Marcin Zych oferuje specjalistyczne uslugi MarTech dla branzy Finance & Insurance. In finance bad data = bad decisions = real losses. 90% of analytics projects fail at the data discrepancy stage. Rozwiazuje 4 kluczowych problemow branzowych. Oferuje 3 sprawdzonych rozwiazan. Gotowe systemy: LTV / CAC Engine, Performance Control Room, ROPO / Omnichannel. Stack technologiczny: Meta Ads, Google Ads, Social Media, Google Analytics / GA4, TikTok Ads.
Data quality is the foundation of every decision
In finance bad data = bad decisions = real losses. 90% of analytics projects fail at the data discrepancy stage.
Data quality is the foundation of every decision
Formularz kontaktowy
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Odpowiem w ciągu 24h. A może umówmy się od razu?
How I can help you
Choose one area or a full system. No "enterprise" and no six-month wait.
Problems I solve
Typical Finance & Insurance challenges I help with.
Reports don't match
GA4 ≠ Google Ads ≠ CRM ≠ Core Banking. Every system has its own "truth". Which one is correct?
1 customer = 3.2 records
Duplicates, different IDs, no cross-device connection. Single Customer View is a myth.
Compliance and audit
The regulator asks for the data source. You don't have a single version of truth you can show.
Reporting delays
24-48h old data. In a crisis you need real-time, but you have yesterday's snapshots.
Transformation
What changes after implementing MarTech solutions.
- 1 customer = 3.2 records
- Reports don't match
- Yesterday's data (T+1)
- No audit trail
- 1 customer = 1 record (99% match)
- Single source of truth
- Real-time dashboards
- Full lineage for audit
Solutions
Proven approaches I apply in projects.
Customer Identity
Deterministic + probabilistic matching. 1 customer = 1 record. Across devices, across channels.
Single Source of Truth
BigQuery as central repository. Governance, lineage, KPI definitions.
LTV & Risk Modeling
Customer value modeling with risk adjustment. Data-driven segmentation.
Ready systems for Finance & Insurance
Proven solutions ready to deploy. Click a system to see the demo.
"People like this make up maybe 10% of any team. You don't need to tell him what to do. He sees the problem, analyzes it, proposes a solution and implements it."— E-commerce Director, Fashion Retail
Collaboration process
Short. Concrete. No month of workshops about nothing.
Data Audit
Source inventory, quality assessment, gaps
Data Model
Schema, KPI definitions, governance
Identity Resolution
Matching rules, deduplication
Single Source of Truth
BigQuery + data lineage
Dashboards + Alerts
Power BI with anomaly detection
Data a challenge in finance?
Let's talk about data quality