Nova SaaS (Enterprise B2B) North America + Europe 11 months
SaaS Demand Generation
68% lower CAC, 2.4x SQL rate
An enterprise B2B SaaS platform was generating leads but not pipeline. MQL-to-opportunity conversion was under 8%, sales was frustrated by low lead quality, and marketing was optimizing to form fills that never became revenue. Ransen rebuilt the demand engine — full-funnel LinkedIn + Google Ads with ABM overlays, offline conversion imports, and a lifecycle model tied to CRM revenue.
-68%
CAC reduction
2.4x
SQL rate
3.9x
Pipeline / $ ad spend
-22 days
Sales cycle
The Challenge
- Lead volume looked healthy but under 8% converted to opportunities — sales was quietly ignoring inbound.
- Meta was cannibalizing intent-driven Google traffic; blended CAC looked stable but hid a 40% waste layer.
- LinkedIn Ads was optimizing to form fills, not pipeline; the algorithm was scaling the wrong outcomes.
- Attribution stopped at first click and marketing had no visibility into which programs generated closed-won revenue.
Our Approach
- Rebuilt the ICP definition with sales, then built matched-audience lists in LinkedIn Ads covering 4,800 named target accounts.
- Wired HubSpot ↔ LinkedIn Ads offline conversion import for MQL, SQL, opportunity, and closed-won events with click-ID stitching.
- Split Google Ads into brand, competitor, non-brand-commercial, and problem-aware search campaigns. Fully sunsetted Meta prospecting.
- Deployed a lifecycle attribution model in BigQuery + Looker joining ad platform, HubSpot, and Salesforce data.
The Results
- CAC dropped 68% within two quarters — driven by killing the Meta prospecting waste layer and refocusing LinkedIn on target accounts.
- SQL rate lifted 2.4x once LinkedIn started optimizing toward SQL events instead of form fills.
- Pipeline generated per ad-spend dollar climbed to 3.9x industry benchmark by month 8.
- Sales cycle compressed 22 days on average, driven by better lead qualification and more contextual outreach.
LinkedInABMAnalytics
