For example: "March 15, 2026 - Converted 'PPC software' from broad to phrase match. Reason: 40% of search terms were job-related or DIY queries. Expected: 30% reduction in cost, minimal impact on conversions." Then check back in 30 days to see if your hypothesis was correct.

The search terms report isn't just a diagnostic tool—it's your roadmap for smarter match type decisions. The data is already there, waiting for you to act on it.

Putting It All Together

Here's your quick checklist for optimizing match types using the search terms report: Pull 30-90 days of search term data from your highest-spend campaigns. Analyze performance by match type, sorting by cost, conversions, and conversion rate to spot patterns. Segment terms by intent quality into high-intent converters, mid-funnel researchers, and irrelevant traffic.

Promote winners to exact match with dedicated bids for maximum control. Tighten losers from broad match to phrase match to reduce waste while maintaining some reach. Build negative keyword lists from irrelevant query patterns, using broad match negatives for categories and exact match negatives for specific bad queries.

Review weekly and iterate based on what the data shows. Track match type performance over time to validate your optimization decisions. Document changes so you build institutional knowledge instead of making the same mistakes repeatedly.

Start with one campaign, get comfortable with the process, and scale from there. You don't need to overhaul your entire account in one session. Pick your highest-spend campaign, spend 30 minutes in the search terms report, make a few strategic changes, and watch what happens over the next week.

The accounts that win aren't necessarily the ones with the biggest budgets or the fanciest strategies—they're the ones that consistently check the search terms report and act on what they find. This simple habit compounds over time into significantly better performance.

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