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Google Search Ads - The More (Data), The Merrier

Ranga Kaliyur
April 8, 2024
April 8, 2024
Table of Contents

The Challenge With Google Search Ads

Search advertising has established itself as the go-to channel for B2B marketers to capture low-hanging demand — and it’s easy to see why. As a marketer for an account intelligence product such as Factors.ai, it makes sense for me to bid on product keywords such as “ABM software” or “visitor identification tools” and competitor keywords such as “leadfeeder alternatives”, so I can attract relevant, in-market customers based on searcher intent. 

That being said, a closer look at the numbers reveals that conversions from search ads can actually be pretty disappointing (and expensive). For context, the average click-through rate (CTR) for search ads across industries is only about 3.17%. It’s even slimmer in the technology industry, at a meager 2.09% (Wordstream). Out of the few ad impressions that do translate into clicks, the average landing page conversion rate (sign-ups, demo form submissions, etc) is around 6% (HubSpot). And of the handful of visitors who do convert, only a fraction go on to become SQLs, opportunities, and ultimately, customers. 

Even the most optimistic benchmarks find that:

  • Only around 30% of Leads become SQLs
  • Out of which, 40% of SQLs become opportunities
  • Out of which, 30% of opportunities become customers 
A conversion funnel graphic showing declining numbers from Visitors (100) to Deals (0.21).

There are countless reasons for such significant drop-offs along the sales funnel:

  • Most lead that land on your website, won’t sign-up
  • Leads that do sign-up, may not schedule a meeting 
  • Leads that do schedule a meeting, may not show up 
  • Leads that do show up, may not be qualified (non-ICP)
  • Leads that are qualified, may not be sales-ready (timing, budget, etc)
  • Leads that are sales-ready, may choose to go with an alternate solution

All these factors suggest that to earn a single customer from search ads, you’d need more than 500 paid clicks (of course, this number varies widely based on category). That’s a lot of clicks…and a lot of money. 

To solve for this, marketers typically rely on three levers: 

  1. Improve ad performance by optimizing keywords, budgets, etc
  2. Improve website conversions with conversion rate optimization (CRO)
  3. Improve quality of clicks via Google Click ID (GLCID) and conversion feedback

In this article, we’ll be exploring the latter of the three. Specifically, we’ll highlight an improved approach to training Google Ads to find the right clicks and traffic for your business via GCLID and conversion tracking. But first, let’s briefly discuss the current approach to Google conversion tracking — and its limitations. 

Google Conversion Tracking & GCLID: As It Stands

As a B2B marketer, you’re probably familiar with how conversion tracking and GCLID work to share conversion feedback with Google, but here’s a quick refresher:

Not all ad clicks are equal. A buyer that matches your ideal client profile is probably more valuable to your business than a student looking for an internship. However, to Google and other ad platforms, a paid ad click, regardless of whether it's by a buyer, a student, or a competitor, is a paid ad click.

To avoid the risk of burning through budgets on irrelevant paid engagement, Google supports the ability to digest feedback on the quality of clicks based on Google Click ID (GCLID) and preconfigured conversion actions. Via GCLID, Google assigns each click with a unique identifier. If the user behind a specific click goes on to perform a favorable action, marketers can flag that click to Google as a “high-quality lead”. Google’s algorithm then harnesses countless factors and historical records from its own database to surface your search ads to other audiences that match this criteria for a “high-quality lead”. Marketers typically tag sign-ups, MQLs, SQLs, and opportunities as favorable conversion actions. This lead-level feedback improves the quality of audience that receive your ads, which in turn, improves conversions. 

In theory, ad optimization with conversion tracking and GCLID sounds fantastic — a feedback loop between advertiser and advertising platform to continually improve ad performance and conversions. That being said, there are two challenges with Google Conversion Tracking and GCLID as it stands today:

  1. Limited data: Google Ads recommends at least 30 conversions in 30 days for its algorithms to take effect in understanding what’s valuable and what’s not. In fact, for minimum CPA fluctuation and a quick learning period, Google suggests a whopping 500 conversions in 30 days. For early and mid-stage companies that are yet to hit these volumes of conversions, this lack of data can be a limiting factor. 
  2. Lagging metrics: B2B sales cycles are notoriously lengthy and non-linear. After a visitor submits a demo form, for example, it might be a couple of days before their demo call, a few weeks before they become an opportunity, and more than a month before the deal is closed. Given that most marketers prefer quick iterations and experiments to squeeze the most ROI out of their campaigns, these extended periods between conversions lengthens the feedback loop when sending lead-level data back to Google. This lagging lead metrics is another limiting factor. 
A screenshot of a Google Ads forum post discussing conversion strategies and learning periods

With bids and cost per clicks becoming increasingly expensive as a result of growing competition, we need a fresh approach to overcome limitations with lead-level conversion tracking. Our hypothesis? Leverage traffic-level conversions to ensure sufficient, leading data availability for Google to work with.

