How Google Analytics Helps Your Advertising (And You)
For some advertisers, Google Analytics is that place you're forced to visit when you need landing page data. For others, it is a playground for insight and data activation. Let's go through some use cases.
Date
29 Oct 2025
Topic
Google Ads
Reading time
5 Minutes
Media Optimisation
Why does it matter?
Thinking strategically about your advertising budget can be one of most immediate ways to improve performance. We use the analogy of stock investment: "how is my money best spent?" Google or Meta? Now or later? Locally or internationally?
We find in-platform attribution - e.g. Data-Driven Attribution in Google Ads, or Incremental Attribution in Meta Ads - are most powerful for making in-platform decisions. However, putting different platform attribution models side-by-side create an immediate challenge. Even when you marry attribution window timeframes and one/many counts, you are still often dealing with different ways of attributing credit.
What can I do?
Google Analytics provides a way to compare traffic from campaigns in a like-for-like fashion and with a consistent attibution model. You can observe engagement rates (or bounce rate if that's your thing), dwell time and conversion rates between channels. This provides insight into user behaviour that can impact decision making. For example, we often find users from Google Search are more likely to have commercial intent (although try filtering out brand keyword traffic) compared to Social Media users (who are more likely to be browsers, having just been taken away from watching cat videos). However, for those looking to increase discovery or awareness, targeting social media browsers may very well be a priority.
As advertising campaigns can be periodic - i.e. on and off throughout the year - there's a risk you can miss out on seasonal opportunities. Search and Sales data in Google Analytics can give an indication of demand within a year and between different years (pro tip: so can Google Trends!). This is important as your money can go a lot further at certain times of the year - when you know demand is high. The same applies to geography and we've done some really interesting work in applying these insight to campaigns.
Google Analytics is one of the first places to understand the value of different channels, platforms and more. Armed with this insight, you will be smarter with your advertising budget.
Conversion Rate Optimisation
Why does it matter?
"Don't make me think" is a common refrain for website designers, and the same applies to advertising: the best experiences are those that are attention-grabbing, simple and easy. Or to use UX parlance, usable and desirable.
Saying that, we find landing pages are a source of regular frustration among advertisers. After intensive keyword research, careful ad copy crafting we're then given a crappy landing page to use. Do you really expect us to use that??
First step, take a deep breath and delete that angry email you drafted. Second, get some hard data on conversion rates and start fighting for (ok, suggesting) a better landing page. This is where Google Analytics comes in.
What can I do?
It's important to know what a 'good' conversion rate looks like. You can ask a search engine or LLM to give you generic benchmarks, but we find real historic data from the website in question is important too, using Google Analytics.
You can likely assume it has already recieved qualified traffic (from Organic Search, say) and that the conversion rate history won't massively change once you start sending ad traffic to it. Forecast out what that might mean for your ad performance, including your cost-per-aquisition and ROAS. A 1% conversion rate is only half as good as a 2% conversion rate, after all. Then present your findings back to your web designer and the wider team. Should we invest in a little conversion rate optimisation first? Armed with this insight, even if we don't change anything, gives us a lot more confidence in our campaign forecast measurement.
Google Analytics can also provide many of the insights needed to improve conversion rate optimisation - whether checking for technical gaps (e.g. site speed), UX frustrations or otherwise.
Audience Optimisation
Why does it matter?
For many years, audiences in advertising has been about one thing: remarketing (lookalikes aren't a distinct 'audience' so don't count). Simply target users who were recently browsing your website and watch the high-intent traffic flow back to your website. Never has increasing ROAS been so easy - just spend more on remarketing, baby!
However, saavy teams have caught on to the risks over over-remarketing. Marketing teams may notice their customer base is not growing, even as advertising reports ever-increasing ROAS. This race-to-the-bottom(-funnel) has diminishing returns. Then new targets emerge from management - new customers needed; better customers needed. Suddenly remarketing isn't enough.
What can I do?
Time to get smarter with audiences.
Firstly, new customers. Google Ads has the option to value new customers more than existing customers and adjust it's bidding accordingly. Meta Ads has the option to focus on incremental conversions (i.e. not bottom-of-funnel existing audiences). Google Analytics can provide the former with audience data to define existing customers. If you're reluctant to reduce remarketing but want to improve it's effectiveness, consider predictive audiences in Google Analytics as an alternative (basically a much smarter way to remarket to website users, above a certain conversion threshold).
Secondly, better customers. This is initially tricky as the definition of a 'better' customer is not immediately apparent. Often this means higher order values and repeat visits; or to put it simply: lifetime value. As much of lifetime value is measured "offline" in CRMs, there is work needed to measure across online and offline platforms. Thankfully we have offline conversion imports on ad platforms that allow this now. Google Analytics can help as an intermediary between CRMs and Google Ads if you count the User ID feature it has, which allows for psedonomysed customer data management between systems, and even an audience import feature. We find this is used in only very specific circumstances, for example on App or SaaS campaigns, where User ID is already captured for product analytics purposes. Read more about this in separate article we wrote on the value of customer analytics in advertising.
We find a combination of offline and online audience analytics, when combined with a clear strategy for value optimisation, can unlock a new layer of growth for advertisers. Just read our case study on an insurance brand where offline conversion measurement led to huge gains in ROAS.
Conversion Signal Optimisation
Why does it matter?
To ignore the role that conversions play in ad buying today is a fool's errand. Whether on Google Ads, Meta Ads or otherwise, conversion signals are critical in helping these systems understand what "good" looks like, what behaviours and characters to look for in audiences, and ultimately optimise for performance. It affects everything from bids to creative to landing page optimisation and more.
Many advertisers understand the importance of collecting conversions and many also invest in collecting more data; however we see the problem of improving conversion signals as more than 'data collection' and an entire optimisation problem in and of itself.
To ignore the role that conversions play in ad buying today is a fool's errand. Whether on Google Ads, Meta Ads or otherwise, conversion signals are critical in helping these systems understand what "good" looks like, what behaviours and characters to look for in audiences, and ultimately optimise for performance. It affects everything from bids to creative to landing page optimisation and more.
Many advertisers understand the importance of collecting conversions and many also invest in collecting more data; however we see the problem of improving conversion signals as more than 'data collection' and an entire optimisation problem in and of itself.
What can I do?
We have written a whole separate article on the process we call Conversion Signal Optimisation. Google Analytics can help in this situation as it is a source of conversions for Google Ads.
To help our clients navigate this, we bucket the process into three parts:
1. Data quantity - i.e. are there enough conversion events to hit the minimum or ideal threshold (Google advises at least 30 conversions account-wide per month, or 50+ for ROAS bidding)
2. Data match quality - i.e. the presence of metadata within each conversion, such as email or click ID, that are used for matching
3. Prospect match quality - i.e. the relevance of the conversion user signal to the ideal prospect.
The first two steps are more technical, as they require improvement in data collection. The last step is more creative as it involves the ongoing decision around what data to mark as the primary conversion goal of a campaign, plus what value to give it. This is especially applicable where a high volume of purchase conversions are not available, e.g. B2B or lead gen advertising.
Google Analytics can help provide some opportunity for advertisers who are stuck for conversion goals, most often through helping understand what on-site behaviour signals a high likelihood of leading to a purchase. It may be a video completion, a dwell time on a site, or otherwise. These can then be fed as alternative conversion signals back into an ad system and tested.
