Clinics / Healthcare Google Ads Case Study

The client background:

 

Our client is a Clinic which provides medical services like: neurology, cardiology, acupuncture, psychology and much more.

 

In 2015 they wanted to increase the number of leads for their clinics so they we’re looking for a Digital Marketing Agency. We were the first Google Ads Premier Partner agency in Transylvania so they reached us for providing Google Ads services.

 

We already had a lot of experience with clinics / healthcare and medical nice, so we knew that we were a good fit!

 

What we did?

 

Firstly, we created a campaign for every services they offer and we allocated a budget of 10.000 EUR. After

 

Firstly, we created search campaigns with ad groups for the services they offer.

 

We duplicated the campaign so we have 2 identical campaigns.

 

In the first campaign we targeted Cluj-Napoca (the city where the clinic is located) and inserted all the specific keywords excluding the words “cluj” or “cluj-napoca”

 

Also, for the second campaign (which was identical) we targeted the whole Romania, but excluded Cluj-Napoca and inserted all the specific keywords + the words “cluj” or “cluj-napoca”

 

Using this strategy, we targeted only people in Cluj-Napoca who are highly interested in the clinics services and also people from whole Romania who are looking for those specific services in Cluj-Napoca.

 

We excluded Cluj-Napoca from Romania campaign so the campaigns does not compete with each other, and by using the keywords “cluj” or “cluj-napoca” we have the certitude that those people are 100% looking for clinic services in this location.

 

Another strategy we implemented is brand protect and competition campaigns.

 

Brand protect campaigns were created after the competition was getting higher and higher in order to keep the clinic’s position number 1 on Google.

 

How we scale this business?

 

We looked over the account after the first month and we saw that there were some irrelevant keywords that people were searching on Google and reached us.

 

We inserted those keywords to the negative list, we stopped the keywords which didn’t bring quality traffic (the conversion rate was low).

 

We set the campaigns with manual CPC and we also increase the bid for the keywords that were having amazing results.

 

We focused on taking the ads to the #1 place on Google with the lowest cost possible and also stopped the ads with low CTR and created new ones.

 

The results

 

After 4 years of collaboration, the results are down below:

 

When we avoided these three Facebook Ad mistakes, ROAS improved by 4x

With this client, we worked in parallel to improve the three factors highlighted at the beginning;  Campaign Structure, Creative, and Targeting.  

If you are a marketer struggling to improve the Facebook Ads performance, I recommend reviewing your Facebook Ad account immediately after reading the blog.

Top 3 Mistakes Marketers Make with Facebook Targeting Settings

Targeting is a major variable and a contributor to Facebook Ads performance. That’s why you need to make sure that you have it exactly right.

If the settings remain suboptimal, it could be a major factor that hinders your Facebook Ads performance. Your Targeting settings is an area that requires attention.  

Let’s dive in the top 3 mistakes we’ve identified that could potentially generate a significant boost to your campaign if fixed.

Mistake #1 Over segmenting your audience

Targeting is set up on a per ad set basis. We have seen cases where the ad sets are based on granular categorized targeting.

The most common ones are:

• Retargeting on recency is subdivide into 1 day, 7 days, 15 days, etc.
• Subdivide the audience age into five-year increments.

Many marketers feel the retargeting should be broken down into granular segments when adjusting the bids. Now, more than before, the bidding logic is very different.

The major difference in the current bidding strategy from before is much of the bidding strategy is controlled by machine learning.

For many cases, excessive segmentation is incompatible with machine learning.

What’s important is targeting that fosters smart machine learning.

A key feature of a machine learning bidding strategy is that machine learning learns performance data and automatically bids on target.

There is one thing you absolutely must keep in mind when bidding with machine learning.

That is; You have to let it learn with enough data for it to make optimal bidding decisions.

Machine learning learns on a per ad set basis. If the ad set is overly fragmented, it isn’t easy to accumulate enough training data. As a result, optimization will not work well.

A similar situation applies to humans. If you want to improve your grades, you study a lot. The same is true for machine learning. To get smarter, you have to study and learn a lot.

The goal is 50 conversions!

Facebook’s guidelines recommend getting “50 conversions per week per ad set”. Anything less than this, your ad performance will be inconsistent due to insufficient training data.

Aim for “50 conversions per ad set per week” and try to consolidate ad sets targeted at similar audiences into one.

Many marketers mistakenly over segment their audiences with respect to things like device, ad placement, platform, gender, age and more.

This seems like it may be a good idea since you’ll have more granular control over each audience segment and an easy way to quickly see various performance metrics for each audience.

The truth is, you’ll actually be heavily inhibiting Facebook’s machine learning algorithms to effectively optimize your campaigns. In other words, you’ll be missing out on more money for your business.

It’s important to strike a balance between the trade off of granular segmenting and feeding machine learning algorithms proper conversion data. It’s recommended to feed a minimum of 50 conversions per week per ad set so that Facebook’s algorithm can properly learn.

Remember that many of the ways you might set up ad sets to measure audience segments in detail can actually still be measured by using Facebook’s dimension breakdowns reporting feature. This feature will allow you to see data for your campaigns by any of the following dimensions:

• Age
• Gender
• Country / Region
• Device
• Ad Placement
• Platform
• And many more…

You might be tempted to separate ad sets by some of the dimensions listed above but we recommend avoiding this unless absolutely necessary. If you’re in doubt just set up an experiment to collect actual data showing which set up performs better.

We recommend that instead of separating the ad sets by any of the dimensions above, just use the dimensions breakdown feature to see how your ads are performing in terms of each dimension.

If you’re feeding the algorithm enough data, then you’ll see your spend automatically being allocated towards the segments in each dimension that perform best.

Occasionally the algorithm may get things incorrect and so it’s still important to monitor your performance across these dimensions and intervene only if necessary.