Marketing Analytics · B2B SaaS · Series B, 8-person marketing team
Marketing Attribution Engine
Built a multi-touch attribution system connecting marketing spend to closed revenue, enabling data-driven budget reallocation and a 10% reduction in customer acquisition cost.
10% reduction in blended CAC within 2 quarters of data-driven reallocation
Identified 3 underperforming channels consuming 31% of budget with 4% of pipeline
Reallocated budget to 2 high-ROAS channels, overall ROAS improved to 3.8:1
First time leadership had a single source of truth for marketing ROI
The problem
A Series B company was spending ₹80L/quarter on marketing with no reliable way to connect spend to closed deals. Budget decisions were based on channel volume (impressions, clicks) not pipeline contribution. Last-click attribution was causing the team to over-invest in bottom-funnel paid search.
The diagnosis
CRM and marketing automation were not connected at the campaign level. UTM parameters were inconsistent. The team was running 12 channels simultaneously with no unified reporting. 'Gut feel' was the attribution model.
System installed
- 01
Audited and standardised UTM taxonomy across all channels and campaigns
- 02
Connected HubSpot campaigns to Salesforce opportunities via custom field mapping
- 03
Built a multi-touch attribution model: first touch, last touch, and linear, selectable by use case
- 04
Created a revenue marketing dashboard: spend, pipeline influenced, opportunities created, CAC by channel
- 05
Ran a 90-day budget reallocation experiment based on the new data
Results
- ▸
10% reduction in blended CAC within 2 quarters of data-driven reallocation
- ▸
Identified 3 underperforming channels consuming 31% of budget with 4% of pipeline
- ▸
Reallocated budget to 2 high-ROAS channels, overall ROAS improved to 3.8:1
- ▸
First time leadership had a single source of truth for marketing ROI