Portfolio kompetencji Marcina Zycha dla stanowiska Advanced Analytics Specialist. I build analytics systems (BigQuery, Power BI) and LTV models for companies with millions of transactions. Data quality from 70% → 99%. From data audit to dashboards to adoption. Kluczowe wyniki: 104 projects to production, 16+ years of experience, 1M+ customers in LTV system (ETOS SA).

Position

Why do your reports disagree?

Twój dział raportuje 3 różne liczby na to samo pytanie. Wiem jak to naprawić.

Advanced Analytics Specialist

I turn data into decisions — 99% data quality, LTV system payback in 13 days.

104 projects to production
16+ years of experience
1M+ customers in LTV system (ETOS SA)
13 days LTV system payback
Book a 30-min consultation View CV
References available on request. Certifications: IAB DIMAQ Professional, Google Analytics Advanced, IT Project Manager (Laba), Business Analyst (Laba).
The value I bring

What sets me apart

13 days LTV system payback

Business first

I start with the business problem, not the tool. A dashboard is a means, not an end.

ROI analysis before the project, not after. Business metrics — not just technical ones.
104 completed projects

From data to decisions

From data audit through modelling to a working dashboard. I don't leave a project at "the data is in BigQuery".

104 projects taken to production
50+ MarTech projects

Pragmatic approach

I choose the simplest solution that works. Sometimes that's Power BI, sometimes Excel, sometimes Python.

Proven solutions instead of over-engineering
15+ Power BI clients

Adoption included

A dashboard without users is a failure. Training and documentation are part of every project.

Data literacy training, self-service analytics

I don't sell technology. I solve business problems.



Who is it for?

Industries where I have documented results

E-commerce ROAS, cart abandonment, LTV
Retail / Omnichannel Single Customer View, ROPO
Performance Marketing Attribution, ROAS, CPO
FMCG Trade marketing, promo profitability
B2B / Tech Pipeline, intent data, CAC
Finance & Insurance Data quality, identity resolution

LTV System — Customer Lifetime Value analysis for 1M+ customers

No visibility into actual customer value made it impossible to optimise the marketing budget

8 weeks ROI: 13 days
Metric Before After Change
Customers in system 0 1M+ ∞
Reporting time 8h/week 15 min/day -97%
Data quality 70% 99% +29pp
System payback — 13 days ✓
Tech stack:
BigQuery Power BI SQL Python ECDP Integration

Key takeaways

Data quality matters more than quantity — 80% of time on data cleaning RFM segmentation delivers quick wins before full LTV ECDP integration is key for data activation

My analytics methodology

A repeatable process from discovery to production

Phase Time Timeline (weeks)
1 Discovery
1-2 wks
2 Data Audit
1-2 wks
3 Modeling
2-3 wks
4 Build
3-5 wks
5 Validation
1-2 wks
6 Optimisation
ongoing
MVP Ready Production
0 2 4 6 8 10 12 14 wks
Typical time to production: 10-14 weeks (with a working prototype after 4-5 weeks)

Change management

Technology without adoption is a cost, not an investment. Every analytics deployment is a cultural change project.

ADKAR Framework Change management model
100% Adoption rate
~10 wks Full cycle
Wk. 1-2
A

Awareness

Awareness

Wk. 2-4
D

Desire

Desire

Wk. 4-8
K

Knowledge

Knowledge

Wk. 6-10
A

Ability

Ability

Wk. Ciągłe
R

Reinforcement

Reinforcement

Awareness
"Why do we need better data?"
  • Board presentation with business case
  • Dashboard demo on real company data
  • Competitive and market trend analysis
Desire
"What's in it for me?"
  • "Day with data" workshops for teams
  • Identifying data champions
  • Quick wins in the first 2 weeks
Knowledge
"How to read dashboards and draw conclusions?"
  • Power BI training for users
  • Documentation with examples
  • Office hours and Q&A sessions
Ability
"Can I analyse data independently?"
  • Supervised practice with feedback
  • Checklists and ready-made reports
  • Peer mentoring programme
Reinforcement
"How to sustain a data-driven culture?"
  • Adoption metrics in the dashboard
  • Recognition programme for power users
  • Iterative improvements based on feedback

Anti-patterns to avoid

Mistakes that torpedo analytics implementations
Big bang deployment without a pilot
Dashboard dump without context
No data owner for each report
Ignoring team data literacy

Relevant experience

Roles and projects related to this area

2024 - 2025

ETOS S.A. / Diverse

Performance Marketing Coordinator

Deployed LTV system for 1M+ customers. Power BI dashboards for management. 6.5M PLN budget management.

  • LTV system for 1M+ customers
  • Team ROI 2308%, payback 13 days
  • 6.5M PLN budget
2018 - present

Marcin Zych

Analytics & MarTech Consultant

50+ analytics projects for e-commerce and retail. BigQuery, GA4, attribution and LTV models. Server-Side Tracking with 70%→99% data quality.

  • 50+ analytics projects
  • BigQuery + GA4 + attribution
  • Data quality 70%→99%
2022 - 2023

Yetiz Interactive

Senior Facebook Ads Specialist

Built Power BI analytics models for 15+ agency clients. ROAS optimisation +40% through multi-channel attribution.

  • Power BI for 15+ agency clients
  • ROAS +40% via multi-channel attribution
  • Client report automation
2019 - 2022

OMNIOXY S.A.

Performance Manager / Technical Marketing Manager

Advanced marketing data analysis. GTM and Data Layer architecture. Power BI reporting. 2M PLN budget.

  • Advanced marketing data analysis
  • GTM and Data Layer architecture
  • Power BI reporting
16+ years of experience
104 completed projects
1M+ customers in LTV

Interested in working together?

Book a free 30-min consultation — a concrete problem, a concrete answer, no commitment.

Free 30-min consultation

A concrete business problem, a concrete answer. No commitment.

Book a 30-min consultation Download CV (PDF)
Open to opportunities

LinkedIn

Full career history, recommendations and professional activity.

LinkedIn
Available now B2B or Employment Warsaw / Remote