The case against ad spend

The most common question / feature request that we see in our live chat on a daily basis is

“Why don’t you show budget”
“Where’s the ad spend shown”
“What’s the CPC’s for this”
And the short-answer, for now at least is, it’s not coming. It’s not a feature. And with Ad Lab data, you simply don’t need it.

Now, before you tell me that I don’t understand, let me explain.

When we look at the ancestor PPC Spy tools available, you’ll see that some report an Advertisers ad spend.

SpyFu for example has a screen like this:

The answer is well, none of these actually.

Looks great doesn’t it. Why wouldn’t any Spy tool want a feature like that.  


Because the numbers are wrong. Not just a little wrong.  A LOT wrong – like 80-90% wrong.

Which makes you wonder why even bother.  In fact some users have said to us – “I know it’s inaccurate but I still want a number”.

Here’s why using a bad number is a really bad idea.

It’s a dumb way to build authority

Imagine the conversation, “Hey my name’s John from Awesome Marketing Inc.  My research indicates you’re spending about $5k a month on Google Ads.  Am I right?”. Click…

You are starting the conversation by undermining your own credibility.  Now call me old-fashioned but I’m pretty sure that breaks every sales rule in every sales book ever.

When it comes to “know, like and trust”, you’ve just created “know, unsure and mistrust”.  Again, hardly sales 101.

If that’s your game plan, go ahead and throw around numbers that you’ve really no idea about.

I need it to prioritize outreach

Let me get this straight.

You’ve got a list of 20 advertisers and their “spends”.  A number we know to be wrong by in some cases 90% or more.

And you want to use it to prioritize your business activities.


Diving deeper into your prioritized list, what does it actually do for you.  In short, nothing.  You might as well put all your prospect names in a hat and pick them at random.

What’s worse is if you are giving this to a team to work with, they will pretty quickly realise the data is next to useless.  The credibility of the data, and your credibility as it’s provider will disappear, leaving your team frustrated.

It’s actually better in my opinion to give your team a list and acknowledge that it’s not sorted than make an attempt that is fundamentally flawed.

It’s severely lacking and incomplete

For a prospect list to be useful, your list needs to have two key characteristics.

  1. It needs to be complete (or close to)
  2. It needs to be reliable

We’ve run tests.  We’ve checked.  Our real-time Ad Lab list depth vs historical data provider lists.

Here’s the result:

We literally found 10 times more advertisers than the closest competitor. If this was a boxing match the referee just stopped the fight.

But more importantly than the 10x depth improvement, let’s consider accuracy.

Other providers use historical data. You know that their list of advertisers DID run ads. You don’t know for sure that they DO run ads NOW. And the reported spend. That’s as old as the data.

That’s why we deliver real-time results. Advertisers running ads today. You simply cannot get a more up-to-date list.

So why is it so hard to get accurate data

Starting with the most obvious point, the only people who actually know how much spend happens in a Google Ads account are:

The account owner (and other users including their agency).
I’m fairly certain that neither of these parties are sharing ad spend data with 3rd party tools. And so, any tool that includes ad spend has to create an estimate (fancy word for guess).

Imagine this scenario:

You run a search for “hvac installer tampa”

A bunch of ads are shown.

Starting with the search term.

An ad could show up for a search for “hvac installer tampa” in any/all of these situations:

Exact match keyword “hvac installer tampa”
Exact match close variant on “hvac installer in tampa”
Phrase match “hvac installer”
Phrase match “installer in tampa”
Broad match “hvac tampa”
Broad match “ac installer tampa”
Broad match “hvac installer” shown to users in tampa
Broad match “residential ac”
Smart campaign based on their website
The problem is with the search term, is that you have no insight (except in the account) as to what the keyword being bid upon is.

And so, you might have picked the one and only phrase that would trigger the ad to be shown. Or there could be dozens, and without testing each of them you don’t know. But even if you do test every combination you can think of, you need to go round this loop again, for every additional phrase you test. It’s a recursive nightmare.

What’s more, each keyword has a quality score that influences cost per click – significantly. And as nobody outside the account can see it, your cost calculations start with an estimated CPC and apply an estimated adjustment.

Now we know the search term problem, let’s look at geography.

An ad showed in a test in zip code 77043. But what about neighbouring zip codes, can we test those too?

Was the ad targeting nationally, a state, a city, a zip or any combination of each, and with or without excluded areas.

You don’t know, and even if you test, unless the advertiser has 100% impression share (they don’t), then you don’t know if you simply got a false negative by NOT seeing their ad in an area.

What about budget?

Again when you test, you don’t know whether you saw an ad showed once or a hundred times (we do, Ad Lab can test). You don’t know if their ads are stopped on an evening or weekend.

There’s more. We could dive into demographics, device adjustments, in-market audiences and more.

All of which is a long way of saying that it’s not a shock that nobody reports good ad spend data.

Here’s a quick look at the estimation formula:

ESTIMATED cost per click
X ESTIMATED Click through rate
X ESTIMATED impressions
X ESTIMATED number of keywords
What’s the alternative then?

It’s actually quite simple, let’s stop pretending we know something we don’t and take action on things that we do know.

Using Ad Lab – we know some of the keywords that an advertiser is bidding on, and how visible they are relative to their peers.

What does that tell us?

It tells us ranking of spend for that keyword. Assuming that click through rates are similar enough across advertisers then visibility = spend.

That’s enough to give us a hierarchy.

Do that for 10 known popular search terms in any given market and you’ve got a pretty reliable ranking by visibility as a proxy for spend.

You can say with certainty that in your testing during [date range] that their ads were the [n]th most visible out of the basket of search terms tested.

What’s more, you can quantify the difference between them and other advertisers in the market.

For example:

In this instance the top advertiser was seen 6 times and the 10th advertiser 1 times.

For your prospecting, you now have a prioritised list to work with based on real data.

You’ll never undermine your credibility as you can justify your analysis and there is no counter-argument from the prospect – as they simply don’t have the date you have or the ability to create it.

You see how working from this one simple fact enhances your credibility and prioritizes action in a meaningful way.

What’s more, rather than the handful of advertisers the old tools provide you’ll have up to 10x more advertisers to prospect to, ranked by priority.

Are you with me yet?

By now I hope you see that a bad number is not better than no number. Quite the opposite, it’s worse than no number as it leads to incorrect decisions and weak positioning.

On the other hand, provable, carefully selected numbers give you all the prioritized prospecting and authority positioning you could ever need.

So start working with the right number – even if it doesn’t have a $ sign.

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