Xforma MQL to SQL case study
Company Executive summary
Xforma is an Italian company doing B2B lead generation focusing primarily on LinkedIn outreach. The provided us data of MQLs in multiple sectors which we were to enrich and provide a weekly update on the companies which are ready to be reached out to. In this article, we will focus on data in the educational sector of around 4500 companies which we transformer into a highly targeted pool of sales-qualified leads.
The Challenge
Xforma.me faced the classic B2B lead generation dilemma: volume versus quality. Their challenges included:
- Data overwhelm: 4,500+ leads requiring manual qualification
- Limited intelligence: Basic LinkedIn data insufficient for proper qualification
- No systematic scoring: Lack of standardized criteria to identify buying intent
- Sales efficiency: Need to focus resources on warm prospects rather than cold outreach
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Our Solution
The pipeline can be divided into three main phases.
Phase 1: Data Enrichment with VAT Intelligence
The initial data provided contained very limited information about the companies.
snapshot of the initial data
Step 1: VAT Number Acquisition We began by building a waterfall enrichment along with specialized validation techniques to obtain their VAT numbers. This crucial step unlocked access to comprehensive European business registry data.
Step 2: Custom Italian Business Registry Scraper We built a sophisticated scraper targeting Reportaziende.it, Italy’s premier business intelligence platform. Our automated system:
- Performs secure login authentication
- Continuously runs in the background
- Extracts comprehensive company data
Scraping in progress
The data scraped is converted to a structured JSON using an LLM call. Enriched Data Points:
- Complete business addresses and postal codes
- Geographic regions and city locations
- Office locations and branch information
- Annual turnover figures (2023 & 2024)
- Profit margins and financial health
- Employee headcount
- Industry classifications
Snapshot of the enriched data
Phase 2: ICP Qualification Engine
On the enriched data, only the companies which specify the required customer profile are kept. We applied filters like:
- Minimum and maximum turnover
- Profit margin requirements
- Company office location
With these filters, the initial 4582 companies were withered down to 1080 companies which satisfy all the ICP conditions and had to be monitored for SQLs on a weekly basis.
Phase 3: Three-Signal SQL Detection
We applied three signals to identify sales ready prospects among the 1080 qualified companies:
Signal 1: Recent Funding Activity
- Monitoring of funding rounds and investment announcements
- Tracking venture capital and private equity activity
- Identification of companies with fresh capital for new purchases
Signal 2: Active Hiring in LinkedIn/Marketing
- Real-time scanning of job postings for LinkedIn and marketing-related positions
- Focus on social media marketing, digital marketing, and LinkedIn specialist roles
- Detection of companies actively scaling their marketing operations
Signal 3: LinkedIn Activity Engagement
- Monitoring CEO posting frequency and content engagement
- Tracking company page publishing activity and post performance
- Measuring overall LinkedIn presence and social selling activity
Live Dashboard Insights and push to CRM
Every week the companies are refreshed for these signals and the client has access to a comprehensive dashboard.
Dashboard provided to client
Individual Lead Enriched Data
The weekly SQLs are also pushed to the companies Hubspot account to their SDR’s for warm outreach.
Key Results:
- 4,582 raw leads processed through our enrichment pipeline
- 1,080 companies met ideal customer profile criteria (23.6% qualification rate)
- 31 SQLs identified last week as ready for immediate outreach (2.9% of qualified leads)
Impact and ROI Analysis
Sales Team Efficiency
Instead of manually researching 4,582 prospects, sales teams now focus on 31 highly-qualified SQLs with:
- Complete financial profiles and business intelligence
- Recent funding and investment activity data
- Active hiring indicators in relevant departments
- Demonstrated market engagement and social selling activity
Conversion Rate Improvement
- Traditional cold outreach: 1–2% response rate
- Our qualified SQLs: Expected 15–25% response rate
- Quality multiplier: 10–15x improvement in prospect readiness
Conclusion
Our MQL to SQL conversion system for Xforma.me demonstrates that intelligent data processing combined with multi-signal detection can transform B2B sales efficiency.
The key insight: in B2B sales, precision targeting beats volume every time. Our systematic approach ensures every SQL represents a real opportunity backed by financial capability, demonstrated need, and market engagement signals. This approach also helps companies to reach out to prospective leads at the right time instead of being too early or too late.