Client

Leading Semiconductor Manufacturer

Project

Marketing Performance Intelligence with Sigma and Snowflake

Sector

Semiconductor

Geography

USA

Customer story

Unifying Marketing Data to Close the Gap Between Spend and ROI

Client

Leading Semiconductor Manufacturer

Project

Marketing Performance Intelligence with Sigma and Snowflake

Sector

Semiconductor

Geography

USA

THE SITUATION

Marketing teams were merging data from Meta, Google, and LinkedIn manually. That fragmentation drove inconsistent ROI reporting and high manual overhead every time someone needed a cross-channel view.

The bigger cost was timing. Budget optimizations during active campaign phases were delayed, because no one had a live, unified picture of what was working until the manual reconciliation was done.

THE SOLUTION

GrowthArc was brought in to remove the reconciliation step entirely, not just speed it up. The governing principle: if every channel feeds a single live source, there’s nothing left to merge, and nothing left to delay reporting.

The approach centered on connecting Meta, Google, and LinkedIn spend data directly into Snowflake, then building a standardized reporting layer on top of it in Sigma. Three views were prioritized: cross-channel ROAS against revenue, audience segmentation through conversion heatmaps, and AI-driven “What-If” projections for spend scenario planning.

WHAT WE BUILT

GrowthArc built a Sigma dashboard with a live, standardized connection into Snowflake. Meta, Google, and LinkedIn spend streams were unified directly within the Snowflake architecture, replacing the manual merge process with a single connected data layer.

Cross-channel attribution and standardized ROI metrics run against this live data, so every report reflects current state without anyone running an extract. Governance was handled through Snowflake’s existing security model. Role-based data visibility carried over directly, so different teams see what’s relevant to them without a separate access layer bolted on top.

Before / After — GrowthArc
Before
After
Marketing data merged manually across Meta, Google, and LinkedIn
+ Spend streams unified directly within Snowflake, manual merging eliminated
Inconsistent ROI reporting across channels
+ Standardized ROI metrics with live ROAS tracking, consistent across all channels
Budget decisions delayed during active campaign phases
+ Live drill-down directly in Snowflake, no extracts required
No self-service scenario planning for spend changes
+ AI “What-If” projections let teams simulate revenue outcomes from spend changes in real time
Audience targeting based on fragmented, siloed segment data
+ Conversion heatmaps enable granular, high-value audience targeting and precise budget reallocation

THE OUTCOME

Marketing Ops cut manual data preparation time by 40% once Meta, Google, and LinkedIn spend streams were unified in Snowflake. CMOs and Marketing VPs now get a 360-degree view of cross-channel ROAS through a single dashboard, with no lag between source data and what’s on screen.

The structural shift is the removal of the reconciliation dependency itself. Reporting is no longer something that gets produced on request. The “What-If” projection layer means scenario planning is now part of the day-to-day workflow for growth planning teams, not a separate exercise.

Metric Cards — GrowthArc

40%

Reduction in Manual Data Prep

Marketing Ops eliminated manual merging of Meta, Google, and LinkedIn spend data by unifying all three streams.

360°

Cross-Channel ROAS Visibility

CMOs and Marketing VPs see spend versus revenue across every channel in one live view, with no data latency.

50%

Increase in Adoption

More of the marketing team now uses the dashboard directly, instead of relying on requests to a central team.

Simplifying Complexities, Amplifying Results!

Our mission is to foster progress along the arc of growth for our customers, employees, and society. We lead with architecture and transform using platforms, AI and data technologies. Turbocharge your growth journey with our partnership.

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