Web Analytics Consultancy
Analytics Engineering & Data Activation

Some of the 500+ brands we've worked with
See all referencesApproved contracts for bounded tasks
What Zeo delivers for Analytics Engineering
When this owner applies
When collection data still needs engineering
Transform, enrich, test, and activate analytics data beyond collection tools with explicit lineage and ownership.
Capability boundary
Where analytics engineering begins and ends
GA4 owns exports and collection tools. Engineering owns durable transformations and downstream models.
Evidence-led delivery
Contracts before tools, acceptance before claims
Every engagement has named inputs, owners, human approval gates, reproducible QA, explicit limitations, and an operating handover.
We transform raw analytics streams into structured, version-controlled data models ready for business analysis. Zeo specialists author dbt models, automated data quality tests, and BigQuery schemas, while your data team owns business logic definitions and warehouse access.


The Analytics Engineering delivery lifecycle
Model raw analytics data
We clean, parse, and structure raw GA4 BigQuery export tables into normalized relational or dimensional data models.
Build transformation pipelines
We write modular, version-controlled dbt models and SQL scripts for recurring sessionization, attribution, and user metrics.
Implement data testing
We configure automated data quality assertions checking for primary key uniqueness, non-null constraints, and metric anomaly thresholds.
Activate downstream
We connect modeled warehouse tables to BI tools, reverse-ETL integrations, and marketing automation platforms.
From our clients
What clients say about measurement work
A client describing the planning and reporting problems we started from.
Activated data
Case Studies
Results that came from acting on the reporting, not just collecting it.
People who build your measurement system
Zeo designs measurement systems that connect a business decision to governed collection and reporting you can check. The people shown here work on the part of that system this page covers.

Yiğit Konur
Founder & Chief Strategy Officer

Burak Pehlivan
Co-founder & CEO

Abdullah Tanıdır
Performance Marketing Team Lead

İlker Emir
Senior Performance Marketing Executive

Deniz Çağın Demirci
Frontend Developer

Mirzamin Aghazada
UI/UX Designer

Serap Yurtvermez
Performance Marketing Team Lead

Sevda Yurtvermez
Performance Marketing Team Lead

İpek Ezer
Performance Marketing Executive

Onur Durdağı
Performance Marketing Executive

Metehan Urhan
New Business & Partnership Manager
Content we've produced on this topic
Tools we use
The measurement stack behind the work
Collection, tagging, product analytics and reporting are separate problems with separate tools. These are the ones we build measurement on.
Core web analytics platforms
- Google AnalyticsThe model-raw-analytics-data step this service names starts from the GA4 export specifically, treating it as the input a CDP or transformation pipeline enriches rather than the final destination. That framing is what separates this page from a GA4 configuration page: here, GA4 is upstream, and the CDP and pipeline work downstream of it.
Tag management, CDPs & server-side tracking
- Twilio SegmentWhen a client already runs a wide spread of mainstream marketing and product tools, we route collection through Segment first, since its destination catalog covers most of that list without custom connector work. That breadth is what makes it the default starting point rather than the tool we reach for on a highly custom or warehouse-first build.
- RudderStackFor a client whose data governance policy requires the warehouse to remain the single source of truth, we build collection on RudderStack instead of Segment, since it writes events directly into that warehouse rather than holding them in vendor-side storage first. That architecture choice is what satisfies a data-residency requirement Segment's model does not.
- SnowplowWhen a client needs strict event-schema enforcement, every event checked against a defined contract before it is accepted, we build collection on Snowplow rather than a CDP that accepts anything and cleans it up later. That validate-at-the-door behavior is what keeps a malformed event from ever reaching the transformation pipeline in the first place.
- mParticleFor a client whose primary collection surface is a native mobile app rather than the web, we route through mParticle, since its identity resolution and SDK routing were built mobile-first rather than adapted from a web-first product. That app-native foundation is what keeps identity stitching reliable when most events never touch a browser at all.
- TealiumWhen a client wants tag management and CDP-style audience building in one platform instead of pairing a separate CDP with GTM, we build on Tealium, since AudienceStream sits directly on top of its own collection layer. That single-vendor pairing is what avoids the extra integration work of stitching a CDP to a tag manager it was not designed alongside.
- Treasure DataFor an enterprise client that needs full customer-360 profiles built from many source systems, not just event routing, we build the activation layer on Treasure Data, since its profile unification and segmentation are designed for that scale of identity resolution. That is a heavier lift than the routing-focused CDPs in this group, chosen only when the unification problem itself is the point.
- AmperityWhen the activation problem is really an identity problem, the same customer showing up as several different records across a client's systems, we build the resolution layer on Amperity, since deduplication and identity stitching are its specific focus rather than a feature bolted onto a broader CDP. That specialization is what we reach for over a general-purpose CDP when matching, not routing, is the hard part.
- Salesforce Data CloudFor a client whose sales and marketing teams already work inside Salesforce, we activate unified data through Salesforce Data Cloud directly, since it writes back into the CRM and Marketing Cloud objects those teams already use rather than a separate destination they would need to learn. That native fit is the deciding factor over a CDP that would need its own Salesforce connector built and maintained.
BI, dashboards & reporting
- Looker StudioOnce the activate-downstream step delivers tested, enriched data, we surface it in Looker Studio for stakeholder reporting. Feeding a dashboard from this pipeline's output, rather than straight from raw collection, is what lets a report show a consistent, already-validated number instead of one that needs re-explaining every time it moves.
Next step
Turn measurement into reliable business decisions


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