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Project Duration
4 months
Client Industry
Marketing and advertising

Target Markets
USA

Locallogy

Automated SEO Audit and Reporting Platform for Growing Local Marketing Agency

Technology Stack

No items found.

Client Overview

Challenges in Modeling Subscription and Business Metrics

Locallogy is a digital marketing agency specializing in SEO and online visibility services for small, local service providers across the U.S. Their focus lies in delivering actionable marketing strategies, competitive intelligence, and technical audits for websites with lean footprints but high local impact. The agency relies heavily on data to deliver monthly performance reports and tailor optimization strategies.

Solutions Delivered

Unified SEO data from multiple tools Automated audits across key SEO dimensions

Dynamic reporting to Looker and Sheets

Editable interface for manual data input

Team Composition

Data Engineer

Cloud Architect

SEO Analyst

Product Manager

Engagement Type

Contractual hiring

Key Challenges

Challenges in Unifying and Scaling SEO Data Management

Fragmented SEO Data

Disparate tools like GA4, GSC, ScreamingFrog, and Ahrefs lacked a unified view

Manual Reporting

Tedious workflows across spreadsheets, APIs, and platforms caused delays

Missing Scalable Audits

Needed technical + content + competitive audits standardized at scale

Strategic Roadmap

To streamline SEO operations, we must centralize data from tools like GA4, GSC, ScreamingFrog, and Ahrefs into a single, unified source. Automating technical and content audits will enable consistent monthly reporting. Selecting a collaborative platform that supports real-time edits and integrates with Looker or ClickUp is essential. Ensuring scalability without added effort will allow seamless onboarding of new clients while maintaining audit quality and operational efficiency.

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To create a scalable and automated SEO audit framework, we built Python-based ETL pipelines that integrated data from ScreamingFrog, GA4, GSC, AWR, and Ahrefs. These pipelines were orchestrated using Google Cloud’s Compute Engine and Cloud Scheduler to automate crawls and data fetches on a regular cadence.

Execution Approach

Automated, Scalable Framework for SEO Audits and Reporting

To create a scalable and automated SEO audit framework, we built Python-based ETL pipelines that integrated data from ScreamingFrog, GA4, GSC, AWR, and Ahrefs. These pipelines were orchestrated using Google Cloud’s Compute Engine and Cloud Scheduler to automate crawls and data fetches on a regular cadence.
Structured data was normalized and stored in BigQuery, creating a centralized source for analysis and reporting.
To support flexibility, we developed a user-friendly interface using Google Sheets and Retool, allowing SEO teams to input ad hoc context, override data when needed, and generate tailored insights. This approach streamlined workflows and improved audit consistency at scale.

BUSINESS IMPACT

Increased Team Efficiency
Scalable Audit Framework
Enhanced Client Communication
Data-Driven SEO Strategy

35%

reduction in manual reporting time through automation workflows

20%

cost savings achieved in data processing operations

25%

increase in client retention with data-driven SEO insights