noalytixDiscover

Global Marketing Analytics Lead

Noah GomaaClarity beyondthe numbers.

I bring marketing analytics, data science and data engineering together to understand real, sustainable growth.

Explore my work

The whole relationship

Growth is arelationship.

I look at acquisition, retention and customer lifetime value together. The quality of growth matters as much as its size.

Acquisition Retention Value

A useful starting point

Start withthe right question.

I validate definitions, separate facts from hypotheses and reconcile numbers across systems before drawing a conclusion.

See the work

Based in Stockholm, Sweden

A closer look

Additional work.

Reporting, measurement and data quality work. Each example includes my role, approach and what I learned.

Marketing performance reportingWeekly readouts, a rebuilt monthly KPI process and recurring quarterly inputs.3 examples

Weekly performance reporting

Recurring
Context

The weekly view needed to explain net subscriber growth, acquisition, cost efficiency and market level drivers together.

My role

I built and presented the marketing readout and explained the story behind the numbers.

Approach
  1. Bring subscriber growth, acquisition and cost efficiency into the same reporting scope.
  2. Explain market level movements and trace the headline changes back to their underlying flows.
  3. Reconcile the numbers before presenting the interpretation.
Tools
  • Looker
  • BigQuery
Outcome and what I learned

A repeatable weekly readout connecting the headline performance to the flows behind it.

A useful explanation starts with matching definitions and scope, rather than a chart that only shows the movement.

Monthly KPI reporting process

Rebuilt and validated
Context

A recurring KPI report can change as customer cohorts mature, even when the reporting period stays the same.

My role

I rebuilt the reporting process and validated how the recurring metrics should be compared.

Approach
  1. Review the recurring report across its market scope.
  2. Check how cohort maturity changes the reported metrics.
  3. Validate comparisons using the relevant reporting and maturity rules.
Tools
  • KPI reports
  • Reporting extracts
  • Cohort maturity rules
Outcome and what I learned

A rebuilt reporting process with metric maturity considered before the figures are compared.

The extraction point and the age of a cohort are part of the comparison, not just details added afterwards.

Quarterly market reporting inputs

Recurring
Context

Quarterly market reporting needs inputs that follow the same grouping rules each time.

My role

I maintained the recurring input process from web analytics data and documented how the data was grouped.

Approach
  1. Prepare the web analytics inputs for the recurring reporting cycle.
  2. Document the grouping rules used to organise the input data.
  3. Keep those rules attached to the recurring process.
Tools
  • Web analytics data
  • Documented grouping rules
  • Reporting inputs
Outcome and what I learned

A maintained recurring input process with the grouping logic available for review.

A reporting comparison depends on consistent grouping rules as well as consistent numbers.

Measurement framework and roadmapA documented KPI framework and a proposed programme of measurement work.2 examples

Measurement framework and KPI documentation

Documented
Context

Metrics need shared definitions before different reporting and measurement methods can be read together.

My role

I wrote the framework and explained how attribution, incrementality and marketing mix modelling fit together.

Approach
  1. Define the acquisition, retention and customer lifetime value metrics.
  2. Set out the different questions answered by attribution, incrementality testing and marketing mix modelling.
  3. Keep the interpretation of a metric attached to its definition.
Tools
  • KPI documentation
  • Metric definitions
  • Measurement methods
Outcome and what I learned

A written reference linking the metrics to the measurement questions they can help answer.

Attribution credit, estimated response and evidence of incrementality should not be treated as the same result.

Measurement roadmap

Proposed
Context

The measurement work needed a clear set of next questions, covering growth quality as well as immediate reporting improvements.

My role

I proposed the measurement programme and a register of practical improvements.

Approach
  1. Propose an incrementality testing programme and a cohort quality analysis.
  2. Include a trial and offer analysis to examine the quality of acquisition.
  3. Keep a register of practical improvements alongside the longer measurement work.
Tools
  • Measurement planning
  • Experiment design
  • Prioritisation register
Outcome and what I learned

A proposed roadmap covering incrementality, cohort quality, trials, offers and practical reporting improvements.

A measurement roadmap should separate the questions worth testing from the evidence already available.

