Jason is the CMO @ ClickGUARD. He is passionate about all things PPC, SEO and has extensive customer acquisition experience. When not writing about SEM he can be found surfing the wildest ocean waves of the South American coast.
Their dialogue uncovers the power of the post-click analysis, known as “the secret sauce of data-driven Google Ads marketers.”
Miloš gives us a crash definition for post-click analysis:
The process of recording the behavior of visitors on your website, after they click on one of your adverts, to turn it into actionable information.
Right away, Miloš emphasizes the importance of making that information actionable.
So, do all marketing ninjas, masters in PPC, use post-click analysis?
No, unfortunately. If you’re one of them, follow Miloš’s advice and start analyzing visitor behavior beyond Google Analytics to really optimize your advertising efforts.
When you’re running ads, you are configuring your ad vendor to target a specific audience. The platform allows you to target them geographically, demographically, based on interests, etc., etc., and will do its best to match your adverts with the right people. Its whole job is to send you leads. But after they’re sending someone to your website (and charging you for the interaction), that’s it.
And, you’ve guessed it right, if you don't do post-click analysis on the traffic you’re paying for, you rely solely on the controls provided to you by the advertising platform. No PPC optimization for you! Doesn’t sound too good, does it?
Can an account be too small to use post-click analysis? Can the budget be too restricted to afford post-click analysis?
We can’t argue that size matters when it comes to data and performing post-click analysis to make it actionable. But, Miloš reminds us, “as any data scientist will confirm, you most certainly can use almost any scope of data, you just need to adjust the algorithm. If the data sample is small you use simpler algorithms to analyze it and make conclusions.”
So, (IF sample=small, THEN algorithms=simpler). (IF sample=big), THEN… this is the best part: you can introduce complexity and perform deeper analysis for better insight. That happens “naturally” when you’ve got a lot of paid traffic.
To conclude, when dealing with data, “the more, the merrier”, and a bigger sample will lead to better conclusions.
But, as Miloš suggests, no need for a scientist to show if a small advertiser would benefit from knowing:
It’s as simple as one plus one, according to Miloš: your marketing strategy must include paid traffic quality analysis and attribution. How else are you going to measure results?
We’ve covered how ad platforms work and how they can trigger clicks and traffic that drain your ROAS, and Miloš tops it up with an example.
Let’s say you want to show your ad in California to people who have an interest in sneakers. Yes, you have a sneakers business for which you launch an ad campaign, and clicks start coming in. You’re getting conversions, meaning a number of people who clicked on your ad end up signing up for your innovative “random sneakers every month” subscription.
There are some numbers there for you to look at and review, like:
When you calculate cost per conversion, you’re like “hey, this works for me. I can pay $250 per conversion because the average LTV of my customer is like $500!”
Your customers usually stick around for 6 months, then they get sick of all the cringy sneakers you’re sending them after the 3rd month. All this seems a bargain, for you’re actually running a successful business.
But then, your business gets competition, the cost per click goes up, and you start noticing some abnormalities. Your Google Analytics shows a lot of traffic on the website and a very high bounce rate. You start noticing crazy click-through rates (like 300%), as well as what your ad vendor will show as “invalid traffic.”
Spoiler alert: you’re going to be dumbstruck for at least three reasons.
All these actions based on data from post-click analysis translates into a lower cost per conversion and maximum ROAS.
If he said it once, he said a thousand times: post-click analysis is the best way for the online advertisers to get intimately acquainted with their audience through actionable insight. There’s no point in watching hours of video recordings of people’s interactions with your website.
Post-click analysis is a part of ClickGUARD’s forensics -- a 360 overview of every single ad click you’ve paid for.
And so, if you want the full potential for PPC optimization, all you have to do is combine post-click behavior data with other data. For instance, look at
To make it all actionable, apply protection and optimization rules, which will automate the process of dealing with wasteful ad traffic.
You’ve run ads before, so you know the drill: your ad vendor provides you with metrics that show how many times your ads were seen and clicked on, and you know nothing about what happened on the website. You know what didn’t happen: conversions. You pay for more clicks, missing on the essential: the better clicks.
The data you get from us empowers your decision-making for PPC Optimization. We provide you with insight that opens up huge opportunities to save your budget from waste and re-allocate it to target intent.
When so many Software as a Service products abuse the AI/ ML buzzwords to draw customers (throwback to Session #5) disregarding altogether transparency, accountability, no wonder some others brag about letting you review the entire session. In all fairness, that allows for some behavior analysis.
No offense to the software, but going through recordings to catch click fraud and wasteful interactions it’s a waste of time for a human. It’s 100% not scalable and 100% not applicable to any decent volume of paid traffic.
When we say transparent: behavior data can never be definitive, but from a data science perspective, we can look for patterns and cases that stand out. We offer you a paper trail so you can always make your own conclusion.
ClickGUARD never claims that a website interaction was made by a bot. Behind our conclusions there are complex algorithms that analyse the interaction, compare it with other similar interactions. There’s no blaming game, as we show the probability that an interaction was made by a bot rather than plainly say “it’s a bot”.
When we say actionable: can those vanity metrics and colorful charts be used to automate and control your PPC optimization process?
For the bonus question and unpredictable answer, tune in to our podcast.