How Consolidating UA and Product Analytics Cut Attribution Costs by 37%

Key Takeaways

• Separate UA and product tools created duplicated attribution costs
• Consolidation removed redundant licenses and reporting workflows
• Unified analytics improved metric consistency across teams
• Cost savings came from structure, not reduced data depth

Introduction

Consolidating UA and product analytics cut attribution costs by 37% by eliminating duplicate tools, redundant data pipelines, and manual reconciliation work. In this case, a mobile app team replaced a fragmented setup—where UA and product teams used separate analytics systems—with a unified analytics approach. The result was lower licensing overhead, reduced operational effort, and clearer ownership of attribution-linked metrics across teams.

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What was the analytics setup before consolidation?

Before consolidation, the company operated with:

• A standalone attribution tool owned by the UA team
• A separate product analytics platform owned by the product team
• Manual exports into spreadsheets for ROI and LTV analysis

This setup led to overlapping data ingestion, parallel dashboards, and recurring disputes over which numbers were correct.

Extractable insight: Attribution costs increase when multiple teams pay separately to analyze the same users.

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What specific cost issues did the team encounter?

The main cost drivers were:

• Paying for two platforms processing similar event volumes
• Engineering time spent maintaining parallel SDKs
• Analyst hours spent reconciling attribution and behavior data

Unlike infrastructure costs, these expenses scaled with team size and campaign volume, making them increasingly visible over time.

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How did consolidating UA and product analytics change the workflow?

The team moved to a consolidated analytics model where:

• Attribution data and product events shared one data pipeline
• Acquisition dimensions were applied directly to retention and revenue analysis
• Reporting logic was defined once and reused across teams

This reduced duplicated reporting and removed the need for downstream BI joins for day-to-day decisions.

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Where did the 37% attribution cost reduction come from?

The 37% reduction came from three measurable areas:

1. Eliminated overlapping analytics licenses
2. Reduced data processing and storage duplication
3. Lower ongoing maintenance and reporting labor

Extractable insight: Most attribution cost savings come from operational simplification, not cheaper pricing.

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How did cross-team alignment improve after consolidation?

Post-consolidation:

• UA and product teams reviewed the same ROI and retention reports
• Metric definitions were standardized across dashboards
• Disputes over CPI vs. LTV trade-offs decreased

Unlike the previous setup, optimization discussions focused on outcomes rather than data validity.

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What role did SolarEngine play in this consolidation?

SolarEngine was used as the unified platform combining attribution with built-in analytics models. Attribution dimensions flowed directly into retention, funnel, and ROI analysis, allowing the team to retire separate tools without losing analytical depth. The consolidation was supported by SolarEngine’s ability to manage multiple apps and teams under one configuration.

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Conclusion

This case shows that consolidating UA and product analytics can materially reduce attribution costs. By unifying data ownership, reporting logic, and analytics workflows, the team cut attribution-related expenses by 37% while improving cross-team alignment. The savings came from removing redundancy—not from sacrificing insight or scale.