Data quality and reconciliationOngoing checks on modelling data, metric tiles and dashboard visibility.2 examples

External modelling data reconciliation

In progress
Context

Data supplied to an external modelling partner needs to be compared using matching date and maturity rules.

My role

I am checking the supplied data and investigating the reasons for discrepancies.

Approach
  1. Check which date keys define each reporting period.
  2. Compare extraction dates and the point at which the metrics were observed.
  3. Apply the metric maturity rules before deciding whether two figures should match.
Tools
  • Partner data extracts
  • Date key checks
  • Metric maturity rules
Outcome and what I learned

The reconciliation is ongoing, with discrepancies investigated through their date, extraction and maturity definitions.

Matching a reporting date does not guarantee that two extracts represent the same point in a metric’s life.

Dashboard quality ownership

In progress
Context

Metric tiles and dashboard visibility need careful review so that a reporting change does not create a new interpretation or access problem.

My role

I am working on metric tile corrections and planning the dashboard visibility changes.

Approach
  1. Review the metric tiles that need correction.
  2. Keep metric changes distinct from dashboard visibility changes.
  3. Plan the visibility changes so their effect can be checked before wider use.
Tools
  • Reporting dashboard
  • Metric tile review
  • Visibility planning
Outcome and what I learned

The quality work is ongoing. Tile corrections and visibility changes are being treated as separate changes to review.

A safe dashboard change needs a clear definition of what is changing and who will see the result.

Offline campaign impact analysisI compared traffic and acquisition around a TV campaign across prior periods and channels.Analysed

Context

The question was whether traffic and acquisition changed before and during a TV campaign, and how the pattern differed by channel.

My role

I built the comparison using SQL and GA4 data, then put the observed movements in context.

Approach

  1. Compare the periods before and during the campaign with prior periods.
  2. Review traffic and acquisition by channel within the comparison.
  3. Describe the observed association without presenting it as proven causal lift.

Tools

  • BigQuery
  • GA4
  • Looker
  • SQL

Outcome and what I learned

A descriptive campaign comparison showing how the observed traffic and acquisition patterns varied by period and channel.

A before and during comparison can reveal an association. It does not establish what would have happened without the campaign.

Traffic and site mismatch analysisI analysed country site mismatches in both directions and presented the findings to stakeholders.Presented

Context

Visitors can land on a country site that differs from their recorded location. Looking at only one direction leaves part of the pattern out.

My role

I analysed visitors landing on a different country site and explained the findings to stakeholders.

Approach

  1. Compare the recorded visitor location with the country site they reached.
  2. Inspect both visitors arriving on another country site and visitors from elsewhere arriving on the local site.
  3. Present the pattern and its scope without assuming why the mismatch happened.

Tools

  • Web analytics data
  • Country site comparisons
  • Stakeholder reporting

Outcome and what I learned

A two way view of the country site mismatch, shared with stakeholders for further investigation.

Recorded location and site choice describe different things. A mismatch alone does not explain a visitor’s intent.

Migration journey auditI scoped a GA4 audit working backwards from confirmed purchases to compare journey evidence by device.Scoped

Context

The proposed audit needs a reliable completed purchase path before comparing completed and abandoned journeys.

My role

I scoped the GA4 analysis and the evidence needed for the journey comparison.

Approach

  1. Start with confirmed purchases and work backwards through the completed journeys.
  2. Define checkout attempt evidence and abandonment rules before labelling non purchasing journeys.
  3. Plan device comparisons using a consistent reporting scope.

Tools

  • GA4
  • Purchase confirmation evidence
  • Journey definitions

Outcome and what I learned

A defined audit scope for comparing journey evidence by device. The journey findings still require execution and validation.

The absence of a purchase does not prove abandonment. The comparison needs evidence of an attempt and an explicit definition.

Behind the work

A curious mind.
A clear purpose.

Noah Gomaa
Stockholm, Sweden

I’m Noah Gomaa, a Global Marketing Analytics Lead based in Stockholm. My work brings together marketing measurement, data analysis and data engineering.

I work across acquisition, retention, customer lifetime value and reporting. I build the analysis, check the underlying data and explain what the results do and do not support.

