It feels like case study season at Google. My feeds are flooded with PDFs and carousels from massive brands touting incredible results from the latest Google Ads features. We’re seeing huge numbers from names like Nissan and L’Oreal, and with Q4 on the horizon, it’s designed to make you think you’re missing out.
I recently sat down with former Googler Jyll Saskin Gales to cut through the marketing noise and analyze what these case studies can actually teach the average advertiser. Because when I see a case study from a multi-billion dollar corporation, my first question is always: What am I supposed to do with this?
Before we dive in, let’s be very clear: we aren’t doubting the numbers. Google has multiple layers of verification to ensure the data is correct. The numbers are real. The problem is that the story they tell is almost always incomplete.
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The Nissan Broad Match Case Study: A 272% Increase in Leads?
The first case study we looked at was from Nissan, which claimed a staggering 272% increase in leads simply by using broad match. My immediate reaction, and Jyll’s, was skepticism. For an existing advertiser of this scale to 3-4x their leads from a single keyword targeting change, there has to be more to the story.
What Google would like you to believe is that flipping the broad match switch magically unlocked a torrent of new, high-quality leads. But after running countless tests on well-managed accounts, I have never seen anything remotely close to these numbers. We use broad match in many accounts, but the results are incremental, not explosive.
What’s Really Going On Here?
The issue with a headline number like this is that it’s completely devoid of context. The comparison data is where things get interesting (and often, misleading). When you see a number this big, you have to ask a few questions:
- What was the baseline? A 272% increase is easy if your starting point was terrible. Were they moving from manual bidding to Smart Bidding for the first time? That alone could account for a huge lift.
- Did the definition of a “lead” change? Maybe they suddenly started tracking store visits as a lead conversion. This is a classic way to inflate lead volume without actually generating more sales.
- What was the time period? Are we comparing one week this year to one week last year? Day over day? Month over month? These choices can dramatically skew the results.
- Were they advertising on their brand before? It sounds basic, but you’d be surprised. Opening up to brand terms via broad match when you previously weren’t could easily create a massive (but misleading) spike in “leads.”
The takeaway here is simple: be extremely careful with single-metric case studies. Should you test broad match? Maybe. But don’t expect a 272% increase in leads. You’ll be lucky to get 10%.
The L’Oreal AIMax Case Study: Impressive Numbers, but for Who?
Next up was a case study from L’Oreal, which went all-in on AIMax (Google’s AI-powered features for Search campaigns) and reported some eye-popping results:
- 67% higher click-through rate
- 2x higher conversion rate
- 31% lower CPA
Unlike the Nissan example, Google gave us a lot more data here, which is a welcome change. These numbers would make almost any advertiser want to jump on the AIMax bandwagon. However, my own experience with AIMax has been underwhelming. Most of what I’ve seen is cannibalization of traffic that other campaigns would have captured anyway.
Digging Deeper into the “Lift”
The case study goes on to say that “the campaign” saw a 27% lift in conversion value. The key word here is “campaign.” This wasn’t a business-wide lift for all of L’Oreal; it seems to be isolated to a specific campaign. While a 27% lift is good, it’s a far cry from the “2x conversion rate” headline.
The study also notes that AIMax helped them capture searches like “best cream for facial spots,” which they supposedly weren’t hitting before. This is the part that makes you scratch your head. For a brand the size of L’Oreal, with a sophisticated search team, how were they not already targeting such an obvious, high-intent keyword with their existing broad, phrase, and exact match setup?
The most likely explanation is that this is a tool for a very specific type of advertiser: one who is truly maxed out on opportunity at a global scale. For a company like L’Oreal, the ability of AIMax to dynamically customize ad text and match landing pages to niche queries across millions of impressions could provide an incremental lift. For most businesses, this is a solution to a problem you don’t have.
Beyond Case Studies: Deconstructing Google’s Holiday Guide
It’s not just the big brand case studies; Google’s general “best practice” advice, like what’s found in their holiday guide, also needs to be viewed with a critical eye. The advice isn’t necessarily bad, but it often lacks actionable substance.
The Search + YouTube Combination
Google is pushing the Search + YouTube combination hard, citing stats that they are present in over 86% of purchase journeys with five or more touchpoints. This is true. YouTube remains one of the most underutilized advertising platforms, and it’s a powerful tool for building consideration.
The real story here is Google’s fight against Amazon, where a huge number of purchase journeys now begin. Google wants you to know that even if someone is searching on Amazon, they are still using Google and YouTube for research and reviews.
The actionable insight isn’t just to “use YouTube.” It’s to start early. YouTube campaigns, especially Demand Gen, need time. From what we see in our accounts, the algorithm takes at least a month to really figure out who to target. If you wait until November to launch your holiday YouTube campaigns, you’re already too late.
The “Always-On” Myth vs. Reality
The guide is full of advice like “run always-on campaigns” and “ensure discoverability.” This is just evergreen jargon. Of course you should be discoverable.
The practical reality is that upper-funnel campaigns require a longer time horizon to prove their value. People need time to act, and the algorithm needs time to learn from those actions. I’ve seen Performance Max campaigns with a ROAS below 1 for two straight months, only to hit a 3 ROAS in the third month. Most advertisers would have turned it off after three weeks, concluding “it didn’t work.”
If you’re going to invest in these channels, you have to commit to giving them enough time (and budget) to learn. Otherwise, you’re just wasting money.
So, What Should You Do With Google’s Recommendations?
After going through all this, the conclusion is clear: these polished, big-brand case studies are not very helpful. They lack the context needed to be actionable.
I know Google Ads works. I know Smart Bidding works. I even know PMax can work in certain situations. But throwing out a huge number without explaining the “why” and “how” makes it impossible for other advertisers to know when and how to apply these lessons.
If you’re looking for resources from Google, my advice is to skip the marketing fluff and go straight to the source: the Google Ads Help Center. The official documentation is dense and dry, but it’s the truth. It will be immensely more helpful to you than reading a case study. And if it’s too dense, that’s where practitioners come in—to translate that documentation into a strategy that actually works for your business.
[TL;DR]
- Be skeptical of big numbers. A huge lift like Nissan’s 272% increase in leads is almost always due to a flawed baseline or a change in measurement, not just a simple feature toggle.
- Context is everything. The L’Oreal AIMax case study shows that a feature might work for a massive, maxed-out advertiser, but that doesn’t make it relevant for most businesses.
- Actionable advice is specific. Google’s generic advice to “be always-on” is less useful than the practical insight that upper-funnel campaigns (like YouTube) need at least a month of data before you can properly evaluate them.
- Read the documentation, not the marketing. The most reliable source of information from Google is the official Help Center documentation, not the polished case studies designed to sell you on new features.
