Carpet Cleaning Google Ads Case Study
My Google Ads Case Study is in the carpet cleaning industry.
One of my favourite jobs is to study data. Boring you say?
It can set you apart from your competition. Let me say that I have tried every bidding option in Google Ads.
My Approach
I studied every automated recommendation. I tried all the suggestions.
Experimented with keyword types and combinations. Studied search terms extensively. Blocked bad searches.
Built a large negative keyword list to stop bad searches.
Created negative keywords to protect Adgroups from competing with each other.
Tried different landing pages. Adjusted sales messages.
Argued with Google staff about click fraud that their systems did not pick up.
I am always looking to find a better way in managing Google Ads.
So Now to My Google Ads Case Study:
Client: Carpet Cleaner
City: Sydney (population around 5 million)
Date: Nov 2021
I researched the average CPC for certain keywords in Sydney and then compared them to my campaign average CPC for the same keywords.
Keyword Activated | Competitive Ave CPC | My Campaign Ave CPC |
$ | $ | |
Carpet Cleaning Sydney | 8.29 | 1.78 |
Carpet Cleaning Castle Hill | 14.46 | 2.07 |
Rug Cleaning Sydney | 8.73 | 2.73 |
Sofa Cleaning Sydney | 12.07 | 1.92 |
Mattress Cleaning Sydney | 7.45 | 1.62 |
Why Does the Average CPC Matter?
I can send 3-4 times more visitors to the website when average click costs are driven down as in this case.
Bad searches have less impact. Click Fraud becomes a minor issue. Indecisive visitors do not waste your campaign budget as much.
Bidding only on conversion data is flawed. Niche long-tail keywords seldom reflect the correct conversion results.
You have to wait a very long time (years) to get the correct conversion data for long-tail keywords.
Most PPC Managers will Fail With Three Things.
They go very narrow on keyword variations. Choosing not nearly enough keyword variations. They do not know enough about their client’s company or an industry.
Managers then mostly compete where strong competition is found. High average click cost is often accepted as the norm by PPC vendors. Even worse, the same managers switch to conversion bidding within a month or two.
This is an automated process that saves management time. In my opinion, it helps PPC managers manage many clients at the same time. Not ideal for the client.
What Makes My Approach Different?
I do comprehensive keyword research. All my campaigns are based on a large volume of keyword variations.
The initial research involves many hours of work. It is the foundation of a quality Google Ads campaign.
In the above case study, results were achieved with a combination of Maximise Clicks bidding and Conversion Data analysis. You cannot discount Conversion Data as it remains a very important aid in Google Ads management.
However, most PPC vendors and Google specialists rely on Conversion bidding exclusively. It has the effect of concentrating competition driving the average CPC’s up. It also overrides many options to segment bidding.
Maximise Clicks bidding (done with Conversion Data) can get Clients more bang for their buck. More leads and phone calls for the same budget.
Increased potential customer searches increase the volume of data collection for other types of marketing. Re-marketing and email outreach come to mind.
Caution!
This strategy only works in certain instances.
My Sydney carpet cleaning client is flexible to have adjustments made to his website. Having an Adwords-ready website is the starting point.
The client has to offer many different services. Limited services drive average click costs up because it shrinks the keyword variation pool.
Finally, my client covers a very large area. Many suburbs/areas combine with different service options. This boosts keyword variations and drives click costs down.
Conclusion:
Research into your client’s company and services is crucial. This takes time to achieve. Specialist PPC vendors are able to draw on historic data of a specific industry.
In my opinion, they also end up with a better understanding of Google Ads.
Data collected from: