Demand Capture - Paid Search Case Study

Last updated on:
September 22, 2023
Demand Capture - Paid Search Case Study

Client: A B2B marketplace business with $20,000-100,000 ACV and 3-6 week sales cycles. 

Our goal was to build out a predictable and scalable paid search channel.

Challenges & Results

A client has reached out to us to help them with their inbound growth. They were in a crisis, their spend was going up, but SAOs (Sales Accepted Opportunities) and Clients were decreasing rapidly.

It was counterintuitive as the number of MQLs was increasing (a great example of why MQLs should never be a goal without conversion rates to the latter stages of the funnel).

The result of our program was Reduced CAC by nearly 70% over a 3-month period while increasing spend by 30%.


  • Fixing attribution, I can’t emphasize enough how important this is for a successful paid strategy. Without proper attribution, there was no way to know which campaigns were generating bad leads and which were generating good ones. The above optimization would be impossible to do without it.
  • Unified conversion flow. A single sign-up experience removes the unnecessary variable from the conversion equation and enables better optimization. Also, moving from subdomain was required to improve attribution.
  • Paid spend reshuffle, removing campaigns that were not contributing towards the bottom line and reinvesting that money into new campaigns.
  • Jobs-to-be-done analysis. This was core in deploying new revenue-generating campaigns.
  • CMS improvements to enable us effectively manage and improve landing page creation in a systemized way.
  • Google Ads optimization, best practices applied.

Detailed implementation

We approached this in a 3 phase process:

Phase 1 - Infrastructure Audit

Our first step was to understand the state of the union and dig deeper into the data, tool stack, and funnel.

This has quickly uncovered huge gaps that had to be remediated immediately.

  • They had multiple application forms, however, the MQL status would be assigned only to the users who completed one of the forms, and the other leads would be simply ignored and stuck at stage 1 of the funnel. This was 100% wasted spend.
  • Some of the forms would be hosted in subdomains and would lose any attribution (roughly 60-70% of the traffic would be without attribution). 
  • There was no attribution & tracking system setup beyond a simple Hubspot pixel.

We consolidated the buyer journey under one multi-step form. This has enabled us to add qualifying questions and build an effective lead-scoring system.

We also added a custom first-party cookie to capture UTMs and push this information to the CRM.

This increased attribution accuracy from 30% to 90%+. 

Finally, we rolled out a CMS and template system to build, test and improve landing pages rapidly.

We were now in a position to start making data-driven decisions to optimize paid marketing performance.

Phase 2 - Funnel Analysis

Before our work started in May, you can see that our client's CAC was between $9,000 and $14,000, and overall growth motion had been slowing down: fewer clients and fewer SAOs = less future pipeline.

We approached this in three steps:

Step 1. Increase Lead to MQL ratio

This was our first area to attack and protect spend by removing leads that don’t convert. It was purely wasted spend as these leads never even reached the sales team (nor should they). 

The charts below show the blue bar being very high on the Lead's side but very low on the MQLs side prior to May, which means a lot of paid search spend is being wasted.

June and July are showing much stronger (3x) Lead to MQL conversion rates (chart above).

Step 2. Increase the number of MQLs. 

As we have freed up large amounts of budget, the focus now was to lean into efforts that were bringing MQLs (more details in Phase 3).

During the next two months (May & June), we nearly doubled the number of MQLs, but it uncovered a different issue. 

These MQLs were not converting to SAOs (yellow cells).

Step 3. Increase MQL to SAO rate.

With more data from MQLs now and functioning attribution (as described in the infrastructure section), we have redeployed money toward campaigns that drive SAOs. The result has been a 6 times increase in MQL to SAOs conversion rate.

Phase 3 - Paid Search Optimization

As discussed in Step 1, we removed campaigns that were not generating SQLs. This lowered our cost per SQL by 70% and freed up nearly $100,000 for new campaigns.

Redeploying spend

The final step was to redeploy this money effectively. We carried out client interviews and deal analysis to identify jobs to be done and build our new campaigns effectively around that.

The result has landed us multiple SAOs and a final CAC of just $3,000.

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