10 Metrics I Use To Analyze Paid Search Performance

Paid search is often the first paid media channel that a marketing team will test because they can target people who are actively looking for the product/service they offer.

While getting a Google Ads account up and running is fairly straightforward, measuring performance can require a bit more nuance. Simply relying on in-platform conversions might not provide advertisers with the level of insight they need to make impactful optimizations which drive business results.

Despite Google Ads being able to provide a seemingly never-ending supply of data, the core metrics that I focus on to evaluate paid search campaign performance are:

• Spend

• Leads

• CPL

• SQLs

• Cost per SQL

• Pipeline

• Pipeline/spend

• Revenue

• Revenue/pipeline

• ROAS

Notice how only one of these metrics is actually pulled from Google Ads, the rest are sourced from a CRM.

Yes, other data points like CTR and CPC can be helpful to improve performance, but for the sake of this post I’m focusing on high level analysis on business impact (revenue and profitability).

By comparing paid search spend against down funnel metrics like SQLs, pipeline, and revenue, I can determine the genuine effectiveness of the campaigns that I’m running.

An example might help to highlight this process.

I recently started working with a new client, and the majority of their paid media investment over the course of this year has been allocated to paid search. Overall performance was solid, but with a dozen or so campaigns running, I wanted to dive into performance at the campaign level.

Initially, a handful of nonbrand campaigns appeared to be strong performers. Lead volume was healthy, and in some cases CPL was actually more efficient than the brand campaign. If I stopped my analysis here, I might have decided to continue investing a large portion of budget into these nonbrand campaigns.

However, when digging deeper I found that the conversion rate from lead to SQL for these campaigns was very low. As a result, these nonbrand campaigns barely generated any pipeline, making it basically impossible for them to be profitable. This completely changed how I started to develop an optimization plan.

Now, instead of continuing to invest a solid chunk of budget in these campaigns, I’ve started pulling back and running deeper analyses on why lead quality was poor. My approach from here is to focus on what I can do to help this client improve both their targeting and messaging for these nonbrand themes.

These 10 data points are what I rely on to get an accurate sense of campaign contribution to revenue and profitability. The insights this data provides helps me to focus on high-impact work.

Have questions, considerations, or critiques? I’d love to hear them! If you’re reading this via email, just hit respond. Otherwise, you can find me on LinkedIn.