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Project Duration
4 months
Client Industry
Telecommunications
Target Markets
United States

Blink SEO

Comprehensive SEO Reporting and Analytics Platform for Digital Marketing Agency Clients

Technology Stack

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Client Overview

Challenges in Modeling Subscription and Business Metrics

Blink SEO is a digital marketing agency offering specialized SEO services to clients across various industries. They manage multiple campaigns and datasets per client and needed a unified analytics infrastructure. Their focus is on delivering actionable SEO insights by combining crawling, keyword, and backlink data through a centralized platform.

Solutions Delivered

Unified SEO reporting and insights Automated daily data ingestion pipelines

Custom Looker dashboards for clients Scalable Google Cloud-based infrastructure

Team Composition

Data Engineer

Cloud Architect

BI Developer

Project Manager

Engagement Type

Contractual hiring

Key Challenges

Scaling Challenges in Multi-Client Marketing Data Management

Data Consolidation

Fragmented data across multiple clients and third-party tools like ScreamingFrog, DataForSEO

Operational Complexity

High manual effort in maintaining reports and handling campaign-level data at scale

Scalability

No centralized warehouse for unified processing and client-level reporting

Strategic Roadmap

To build a scalable and efficient data foundation, we must first consolidate fragmented client and tool data into a unified schema. Automating extraction from APIs like ScreamingFrog and DataForSEO is key. Aligning dashboards to client-prioritized KPIs—such as keywords, SERP rankings, and backlinks—ensures relevance as the architecture scales with growth.

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To support multi-client SEO operations at scale, we built modular ETL pipelines using Python-based Cloud Functions for efficient and automated data processing. For high-volume ingestion tasks, we deployed scalable virtual machines on Compute Engine to handle API-based data collection from tools like ScreamingFrog and DataForSEO.

Execution Approach

Modular, Scalable Infrastructure for SEO Data Pipelines

To support multi-client SEO operations at scale, we built modular ETL pipelines using Python-based Cloud Functions for efficient and automated data processing. For high-volume ingestion tasks, we deployed scalable virtual machines on Compute Engine to handle API-based data collection from tools like ScreamingFrog and DataForSEO.
All processed data is stored in BigQuery, creating a centralized warehouse that supports seamless querying and cross-client analysis.
On top of this foundation, we designed customized Looker Studio dashboards tailored to each client’s SEO KPIs—such as keyword rankings, backlink profiles, and SERP visibility—ensuring accessible, real-time insights that align with marketing goals and performance tracking.

BUSINESS IMPACT

Timely and Unified Reporting
Operational Efficiency Gains
Enhanced Data Accuracy
Stronger Client Relationships

40%

increase in reporting efficiency across all clients

35%

boost in data accuracy through automation

50%

faster dashboard delivery using modular templates