Automating Bookstore Email Lead Generation for Safar Publishing via n8n
In the rapidly evolving publishing industry, timely access to relevant business contacts is a crucial driver of sales and partnerships. The ability to quickly identify and connect with potential distributors, retailers, and bookstores can significantly impact a publisher’s ability to expand into new markets and sustain a competitive edge. Traditional methods of building contact lists, manual searches, cold calls, and reliance on outdated databases, are no longer efficient in a digital-first business environment.
Safar Publishing, a growing name in the publishing sector, recognized this reality. The company aspired to expand its distribution network by targeting independent bookstores and retail outlets across multiple cities and countries. However, the sales team’s reliance on manual research via Google Maps and other online directories presented both operational inefficiencies and limitations in scale. The process often took days to compile even a modest list of prospects, and the quality of data was inconsistent, with duplicates and irrelevant contacts undermining outreach campaigns.
This case study explores how Safar Publishing adopted a custom-built n8n automation to extract bookstore contact information—particularly email addresses, directly from Google Maps. This solution streamlined their lead generation process, improved data quality, and freed up valuable resources for more strategic marketing and relationship-building activities.
Problem Statement
Before the automation project, Safar Publishing faced a set of interconnected challenges that hindered its outreach effectiveness:
Manual Data Collection Inefficiencies
Researching bookstores on Google Maps and extracting their contact information was labor-intensive. A single staff member could spend 5–6 hours compiling a list of 50–100 contacts, which was insufficient for scaling operations.
Inconsistent and Incomplete Data
Manual copying often led to missing or incorrectly recorded information. For example, websites were sometimes recorded without the correct domain suffix, and emails were occasionally skipped if buried deep in the site’s structure.
Duplicate and Irrelevant Contacts
Many bookstore chains had multiple branches with the same central contact email. Without a robust filtering system, duplicate entries inflated the database and wasted outreach capacity.
Low Relevance of Generic Emails
A significant portion of collected emails were generic addresses (e.g., info@bookstore.com) rather than direct decision-maker contacts, reducing the conversion potential of outreach campaigns.
Lack of Real-Time Updates
As data was collected manually, it quickly became outdated. New stores opened, and old ones closed, making the existing contact database obsolete without frequent refresh cycles.
The absence of an automated, scalable system meant that Safar Publishing’s lead generation efforts were bottlenecked, with growth dependent on increasing manpower rather than improving process efficiency.
3. Objectives
The project’s primary objectives were as follows:
Scalability: Develop a system capable of processing hundreds of Google Maps queries in a single run without human intervention.
Data Accuracy: Ensure collected data is clean, relevant, and up to date, reducing bounce rates and irrelevant outreach.
Time Efficiency: Reduce lead collection time by at least 80% compared to the manual process.
Automation of Data Delivery: Store collected leads in a centralized, easily accessible platform (Google Sheets) for instant use by the marketing and sales teams.
Customizability: Allow for quick adjustments in queries, filtering rules, and output formats to target different geographical locations and business categories.
4. Methodology
4.1 Approach
A workflow was designed in n8n, an open-source workflow automation tool. The choice of n8n was strategic—it offers flexibility, a wide range of integrations, and the ability to build custom scraping logic without extensive hardcoding.
4.2 Data Source
Google Maps was selected as the primary source for bookstore discovery due to:
Its extensive, regularly updated business listings.
Rich metadata, including addresses, websites, and contact details.
4.3 Data Collection Process
The workflow was structured to:
Take a predefined list of search queries (e.g., “bookstore in london”, “independent bookshop in London”).
Retrieve Google Maps search results for each query.
Extract business website URLs from the search results.
Visit each website to locate email addresses.
4.4 Tools and Techniques
n8n Workflow Nodes for automation and data parsing.
Filtering Algorithms to remove duplicate and irrelevant URLs.
Regex Patterns to identify valid email formats.
Google Sheets Integration for storing results in real time.
Execution Delay Controls to prevent triggering rate limits from Google Maps.
4.5 Testing & Validation
Before full-scale deployment, the workflow was tested with a small set of 10 queries to verify:
The accuracy of extracted emails.
The removal of duplicates.
