In episode three of The Undiscovered Metric, we’re joined by Kim Whittaker, Senior Director of Professional Services at Adverity to talk about Google Ads metrics.
Check out our podcast to find out the key metrics for Google Ads, and how they can help you optimize your marketing campaigns.
Hi Kim, could you give us a quick intro to your role and how you got into working with data?
I’m the Senior Director of Professional Services at Adverity. My journey at Adverity started in the implementation team supporting our clients in the initial onboarding stage of our platform. My team now supports all of our clients throughout the lifetime of their journey with Adverity. Due to the range of small to enterprise clients, I’ve worked with, I’m hoping I can give some helpful insight into Google Ads today based on the knowledge I have built up over the last three and a half years.
Can you give us a bit of background on Google Ads?
First of all, it's the world’s top search engine at around 85% market share. This means it’s an obvious starting point for marketers to use as a key platform to promote their business as it allows them to easily reach large audiences with high-quality traffic. It operates a PPC (pay-per-click) model, so advertisers only pay when users click on their ads.
Google Ads is a very flexible platform. You can focus on different audiences, platforms, devices, and location targeting and be flexible with budgets for certain optimization opportunities. This is especially important for new businesses. PPC can really fill the gap whilst you build up your SEO efforts over time.
In Q4 last year, google saw its second ad revenue drop in the company's history. There are many reasons for this, such as the current economic climate's effect on the tech industry, and competition from TikTok for instance, competing with YouTube shorts. However, I still think it's a key player for marketing teams and their business.
What are the key metrics for Google Ads that marketers should be looking at?
For anyone starting out, my advice is to keep it simple. Marketers should be asking themselves what each KPI is telling them about their ads. So, to start with the obvious: impressions, clicks, conversions, costs, CTR, CPC — these are all key Google Ads metrics.
Firstly, looking at Impressions not only tells you how many times your ad was seen but comparing this metric against other campaigns can show you which messaging performs the best and what is driving the most clicks.
For clicks, we’re not just looking at the number of clicks your ads receive daily, but actually tracking this against impressions to see if your ads are reaching the right audience. A higher number of clicks usually means more conversions.
Conversions give you a good indication of whether users are completing the desired outcome from your ad. The average conversion rate for Google Ads on the search network is around 4 - 5%, and for display it’s 0.5 - 1%, which you can use as a very general benchmark, but bear in mind that this varies across industries. Search network generally gets more conversions due to the user actively looking for a solution compared to display ads.
4. CTR (Click-through rate)
CTR is a good indicator of performance, especially when comparing different ad or keywords to figure out where optimizations can take place.
These metrics show you how much you’re actually paying for each click on your ads. They’ll be the main metrics your budget depends on.
There are many reasons that most Google Ads and performance dashboards at Adverity hold these metrics. They’re a key starting point to understanding your audience and answering key business questions.
What questions can Google Ads metrics help you answer?
Google Ads is always a good place to start, but as the marketing world evolves, so do our platforms. It’s great to understand the interest, the impressions, the clicks. However, this can drive you to more business questions around how to update your strategy.
A lot of marketers will use Google Ads as a base and link this alongside Google Analytics properties for instance. Whilst Google Ads gives us that base knowledge, linking with Google Analytics will give answers to more of those key business questions. For example:
- Out of those clicks, which clicks are new visits Vs. returning visitors?
- Outside of Google Ads, where is my referral traffic coming from?
- Could I target another audience outside of Google Ads to complement it?
- How long are users spending on my landing page?
- Which keywords are leading to high conversions?
- What is my drop-off rate?
The combination of different Google suites can be powerful in answering more business questions.
What would you say is the most undervalued of the Google Ads metrics and why?
I’m going to go with two key metrics — quality score and impression share.
The quality score is the combined score from 1 - 10 calculated by looking at expected click-through rate, ad relevance, and landing page experience. Why is this important? It gives you ideas to improve your ad experience, and in turn increase conversions and reduce costs.
The timeline of events for the quality score is vast — there have been frequent updates and changes to the algorithm constantly making improvements and refining the formula and positive effect on its ads. It means by staying on top of this metric you’re staying current and your content relevant.
Obviously, there are for and against arguments for many metrics and many may disagree with this being my undervalued metric, but I believe it's one of the most consistent approaches around keyword data.
The second metric I mentioned is impression share. This is an important metric to understand if you would like to transform the performance of your ads. It looks at your actual impressions and divides them by your total eligible impressions. If your impression share is low, this points towards potential keyword or budget issues.
If you have a low impression share then you can optimize your setup by looking at your target audience, quality score, increasing budgets, location targeting, getting rid of low-performing campaigns, or seeing if any competitor ads are overlapping.
I don't see this used enough, and there could be for many reasons for this — perhaps users tend to focus on more vanity metrics rather than actionable metrics in order to secure budgets.
Do you have any useful tips and tricks for working with Google Ads metrics? Any common mistakes people should avoid?
I think a lot of what I have spoken around today is really enabling and focusing on those optimization opportunities. Tools like keyword and negative keyword optimizations for example will really help in driving CTR and conversions.
In terms of things to avoid — don't just auto-apply recommendations. Make sure you know your data, really think is this having a positive impact and driving my own goals, especially around search and display networks?
Looking forward, what are the top 3 things set to catch fire in marketing data over the next 18 months?
Firstly I’m expecting to see even more short-form video trends. You only have to open Instagram to see how the platform is trying to push you to upload reels rather than pictures. Also the increased revenue and usage on TikTok is a good indicator here.
Secondly, there’s an increased focus on first-party data due to restrictions on third-party. Google is set to deprecate third-party cookies by 2024 which has had frequent delays and has been pushed out. I am sure there will be a lot, and there has already been a lot, of conversations and discussions on its replacement too.
Thirdly, using integrated data platforms and tools to get a single source of truth and trusting your data.
Finally, what’s the one piece of advice would you give to your younger self or to someone starting in the industry?
Challenge yourself and get out of your comfort zone — the industry is fast-moving. Don’t be left behind! Ask questions and continue learning.
If you enjoyed the Undiscovered Metric, you can check out the previous episode here.
Marco Senftleben, Senior Solutions Consultant at Adverity explains how Amazon Marketing Cloud can help marketers uncover patterns in customer behavior by combining data from different sources in a data clean room.