To optimize a PPC campaign successfully, it’s necessary to have statistically significant data. More often than not, there are 2 factors that prevent this: inadequate targeting and insufficient budget.
If your audience or budget are not big enough to gather the data needed, it’s extremely hard to make educated decisions for campaign optimization.
Fortunately, in this client’s particular case, we were able to work with the right targeting and a sufficient budget for it. Thus, we were able to create a very successful account due to having enough data to back our optimizations.
Data is sacred because of privacy and its worth. On this note, let me make clear what data I will be and will not be sharing in this case study.
I cannot and will not share any of the following for privacy reasons:
Basically, anything that has to do with personally identifiable client information or the details of our success formula is off limits. However, I am going to be sharing the following relevant information:
Traffic Source: Google Ads
Niche: Auto Glass Services
Conversion Tracking: Lead Generation (Phone Calls & Contact Forms)
Duration: 3.5 Months (as of now)
Of course, I will also be sharing the results and how we were able to achieve them.
Here are the results of 3 and a half months of account optimization:
The total number of qualified leads increased 401.32%
The overall click-through rate increased 18.00%
The average cost per lead decreased 12.33%
The overall average cost per click decreased 41.67%
It’s important to note that our client significantly increased the ad spend budget twice during this time period due to the improved results. Another noteworthy point is that before we took over the account management, the account had not been optimized since January 2019. That month had similar spend to our current spend starting last month. I believe it is worthwhile to compare the results of both months because the total leads will not be inflated due to budget differences.
Here is the results comparison for January 2019 vs June 2019:
The total number of qualified leads increased 100.00%
The overall click-through rate increased 36.62%
The average cost per lead decreased 47.45%
The overall average cost per click decreased 40.90%
I believe that these results are more representative for the optimizations that we have done on the account because the ad spend was very close. With the same ad spend, the qualified leads doubled due to improving the quality of traffic and lowering the costs.
We were lucky to be working with an account that already had relevant historical data that would allow us to make informed decisions based on past trends.
The way the account was set up before we took over was the following:
As you can see, the account had many active campaigns. After analyzing the data, I found that most of them were underperforming and decided to pause them. I reallocated the budgets accordingly. I also noticed that Campaigns 1 and 2 each had one particular ad group that was taking most of the budget, but was also converting the best.
Given this information, I made the decision to separate each of these ad groups into their own separate campaigns (let’s call them 1a and 2a). Because this account restructuring took place during the middle of the first month, there is still data for the campaigns that were paused. The campaigns highlighted in red were no longer active after the restructuring.
Here is what the first month looked like:
Based on the data collected from Month 1, these were the new changes I applied to the campaigns:
Conversion-focused Keyword Bidding
I reviewed all the keyword data and increased bids for converting keywords to improve their rank. I also looked at the CPA for these converting keywords and lowered the bids for those keywords that converted at a high cost.
New Expanded Text Ad variations
All the campaigns were very overdue for new ad variations because they had been running the same ads for a long time. Adding new variations would allow us to AB Test them for performance.
Added Positive Bid Adjustments to the Ad Schedule
This way we could focus our efforts on the best day of the week to drive more leads.
Removed Negative Bid Adjustments on Devices
Most of the traffic was coming from desktop so, in order to drive more mobile traffic, we removed the negative bid adjustments on all campaigns.
The campaigns ran with minimal adjustments for the rest of the month and these were the results:
Campaign 1 did not do so well when it came to cost vs conversions compared to the other campaigns. I did not feel that it was right to get rid of it just yet, as there was not enough data to back that decision yet. I decided to leave the campaign be for now and observe it for another month.
However, the account as a whole performed much better than the previous month, being able to increase the leads and traffic by almost double without spending a significantly higher amount.
These were the new changes for the month:
Paused Underperforming Ads
Now that we finally added new ad variations to the campaigns to test for performance and improve the appeal and relevance of our ads, it was finally time to check the data and see what was performing and what had to go. We paused the underperforming ads and created new variations of the top performing ads in order to continue AB testing.
Reviewed Search Terms and Added Converting Terms as Keywords
I noticed that there were several highly relevant search terms that ended up converting at a good rate and were not being directly targeted as keywords. To improve our conversion metrics, we added those terms to our campaigns to make sure that we were showing up for those searches as well.
Added Negative Bid Adjustments to the Ad Schedule
There were certain times and days of the week that were not performing as well and were producing higher costs. By adding negative bid adjustments for those times, we open up the budget for better performing times and days and make better use of the budget to improve the account’s return.
A bit over halfway through the month, our client decided to add another significant increase to the ad spend budget which further boosted our traffic and conversion volume because of the good results we had brought with our previous optimizations.
Here are the results for the month:
I was right when I decided to give Campaign 1 a chance to redeem itself from an underperforming month. Now it had caught up to the other campaigns. As for the budget increase, the highlighted campaigns received most of it because those were the ones that were limited by budget. The other campaigns did not have that issue.
The cost reduction efforts took a bit of a toll on the conversion rate, as you can see from the traffic nearly doubling but the conversions increasing by 48%. However, the cost per click went down by 28.35%, which resulted in the overall cost per acquisition going down by 5.83% – a net positive result.
This month was somewhat irregular because there were some client requested changes and the campaigns were paused by the client for a few days, which caused a drop in performance due to lost traffic compared to the previous month.
Paused High CPA Keywords
The big change that I did to the account this month was getting rid of keywords that were converting at a really high cost. This would open up room for other keywords that are converting well at a lower cost to have more budget available to continue producing good results.
Added Responsive Search Ads
I also went ahead and added Responsive Search Ad variations to our ad groups in order to test for their performance, as they are a new type of ad that utilizes machine learning to fine tune itself and I wanted to see if it would bring any benefits to the account. I will be coming back to them to see how they have performed.
Here are the results:
We received less traffic and, as a result, less conversions than last month. This resulted from the campaigns not running for a few days. This should not present a big issue in the months to come as long as we can run the campaigns like normal.
Campaign 2a stood out this month because it spent almost as much as Campaign 2 but had only around 60% of the conversions. The reason for this was because the ads were showing in lower positions than usual, which affected the conversions. Bidding for this campaign needs to be adjusted accordingly because it performed well in previous months and can be brought back to a good level of performance with the right adjustments.
Overall, both myself and my client are very satisfied with the results the account has produced thus far. Optimization is all about making educated decisions using the right amount of historical data in order to improve performance. As time goes by and trends become more apparent, this account can be further optimized to produce even better results! Now that we have a few months’ worth of data to work with, we can focus on narrowing down our top performers even further and shutting down what isn’t performing at an ideal level. That’s just the way it goes when working with Google Ads. It’s all about doing our best for our clients with the data that Google provides us.