For market intelligence SaaS companies, data is the foundation of the product. Insights, dashboards, and analytics all depend on high-quality, structured, and timely data.
Yet for many SaaS teams, building and maintaining the data infrastructure internally becomes a costly bottleneck. Engineering resources are diverted, servers and proxies require maintenance, and scaling data coverage to new markets slows innovation.
This case study explores how a leading Market Intelligence SaaS partnered with Grepsr to:
- Reduce data acquisition costs by 62%
- Scale data coverage across multiple markets without adding infrastructure overhead
- Improve data reliability, accuracy, and refresh cycles
- Free engineering teams to focus on core product development
By treating data extraction as strategic infrastructure rather than an operational headache, the company transformed its growth trajectory, accelerated product development, and delivered more timely insights to clients.
The Challenge: Internal Infrastructure Was Limiting Growth
The SaaS company’s platform aggregated competitive, pricing, and market data from hundreds of sources to power client dashboards. Initially, the team relied on in-house scraping scripts and on-premise servers.
However, scaling data coverage and maintaining reliability presented significant challenges:
- High engineering costs: Dev teams spent significant time maintaining scripts, proxy servers, and monitoring pipelines.
- Frequent downtime: Websites changed structures or deployed anti-bot measures, causing repeated extraction failures.
- Limited scalability: Adding new sources or expanding into international markets required considerable engineering effort.
- Delayed insights: Analysts spent hours cleaning and normalizing raw data, slowing decision-making for clients.
“Our engineers were tied up fixing scripts instead of building product features. It was stalling our roadmap,” said the CTO.
“We were spending more time maintaining infrastructure than actually delivering value to clients,” added the Head of Data Engineering.
The company realized that relying on internal systems for data acquisition was limiting both speed and quality, threatening their ability to compete in a crowded SaaS market.
Why Traditional Approaches Were Insufficient
Many SaaS companies attempt to build data infrastructure internally. At small scales, this can work. But at enterprise scale, these approaches face serious limitations:
- Expensive servers and proxy networks
- Continuous engineering cycles for script maintenance
- Errors in raw or normalized data
- Slow onboarding of new data sources
- Reduced operational agility
“The internal approach worked for a handful of sources, but it quickly became unsustainable,” said the Product Lead.
The company needed a managed, scalable solution that could deliver accurate, structured data across multiple markets while minimizing overhead.
Why Grepsr Was the Strategic Choice
Grepsr was selected as a partner for its ability to provide fully managed, enterprise-grade data pipelines that aligned with business outcomes.
Key differentiators included:
- Turnkey Managed Extraction: From hundreds of sources, including complex websites and dynamic content, delivered as structured datasets.
- Self-Healing Pipelines: Automated adjustments when websites change, reducing downtime and manual intervention.
- Analytics-Ready Data: Normalized and clean, ready to feed dashboards, machine learning models, and reports.
- Scalability: Ability to expand coverage across new sources and international markets without additional engineering effort.
- Strategic Partnership: Grepsr acted as an extension of the SaaS team, accountable for uptime, quality, and delivery.
“Grepsr allowed us to scale our data coverage without adding headcount or servers. It felt like an embedded partner rather than a vendor,” said the CTO.
Implementing a Managed Data Pipeline
The SaaS company worked with Grepsr to implement a structured, scalable data pipeline:
Step 1: Data Source Mapping
The team identified critical sources, prioritizing based on relevance, volatility, and client impact.
Step 2: Structured Extraction and Normalization
Data was extracted from each source and mapped to the SaaS platform’s internal taxonomy, ensuring consistency across markets and dashboards.
Step 3: Automated Refresh Scheduling
Sources with high volatility were updated multiple times per day; others were refreshed daily, optimizing resource usage.
Step 4: Continuous Monitoring & Self-Healing
Grepsr implemented pipelines that automatically adjusted to site structure changes, minimizing downtime and manual fixes.
Step 5: Seamless Integration
Structured data was delivered directly into the SaaS platform’s ETL and analytics systems, reducing time from extraction to insight.
“With Grepsr, we finally focused on analyzing data, not chasing it,” said the Head of Product.
Results: Cost Savings, Scale, and Reliability
62% Reduction in Data Acquisition Costs
By eliminating internal infrastructure and outsourcing extraction, the company reduced total costs significantly.
“The savings allowed us to invest in product innovation and client experience instead of maintaining pipelines,” said the CFO.
Expanded Coverage and Faster Refresh
Automated pipelines enabled near real-time updates across hundreds of sources, supporting faster insights for clients.
Improved Data Accuracy and Reliability
Self-healing pipelines and normalization logic ensured high-quality, consistent data for dashboards and analytics.
Engineering Resources Focused on Core Product
Internal teams no longer needed to maintain extraction infrastructure, freeing engineers to build features and improve the SaaS platform.
“Grepsr became an extension of our team. We could finally scale data coverage without scaling engineering headcount,” said the CTO.
Strategic Insights
- Managed data pipelines reduce operational complexity and cost.
- Automation and self-healing improve reliability, ensuring consistent insights.
- Partnerships unlock scalability without infrastructure overhead.
- SaaS companies can reinvest time and savings into product development and client experience.
Frequently Asked Questions
What is managed data extraction?
A service where a partner handles scraping, normalization, and delivery of structured data, reducing internal engineering overhead.
Why should a SaaS outsource data acquisition?
Outsourcing reduces infrastructure costs, engineering effort, and scaling complexity while ensuring reliability.
Can managed data pipelines scale internationally?
Yes. Partners like Grepsr can extract structured data across multiple countries, languages, and markets.
How does cost reduction occur?
By removing internal servers, proxies, script maintenance, and monitoring, SaaS companies can achieve substantial savings.
Is data quality maintained with managed pipelines?
Yes. Self-healing pipelines, normalization, and QA checks ensure accurate, timely data delivery.
Reclaim Resources, Scale Faster, and Focus on Growth
By partnering with Grepsr, the SaaS company achieved 62% cost savings, expanded data coverage across markets, and freed engineering teams for product innovation.
Managed data extraction transforms data from an operational burden into a growth lever. SaaS companies can scale faster, deliver insights more reliably, and focus on building client value rather than managing infrastructure.
Take the first step toward smarter, scalable, and cost-efficient data acquisition — partner with Grepsr today.
