Case Study – Diagnosing and Improving Lead Quality for an engineering services company
Client – an engineering services company
Problem – the Before state
Inbound lead quality had tanked.
The client was seeing a dramatic decline in the quality of their inbound sales-ready leads, and they had no idea why!
What made the situation particularly frustrating was that the decline happened abruptly. To add to the confusion, lead quantity wasn’t dropping (it was actually increasing)… it was the low quality of leads coming in that was the issue.
- Were market conditions changing due to the transition to an AI-centric world?
- Had something inside Google Ads drifted off course?
- Something else?
The business wanted some level of clarity, and more importantly, a path forward. They decided they needed an outside perspective from an engineering marketing consultant.
Outcomes
The client and RocLogic collaborated to diagnose the situation – combining observations from sales and marketing with corresponding CRM metrics and analytics data.
The Journey
The effort began with uncertainty and competing high-level hypotheses. Rather than assuming AI search disruption was solely responsible, RocLogic focused on isolating variables and identifying the most likely root causes.
RocLogic collaborated closely with several of the client’s team members. The interactions were friendly, professional, and problem-focused. It was a very positive experience overall, especially given the level of pressure that we were all feeling.
RocLogic approached this challenge as a diagnostic exercise, leveraging a combination of judgment and data-informed evidence.
Lead source data suggested the biggest change in lead quality was within Google Ads.
After assessing:
- Google Ads campaign history,
- conversion tracking,
- keyword strategies,
- and the relationship between Google Ads and HubSpot,
several working theories emerged.
Among the strongest hypotheses was that Google’s optimization systems had become trapped in a feedback loop driven by poor conversion signals.
The analysis suggested that the sudden decline wasn’t behaving like a market shift.
The resulting strategy emphasized testing and continuous iterative learning rather than assumptions.
That practical, test-first mentality helped transform a confusing lead-quality problem into a more methodical optimization effort, built around measurable hypotheses and continuous improvement.
The client implemented a series of corrective actions to test the first working theory.
While there may be additional improvements desired in the future, according to the sales team, lead quality has recovered to a level of quality they’re much more comfortable with.