The correct population of the Google Sheet.
5. Findings / Analysis
During initial trials and analysis of the collected data, several key observations were made:
Volume of Leads: A single batch of 50 queries yielded over 1,200 unique bookstore email addresses.
Data Relevance: After filtering, ~78% of emails were determined to be relevant for outreach (excluding generic or irrelevant addresses).
Speed: What previously took a team member a full workday was now achievable in under 30 minutes.
Pattern Insights: Certain regions showed a higher density of independent bookstores with public contact emails compared to others. For instance, urban areas with strong literary cultures (e.g., Portland, Edinburgh) had significantly more accessible leads.
A data snapshot revealed that ~15% of Google Maps listings either lacked a website or had outdated contact details. This insight allowed Safar Publishing to refine its queries to focus on regions and categories with higher-quality results.
6. Solutions / Implementation
The automation solution was implemented in a phased manner:
Workflow Design
Built a modular n8n workflow consisting of:
Query loop execution
Google Maps search integration
URL extraction
Web scraping for email extraction
Filtering and deduplication
Google Sheets export
Custom Filtering Rules
Implemented keyword-based email filtering to prioritize addresses containing words like orders@, manager@, or contact@ while optionally excluding overly generic addresses.
Error Handling & Logging
Added nodes to log failed URL requests or scraping errors for review, ensuring no potential lead source was permanently lost.
Execution Scheduling
Configured the workflow to run weekly, automatically refreshing the lead database without manual intervention.
Google Sheets Integration
Connected the workflow to a central Google Sheet accessible to both the sales and marketing teams, ensuring immediate use of fresh leads.
7. Results / Outcomes
The deployment of the automation yielded measurable business benefits:
Time Savings: Lead collection time reduced by 92%, allowing the team to redirect resources toward active sales and relationship building.
Lead Volume: The average weekly output increased from ~150 contacts (manual) to over 1,500 contacts (automated).
Data Quality: Duplicate rate dropped to under 2%, and bounce rates on outreach emails decreased by 35% due to more accurate data.
Scalability: The workflow can handle thousands of queries with minimal configuration changes, enabling expansion to international markets.
Example: In a single week, Safar Publishing was able to prepare a targeted outreach campaign for 800 independent bookstores across five cities—something that would have previously taken over a month.
8. Challenges
While the project was successful, several challenges emerged during implementation:
Google Maps Rate Limits
Initially, the workflow triggered Google’s automated bot detection, halting data collection. This was resolved by introducing adjustable delays between queries and limiting concurrent requests.
Website Structure Variations
Many bookstore websites had non-standard structures, making automated email extraction challenging. Custom parsing rules and fallback scraping strategies were implemented to address this.
Data Irrelevance
Some extracted emails belonged to unrelated entities hosted on the same domain (e.g., a coffee shop within a bookstore). Enhanced filtering rules minimized this issue.
Ongoing Maintenance
As websites and Google’s interface evolve, periodic updates to the workflow logic are necessary to maintain accuracy.
9. Conclusion & Learnings
The n8n-based automation significantly transformed Safar Publishing’s lead generation capabilities. The project underscored the importance of:
Automating repetitive tasks to free up human resources for high-value activities.
Prioritizing data quality over quantity for improved outreach effectiveness.
Building flexible systems that can adapt to changes in data sources and business needs.
From a broader perspective, the case highlights that in modern sales operations, the ability to rapidly acquire and refresh data is as critical as the outreach strategy itself.
10. Future Scope
The success of this project opens several potential avenues for enhancement:
Integration with CRM Systems – Directly syncing leads into platforms like HubSpot or Salesforce for automated campaign triggers.
Geolocation-Based Targeting – Filtering leads based on proximity to specific cities or event locations.
Additional Data Points – Expanding scraping to collect phone numbers, owner names, and social media links.
AI-Based Email Validation – Integrating real-time verification to ensure deliverability before outreach.
Multi-Source Data Collection – Supplementing Google Maps data with information from business directories, literary event listings, and publisher associations.
By continuing to refine and expand the system, Safar Publishing can maintain a competitive advantage in bookstore outreach and distribution network expansion.