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January 18, 2024
December 2, 2020

When someone purchases via your website, every digital advertising channel that they have been exposed to (often in the last 30 days) will claim credit for driving you that sale.

This presents a visibility challenge for brands running multi channel combinations such as Facebook + Google Ads, as the revenue from that sale (human) saw both a Facebook and a Google ad on their path to buy is now being claimed to have been driven by Facebook and Google, which although true, results in two reported sales and double the revenue.

There are two common ways that people commonly report your digital advertising return and cost per sale.

The first is based on Google Analytics data.

Google analytics is tracking on your website that records behaviours and how the user came to land on your website.

PRO: This won’t double up reported revenue.

CON: It will only give credit to the channel that drove them to the website for the session they became a customer. So if they saw 100 Facebook ads, but then came back 2 weeks later through a Google ad, FB get’s no praise.

The second is based on ad channel data.

Ad channels tag users that see/click your ad and know if the same person ends up purchasing via your site.

PRO: We get a much fairer view on if someone who has become a customer for your business has been exposed to your digital advertising regardless of what channel drove them last. This is important as rarely will people see a single ad>click that ad>buy. They need multiple touch points to get to the point of transacting. The amount of touch points varies depending on the industry, but rarely is it 1.

CON: If your digital advertising channels compliment each other (as they should) and work together to drive a purchase, then they are both taking the credit.

I know what you’re thinking… just figure out which people saw/clicked both channels ads and which only saw/clicked one channel’s ads! Trust me, i’ve tried. Attribution is more of an art than a science, and whilst platforms do exist to try and crack the science part beyond a doubt, they aren’t without their own range of inaccuracy and a hefty investment (if you’re paying for it you’d want it to be 100% right?).

So how do we report to you the return from your paid digital advertising activity?

It sure isn’t by claiming your investment returned X amount of revenue via a method that has a major con (both of the aforementioned ways).

Our approach is pretty simple, but does require you to think a little differently.

Firstly, use each ad channel’s data to highlight all the revenue they think they are 100% responsible for. This might look like this:

  • $45k of revenue came from someone who saw/clicked a Facebook ad
  • $50k of revenue came from someone who saw/clicked a Google Search ad
  • $5k of revenue came from someone who saw/clicked a YouTube ad

Work out your de-duplicated worst case scenario of digital advertising return, which would be the biggest number out of all these channels. In this case it’s $50k. This would assume that the $45k and $5k leads from Facebook and YouTube ads are part of the $50k that Google Search is claiming – all channels working together.

Then, work out what your best case scenario is. This would be adding each ad channel together and assuming that each is reporting unique transactions. So you get 45k+50k+5k = $100k as the absolute best case incremental return from your investment.

This gives you $50k-$100k leads as your incremental success range. This means that there is $50k-$100k of revenue that we can without-a-doubt attribute to your campaigns.

In this scenario, you should raise the lower end of the range if your paid traffic revenue in Google Analytics is greater than $50k, as that is indisputable evidence that a portion of purchasers only came across one channel’s ads.


If ad channel data (prone to duplication) says….

  • $45k of revenue came from someone who saw/clicked a Facebook ad
  • $50k of revenue came from someone who saw/clicked a Google Search ad
  • $5k of revenue came from someone who saw/clicked a YouTube ad

But Google Analytics data (de-duplicated) says…

  • $20k of revenue came from someone who saw/clicked a Facebook ad
  • $40k of revenue came from someone who saw/clicked a Google Search ad
  • $2k of revenue came from someone who saw/clicked a YouTube ad

You should sum this revenue as it is already de-duplicated via Google Analytics, so the worst case actually becomes 20k+40k+2k = $62k.

So $62k-$100k right? That’s still a pretty big range?! Yeah you’re right. The reality is, everyone is influenced differently by advertising and some people will get touched by multiple channels and some will become a customer straight away after clicking just one ad. Some people might not even get targeted by all channels, and some by all of them.

So although it’s not perfect, it’s actually the most transparent way to report the return on your digital ads spend (for now! Rest assured we’ll let you know when a free & flawless way to do this becomes available). If you have to have a single number, go the median of your range.


Why not just use Google Analytics if it’s already de-duplicated?

  • You either report on de-duplicated leads by where they came from last (analytics) or you tag duplicated leads. The main issue with going the de-duplicated route is more often than not none of your advertising will be credited with a lead it drove.

For example, take this path to becoming a lead for your brand:

  • Never heard of your brand before
  • I see a FB ad, don’t click.
  • + 2 days
  • I see your YouTube ad.
  • +5 days
  • I decide to search about you and your competitors and click your Google Search Ad.
  • +1 day
  • I search your brand name and click your unpaid google listing and click to call you.

Guess what drove my phone call? All of your digital advertising.

Guess who claims it in Google Analytics? Google organic traffic.

What about the models available in Google Analytics that aren’t last click? Shouldn’t we just use them?

  • 90 day windows as a max which isn’t great for longer lead cycle businesses.
  • Doesn’t factor in a view or impression as the user has to click the ad for Google Analytics to fold it into modelling.
  • Whenever cookies are involved, you can bet this isn’t going to be accurate.
  • The only machine learning option requires 600 conversions every 30 days.
  • You really think Google is going to say that FB ads did a better job than theirs?

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