Lead scoring isn't a new concept in the digital marketing realm, but its application to platforms like Google Ads can transform the way businesses target and optimize towards their potential customers. When investing money into Google Ads, ensuring that you're targeting the leads most likely to convert is paramount. Rather than focusing on CPA, lead scoring enables you to optimize to ROAS so it’s more about lead quality rather than quantity. By passing back these signals to Google Ads, it ensures your budget is being spent on the right keywords, audiences, locations, devices etc.
Let's dive into a step-by-step guide on how to implement lead scoring for Google Ads to optimize your ad spend to effectiveness, rather than efficiency.
Before delving into the process, it's essential to grasp what lead scoring is. In essence, lead scoring is a strategy used to rank potential customers based on the likelihood that they will convert into actual customers. Various criteria, such as form submission information, online behavior, and engagement levels, can determine this ranking.
It’s best to analyze your own data in order to find the right criteria for lead scoring and this can be done in a few different ways. For example, you can start simple and just use internal knowledge that leads coming from a company email address vs. Gmail / Hotmail tend to be much better quality. On the other hand, if you have lots of data on your leads because they submit an application form (e.g. loan application) then you can use this data to score key criteria based on the user’s input. If you want to try something even more advanced then you can use historical, structured data to train an AI model using a tool like Google’s Vertex AI which will tell you what the most important criteria is for scoring a lead and you can reference this model each time a user fills in an application or form online.
This guide focuses on a fairly standard way to implement lead scoring which includes a bit of data analysis and front-end web development. We would recommend testing this first before trying a much more advanced method, such as using a pre-trained AI prediction model. For those who want to try a much simpler method, we have have included a bonus section at the end of the article.
As a working example, let’s say you’re a gym franchise and you ask the following questions in your free trial lead form;
Whilst contact details aren’t very useful for lead scoring, unless you have good data on business email vs. personal, the other two inputs can be great for lead scoring. From analyzing your own data you might find that users interested in “personal training” that are from a certain state have a much higher LTV or lead > customer conversion rate.
Not all criteria hold equal importance. For instance, certain user inputs or engagement metrics can be more valuable than users. It’s important to define a point system and assign values based on the significance of each metric. For instance, using the gym example above;
This means that if someone is looking for “personal training” and lives in 1002 postcode then they score 75, which is 582% more valuable than a lead that was “don’t know” from the 1003 postcode.
Now that you have the criteria and the scores, the best way to send this information to Google Ads would be to implement a data layer (push event) which is triggered when your online lead forms are submitted.
Here is an example of the data layer push event that a front-end developer can implement and populate once a lead form has been successfully submitted on your website. The custom variables should update depending on the user’s input e.g. 'personal training' would change depending on what the user selected in the lead form dropdown menu.
window.dataLayer = window.dataLayer || ;
'interested_in': 'personal training',
Once a data layer push event is in place, we recommend using a tag management system, such as Google Tag Manager to configure the scores (via a lookup table) and the relevant Google Ads tag.
Using a lookup table as a User-Defined Variable allows you to easily update the output values without needing to get a developer to edit any code or update any on-site logic. Meaning that if these values change after you collect more data then you can edit this in a matter of minutes, rather than waiting for the next dev sprint.
Once you’ve created your lookup table then you will want to edit your existing lead submit event or create a new one. You assign the firing trigger as the data layer event e.g. “lead_submit” in this example and you add the lead scoring lookup table as a variable for the “Conversion Value”. This means that you pass-back the “output” which is a numerical value, enabling you to optimize your Google Ads budget to “value” (ROAS).
We recommend that you collect data against this tag for 30 days before enabling a ROAS bidding strategy in Google Ads. This will give you a good idea on what ROAS target to start with, for example, a “conv. Value / cost” of 4.10 means that you should use 410% as your ROAS target initially. Let the new ROAS bidding algorithm go through the learning phase and then you can adjust depending on your business goals - increasing the ROAS should increase the quality, although decreasing the ROAS target should increase the quantity of leads.
You can enhance the lead scoring bidding strategy by increasing the amount of 1st-party data that you share with Google Ads so it can use this data to better understand what “quality” looks like. Google’s auction-time bidding models already have access to Google-owned audiences, although we recommend using customer match and GA4 to segment audiences and overlay these on your campaigns in order to increase the amount of signals that Google can use to set the right bid.
Like all digital marketing strategies, lead scoring for Google Ads isn't a set-it-and-forget-it method. Regularly analyze your metrics to see if the scores correlate with actual conversions. Adjust your criteria, point system and segments based on this data.
If you're using a Customer Relationship Management (CRM) system, integrate it with Google Ads. This integration can help you track the lead's journey and score throughout the conversion funnel - this can be achieved by using offline conversion tracking. You might not have enough data to optimize for “offline conversions” but this can be a useful exercise to look at retrospectively to ensure you’re spending your budget in the right areas.
In addition to this, it’s possible to implement touchpoint / funnel lead scoring based on how far users get in the sales process. It’s about finding the right balance between volume and quality signals - you want enough conversion volume for the bidding algorithm but a lead status which gives the right signals back to Google. Some successful strategies combine both lead scoring based on a user’s input and CRM data such as leads marked as “Qualified” by the internal sales team.
As you get a better understanding of lead scores, personalize your ads & communications accordingly. High-scoring leads can be targeted with more direct conversion-focused ads and emails, while lower-scoring leads might benefit from informational or awareness-based content.
Lead scoring, when tailored for Google Ads, can drastically improve the effectiveness of your campaigns. By prioritizing high-quality leads, you can ensure better resource allocation and improved ROI. Regular refinement and a thorough understanding of your target audience are crucial. So, as you embark on your lead scoring journey, always be prepared to adapt and evolve with the ever-changing digital landscape.
If you’re looking to test a simple version of lead scoring without implementing a data layer or you don’t have any lead form criteria that you can use for the score values, then a simplified version of this can be adding different pre-set values to your Google Ads tags.
For example for a B2B SaaS product you could do the following, “free trial click” = 5, “pricing page view” = 10, “free trial sign up” = 50. This still allows you to score on-site actions without having to implement a data layer and it can be useful for accounts that get a small amount of conversions (usually B2B with few but high-value leads).