Case Study

Increasing Sales-Qualified leads by 139% while slashing Cost Per Lead in half for a SaaS product

  • Paid Search, Google Ads

Download Summary

The Challenge

Vectorworks came to us looking to solve a big problem – really high costs per sales-qualified lead ($1,000+ on bad months). In addition to the high costs, the leads coming in were mostly marketing-qualified leads that weren’t moving down to the sales-qualified stage. Clearly, there was some misalignment between the customers Vectorworks wanted and who was showing up through Google ads. Time to go to work!


The Process


We started with a deep dive into the audience Vectorworks was targeting. This allowed our team to develop a stronger keyword strategy and ad copy that could speak to the specific industry vertical and ideal customer (e.g. architecture professionals, not DIYers looking to redesign a room).

From there, the team focused on getting indicators of quality into the Google Ads platform, deciding on importing the sales-qualified stage data from their CRM directly into the platform. The team then was able to more effectively test using advanced bidding strategies moving from manual cost-per-click to conversion-focused Smart Bidding strategies taking advantage of Google’s advanced bidding platform.

This match of tailoring our keyword and copy to the ideal consumer paired with the data-backed efforts of importing CRM data and utilizing bidding strategies that focus on those imports, we were able to see year-over-year growth. Thanks to these strategies, Vectorworks increased the ads budget allowing the team to maximize the targeting efforts improving the CPLs even further.

 

The Results

Optimizations and strong ads strategies paid off. Sales-Qualified Leads (SQL) improved by 139%, and customers improved by 118%.

 

While extra budget was a factor starting in May of 2024, we were still able to get more for less by improving our cost per sales-qualified lead by 54% and improve our cost per customer by 21%

The proof is in the numbers

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SQL volume improvement

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customer volume increase

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drop in Cost Per SQL

%
decrease in Cost Per Customer

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