The Paid Search Podcast: How Exact Match Keywords Actually Function

Episode 502 • March 2, 2026
Host: Chris Schaeffer, Certified Google Ads Specialist

Episode Overview

In this eye-opening episode, Chris Schaeffer breaks down how "exact match" keywords in Google Ads have evolved from their traditional, highly targeted roots to a far more intent-based, machine learning-driven system. Chris aims to demystify what advertisers are really getting when they use exact match keywords today, sharing real-world campaign examples and candid insights. The episode is particularly relevant for advertisers who rely on precision targeting and those working in niche industries that require tight control over their PPC campaigns.

Key Discussion Points & Insights

1. Google’s Definition of Exact Match Keywords (01:06–06:25)

• Traditional Expectation: Advertisers expect “exact match” to mean only highly specific, literal queries trigger their ads.
• Google’s Evolution of Exact Match:
  • Acceptable variants now include:
    • Plurals/singulars (e.g., “red running shoes” vs. “red running shoe”)
    • Minor word reordering (“running shoes red”)
    • Close variants (“red runners”), which often lack critical original keyword terms
    • Implied meaning (“buy red running shoes”)
  • Google uses signals like user search context, past searches, and increasingly “new machine learning models” to evaluate search intent.

Notable Quote:

“Exact match, in essence, is now intent-based, not word-based, not character-based.”
— Chris Schaeffer, 06:10

2. Real-World Examples Exposing the Gap (12:35–31:03)

Chris shares four client campaign examples, highlighting the dramatic overreach of “close variants" and how frustratingly broad Google’s "intent" interpretation can be.

Example 1: “Headshot Near Me” (12:40–15:22)

• Expectation: Show only for “headshot near me”.
• Reality: Matched to a wide array of terms, including:
  • “Professional headshots near me”
  • “Headshot photographer near me”
  • “Business headshots near me”
  • “Photographer headshots near me”
• Analysis: Most terms qualify only as very broad “close variants,” undermining advertiser control.

“Close variance is doing a major stretch here … Google’s forcing me to show on hundreds and hundreds of other things.”
— Chris Schaeffer, 15:10

Example 2: “Listing Agents Near Me” (Real Estate) (16:12–18:45)

• Expectation: Target people wanting to list and sell their homes.
• Reality: Searches included:
  • “Real estate offices near me”
  • “Find a Realtor”
  • “Finding a real estate agent”
  • “How to find a listing agent” (the only truly relevant one)
• Issue: Most queries target buyer agents or general real estate, not the specific listing intent.
• Point: Industries with distinct subcategories (like real estate) suffer the most from poor match precision.

Example 3: “Rent Car for Uber” (Transportation) (19:00–22:45)

• Expectation: Attract drivers seeking to rent vehicles specifically for Uber work (B2B transaction).
• Reality: Matched to terms like:
  • “Uber rental cars”
  • “Uber car rental near me”
  • “Uber rentals”
• Problem: Fails to distinguish between users wanting a ride and those wanting to drive for Uber.
• Commentary: Nuanced intent lost; most matches are too generic.

Example 4: “In Home Nurse Care” (Senior Services) (22:46–25:19)

• Expectation: Target families seeking medical care provided in-home.
• Reality: Matches include:
  • “Home health care”
  • “Home health aides”
  • “Home health aid agency”
• Critique: “In home” as a critical qualifier is often missing; matches are too loose to be reliably profitable.

3. The Underlying Cause: Machine Learning and Close Variants (31:04–36:40)

• Chris fingers the new machine learning models as the main culprit pushing matches beyond reasonable “close variant” territory.
• As Google’s systems try to interpret both searcher and advertiser intent, precision is lost, especially in niches or “gray areas” between commonly understood industries.

Notable Quote:

“The less Google understands your keywords or what it is that you're looking for, the worse the matching of the search terms.”
— Chris Schaeffer, 31:27

4. Consequences for Advertisers (36:41–45:02)

• Loss of Control: Advertisers can no longer rely on deep Google Ads expertise or careful keyword research for optimal results—success now rests on Google’s understanding of intent.
• Wasted Spend: Advertisers waste money sifting through irrelevant close variants before extracting value.
• Increased Costs: Exact match keywords now have a higher cost-per-click (CPC) threshold, yet deliver less targeted traffic.
• Marginalization of Niche Industries: Real estate, home services, and other verticals with nuanced needs are especially at risk for becoming unviable on Google Ads.

“If Google doesn't understand what you want... your account may never succeed. Not because you're not good at Google Ads... but because Google doesn't understand what you want and it doesn't think that that traffic exists...”
— Chris Schaeffer, 43:45

5. The Future of Exact Match & Final Reflections (45:03–end)

• Chris wishes for an “old-school,” precise Google Ads mode but acknowledges it may never return.
• Advertisers should realistically assess Google’s capacity to deliver the intent they desire, and consider moving spend away from Google Ads where needed.
• Some industries may still thrive—success now simply depends on how well Google understands both the advertiser’s intent and the customer journey.

“There's nothing like Google Ads advertising. It is the only place that you can find in-the-moment traffic that is ready to purchase, buy, call, whatever in that exact moment. But... we can no longer replicate the traffic that we're looking for to the precision that we need.”
— Chris Schaeffer, 46:00

Memorable Moments & Notable Quotes

• “Exact match is now intent based, not word based, not character based.” (06:10)
• “Close variance is doing a major stretch here because this, these searches are... just not what I want.” (15:10)
• “The less Google understands your keywords... the worse the matching of the search terms.” (31:27)
• “Your success now depends on Google’s understanding of that traffic that you want.” (43:16)
• “Some industries absolutely may work... [but] if Google doesn't know it, then your account may never succeed.” (43:45)

Important Timestamps

| Segment | Topic | |---|---| | 00:00–06:25 | Introduction & Google’s Definition of Exact Match | | 12:35–15:22 | Example 1: Headshot Near Me | | 16:12–18:45 | Example 2: Listing Agents Near Me | | 19:00–22:45 | Example 3: Rent Car for Uber | | 22:46–25:19 | Example 4: In Home Nurse Care | | 31:04–36:40 | Machine Learning & Close Variants | | 36:41–45:02 | Consequences for Advertisers & Niche Industries | | 45:03–end | Final Thoughts, Industry Outlook |

Conclusion

Chris delivers a clear, at times blunt assessment: for many advertisers, especially those in nuanced or niche markets, exact match keywords no longer offer the precise targeting they once did. Instead, Google’s intent-driven approach, fueled by machine learning, provides results that often drift wide of the mark—leading to higher costs and more wasted spend. While some sectors may still gain value from current Google Ads campaigns, others may need to reconsider their approach. For businesses and PPC managers relying on dialed-in control, this new reality should inform their strategies, budgets, and campaign expectations going forward.