Traffic-level Conversion Tracking: A Better Approach

Most marketers typically use sign-ups, M/SQLs, or other lead-level conversions as their conversion action goals. However, as noted earlier, only about 6% of visitors typically submit a form, with fewer still converting down funnel, after a delay. This results in small, lagging data sets for Google to work with. 

Rather than sending back lagging conversion data for 6 out of a 100 visitors on your paid landing pages, what if you could send leading data for 60? This is exactly what Traffic-level conversion tracking seeks to achieve via IP-based account enrichment, engagement tracking, workflow automations, and GCLID.  

Here’s how it works

Even though only a fraction of the traffic on your paid landing pages will sign-up, there’s still variable value in the remaining ninety something percent of visitors that are yet to convert. Say that 10 visitors land on your website from a search ad. Out of these 10, 2 are in-market ICP buyers that immediately sign up. 5 are ICP buyers that would make a good fit for your business, but decide that now is not the best time for a demo, so they drop off without submitting a form. And 3 are non-ICP visitors: a student, a job seeker, and a competitor — who also drop off without submitting a form.

Pie chart showing ICP converted (20%), ICP not converted (50%), Non-ICP (30%)

The typical approach suggests sending the 2 ICP visitors that converted back to Google Ads as feedback. While this is helpful, it doesn’t encapsulate the full extent of data collected here. It fails to acknowledge the 5 clicks (50%!)  that albeit didn’t convert but matched our ideal client profile. While these clicks may not be as valuable as the 2 ICP clicks that converted, they’re certainly more valuable than the Non-ICP clicks. If ICP converted is worth $20, ICP not converted could be worth $10, while Non-ICP could be worth $2. This is valuable data for Google to make sense of ad clicks, even in cases where an explicit “conversion action" may not have taken place. By supplying Google with a larger set of relevant data, its algorithms will have a better understanding of what kind of visitors you value most. This data needn’t be limited to ICP data (firmographic) alone; it may be based on engagement (time-spent, scroll%) as well. 

Accordingly, traffic-level conversion tracking seeks to identify, qualify, and feed Google with a larger volume of granular, leading data by de-anonymizing website traffic and engagement at an account-level. This is where an account intelligence tool (*ahem* Factors.ai) comes into the picture.

How Factors Fits In: Your Data + Our Data = Ad Magic

The process we’re exploring here involves identifying website traffic, qualifying that traffic based on their firmographics (for ICP fit) and engagement (for intent fit), and pushing that data back to Google as feedback to attract better, more relevant audiences that *we hope* improves conversions and pipeline. Accordingly, we’ll need the following:

  1. An IP-based intelligence tool to identify and enrich landing page traffic at an account-level 
  2. Assign conversion value to incoming traffic based on your ICP and engagement criteria 
  3. Automate a workflow that pushes this traffic-level conversion data to Google

As luck would have it, Factors.ai supports all three requirements with industry-leading account identification, engagement scoring, and workflow automations. Here’s an example of what a Factors-powered Search ads conversion tracking process could look like:

  1. Identify up to 64% of anonymous companies landing on your website via search ads but are yet to convert 
  2. Qualify and segment identified companies based on firmographics (industry, size, etc) and engagement (time-spent, scroll-depth, etc)
  3. Push traffic-level conversion action data (along with lead-level data) back to Google automatically with the likes of Make, Zapier etc 
  4. Google leverages a larger set of leading data to improve the quality of clicks and traffic
  5. Improved audience quality results in better conversions and cost-effectiveness
A Google Ads conversion summary page highlighting ICP Traffic

Interested to see it in action? We’d be more than happy to set up a similar process for you over a trail with Factors.ai. 


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