Metric Theory’s founding team all hails from a cloud software background, and we have a special expertise in working with B2B technology companies. If optimizing PPC accounts is a mystery where you are trying to solve for what drives ultimate revenue, then the jump from direct sales to B2B lead gen is like going from Hardy Boys to Sherlock Holmes.

For a retail company, there’s no such thing as a bad conversion. Every sale yields dollars for the advertiser. That’s not the case for B2B lead gen companies, where only a very small percentage of leads actually monetize for the company. That doesn’t mean their ultimate goals shouldn’t be the same: generate a strong return on their ad spend and generate revenue from PPC leads.

What makes the mystery more difficult to solve, is that unlike ecommerce where conversions = sales, in B2B it’s more difficult to see ultimate revenue and ROAS numbers tied to individual campaigns, ad groups, and keywords. What is more typical is that B2B companies track information on the referring campaign/ad group/keyword through their landing pages and this data gets associated with the lead in Salesforce. Those leads then take on additional lead statuses as they move through the funnel (i.e. – pre-qualified → qualified → won).

At that point, the advertiser can generate reports determining which campaigns, ad groups, etc. are driving qualified leads and actual sales. Advertising without this data is akin to trying to run an ecommerce account without revenue and ROI numbers.


Step 1 – Data Collection

Set yourself up for success. What data do you want to be able to track? You’ll probably want to break down qualified leads by channel, campaign, ad group, and keyword. There are also ValueTrack parameters to pass through additional information. What is critical is that you determine which hidden fields are set up on your landing page, so that you know how to tag ads appropriately. For example, based on how your LPs are set up, you might need to tag the URL as &campaign=Best-Campaign vs. &utm_campaign=Best-Campaign.

Step 2 – Goal Setting

Just like any paid search account, you should have a target goal in mind. If you have a set budget, then a likely goal is to maximize qualified leads within that budget. You may also have a maximum cost per qualified lead below which you want to maximize leads (think of that like a target CPA).

Some advertisers will be able to provide the anticipated lifetime value (LTV) or annualized contract value (ACV) that gets you closer to a direct ROI number. That said, with many B2B sales organizations, the average amount of time from lead to close is 60-90 days, if not more. That makes the data much less actionable. Additionally, paid search has more direct control over bringing in qualified leads whereas turning those qualified leads into won business is the domain of the sales team. For that reason, we’d recommend setting primary goals around qualified leads.

Step 3 – Reporting

Now that you have your goals, establish what reports are necessary on a daily, weekly, and monthly basis. Since you can’t click into an AdWords profile and see this data tied to your channel metrics like cost, CPC, position, etc. it’s even more important to set up the reports that will allow you to optimize toward your goal.

Step 4 – Optimize!

Let’s take a scenario where a B2B client is optimizing toward a $1,000 cost/SQL (sales qualified lead). First, you are going to want to determine your historical benchmark levels. What is your cost/SQL by month on the account level? Also, what is that at the campaign and keyword levels? Make sure you are also establishing baselines for brand vs. non-brand.

You should be able to use a v-Lookup to pull in your channel cost data and your back-end SQL data into one sheet and calculate cost/SQL metrics. At this point, you are using this data like you would use normal revenue and ROI data for a ecommerce client.

Which campaigns and keywords have an above vs. below average cost/SQL? Independent of bidding, the categories where you have a below average cost/SQL should be ones in which you are looking to expand. Is there more keyword expansion you can do? Are you on search partners? Mobile devices? Can you emphasize new ad copy that generates higher CTRs rather than just the lowest CPA?

The biggest impact can be from bidding strategies. To be most effective, it usually makes sense to categorize campaigns by their conversion to SQL rates. This can be incredibly important for driving stronger performance. I know that our account average cost per SQL is $1,000. I also know that campaigns range from a 5-20% conversion to SQL rate. If Campaign A has a 20% SQL conversion rate, 1 in 5 leads turn into an SQL. That means I can spend $1,000 on 5 leads. This means that for this campaign, I want to bid toward a $200 CPA or lower.

If Campaign B has a 5% conversion to SQL rate, then 1 in 20 leads turn into an SQL. That means in this campaign, I can only pay $50 per lead to stay under that $1,000 cost/SQL. Keep in mind, if you weren’t looking at SQL data, you might be bidding down a keyword in Campaign A with a $175 CPA and bidding up a keyword in campaign B with a $75 CPA. This would have an adverse impact on your business.

Metric Theory establishes tiers (using AdWords labels or dimensions in a platform like Kenshoo) for Tier 1 vs. Tier 2 vs. Tier 3 keywords. This allows us to still bid toward a CPA, but one that is adjusted based on that campaign’s likelihood of delivering an SQL.

Campaign Set

Conv to SQL Rate

CPA Threshold

Tier 1

> 15%


Tier 2



Tier 3

< 10%


There are certainly other setups and you can get more granular with your campaign sets (down to ad group or keyword level sets), but campaigns should give you enough aggregate data to make more intelligent bidding decisions.


Considerations Outside of PPC

Sometimes an increase or decrease in SQLs gets wrongly blamed (or credited) to PPC efforts. When you see bigger swings, there are a number of questions it makes sense to ask before landing on the conclusion that it was PPC related:

– Did the sales organization change?
– Did they fire or hire new sales people?
– Did the enterprise sales team start handling more SMB leads or vice versa?
– Did they change the way they classify an SQL?
– Did they add/subtract other marketing channels?


Final Thoughts

B2B PPC optimization using back-end data should be viewed a bit like driving in snow. Avoid sharp and frequent turns! The time between a conversion and a SQL can have some lag and there are an increased number of outside factors that can impact performance. There’s also only a limited amount of total data around SQLs and especially actual sales, so you are not relying on a whole lot of information.

I’ve had clients who saw one big deal come in from a keyword category that had otherwise been a very poor producer of conversions and SQLs. The client wanted to triple the budget on that keyword category because “It’s driving the highest ROI by far this month!” Don’t do that.

Come in with a plan, grab your Sherlock hat and pipe, and make wide turns guided by the data and you will be able to master optimizing B2B PPC accounts.