The experience behind the work

Approach

How I think.

Measure real growth.

I focus on real, sustainable growth. Clicks and platform reported numbers are signals, not the final measure of success.

Follow the full relationship.

I look at acquisition, retention and customer lifetime value together. The quality of growth matters as much as its size.

Be clear about certainty.

I describe association as association. A pattern in the data is a reason to investigate, not proof of a causal effect.

How I work.

  1. Validate definitions first.

    I confirm the population, time window and calculation before comparing a metric.

  2. Separate facts from hypotheses.

    I state what is observed, what is inferred and what still needs a test.

  3. Reconcile the numbers.

    I check date keys, extraction timing and cohort maturity instead of forcing different numbers to match.

Expertise

From the question.
To a clear decision.

I bring together marketing measurement, statistical analysis and reliable data. The work starts with a useful question and ends with an explanation people can act on.

Marketing measurement

  • KPI definition and governance
  • Funnel, cohort and retention analysis
  • CLV/CAC, CLV/SAC, ARPU, ROAS and payback
  • Brand demand, share of search and campaign analysis

Experimentation and statistics

  • A/B test design, hypotheses and guardrails
  • Effect sizes, confidence intervals and test power
  • Attribution models versus incrementality
  • Association, causal assumptions and model limitations

SQL, Python and R

  • Advanced SQL in BigQuery and PostgreSQL
  • Python with pandas and NumPy
  • R for statistical analysis
  • Reproducible analysis in Jupyter notebooks

Data engineering and quality

  • dbt data modelling and Airflow orchestration
  • Apache Spark for data processing
  • Grain, joins, deduplication and metric maturity
  • Data validation, automated tests and reconciliation
  • Cost aware querying

Models and forecasting

  • Regression, forecasting and machine learning
  • Marketing mix modelling coordination
  • Adstock, saturation and response curves
  • Attribution, customer value and budget scenarios

Communication and delivery

  • Weekly, monthly and quarterly performance readouts
  • Stakeholder communication and data visualisation
  • Metric documentation and dashboard quality
  • Measurement roadmaps and analytical prioritisation

Tools I work with.

Chosen for the question, the data and the people using the result.

Data platforms and pipelines

  • Google BigQuery
  • Snowflake
  • PostgreSQL
  • Google Cloud Platform
  • dbt
  • Apache Airflow
  • Apache Spark

Analytics and business intelligence

  • Google Analytics 4
  • Google Tag Manager
  • Looker
  • Looker Studio
  • Power BI
  • Grafana

Analysis and development

  • pandas
  • NumPy
  • Jupyter
  • Git
  • Streamlit
  • Plotly
  • Matplotlib

Experience

Experience behind
the perspective.

View LinkedIn

Marketing analytics, measurement and data leadership. These roles shape how I approach the work.

Storytel

Marketing analytics

Global Marketing Analytics Lead

Marketing ROI, acquisition efficiency, advanced analytics and attribution.

Zmarta

Attribution and measurement

Marketing Analytics Expert

Led digital marketing analytics across channels. Built dashboards with Looker and BigQuery, and a unified data model for multi touch attribution to support budget allocation.

nvrmind AB

Data leadership

Head of Data

Data leadership experience spanning Adoore, Widforss and MEDs.

Curamando

Analytics and data architecture

Data Analyst Consultant

Advised clients including Samsung Nordics, Åhlens and Tikkurila on digital analytics, data architecture and dashboards. Improved reporting through automation and clearer visualisations.

Bombayworks

Digital analytics

Senior Digital Analyst

Digital analytics experience with Toyota and Global Connect.

Additional experience

insightsoftware

Data Analyst Consultant

Fishbrain

Data Analyst

Now Interact

Senior Data Analyst and Data Analyst

SiriusXM

Customer Success Manager

Education

Computer Science
& Information Systems

Bachelor’s degree, October 6 University

Selected credentials

Analytics and
digital development

Google Analytics Certification, Google
Front End Web Development, Beetroot Academy

Contact

Where do we
go next?

For conversations about marketing measurement, analytics and data, you can reach me on LinkedIn.

LinkedIn

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