Web Analytics Consultancy
Product & Customer Analytics
Some of the 500+ brands we've worked with
See all references
Approved task contracts
Choose the product or customer decision to measure
Decision systems
Cohort, Retention & LTV Analysis
Nothing was changed on purpose here; you are comparing customers who arrived months apart, so the curves get aligned by maturity before a younger cohort is called weaker.
Product Experimentation Measurement
A specific in-product change is being tested, so the hypothesis, sample size, guardrails, and stopping rule are timestamped before the first assignment rather than after the readout.
When this owner applies
Make product and customer behavior measurable
Measure in-product behavior, experiment integrity, retention, and customer value with reproducible definitions and bounded claims.
Capability boundary
Analytics owns the readout, CRO owns the treatment
Analytics owns measurement design and readout. CRO owns treatment and programme execution.
Evidence-led delivery
Set the experiment and metric rules up front
Every engagement has named inputs, owners, human approval gates, reproducible QA, explicit limitations, and an operating handover.
We measure in-product feature usage, onboarding funnel drop-offs, user retention cohorts, and experimentation validity. Zeo specialists instrument telemetry contracts, cohort analysis, and A/B test validation, while your product managers authorize feature rollouts.
From behavior definition to a reviewable readout
Define product telemetry
We map feature engagement, onboarding steps, user activation milestones, and lifecycle events into an explicit product tracking schema.
Configure cohort tracking
We build user cohort analyses to measure N-day retention, churn risk indicators, and customer lifetime value (LTV) models.
Measure experimentation
We instrument feature flag tracking, check sample ratio mismatch (SRM), and validate statistical confidence in product A/B tests.
Enable product insights
We construct self-serve product analytics dashboards for product managers, designers, and growth engineers.
From our clients
What clients say about measurement work
A client describing the planning and reporting problems we started from.
Before working with Zeo, we had serious problems, especially with planning and measurement. Because we couldn't get complete measurements and reports, we struggled to evaluate our work. Evaluating the data correctly, in line with scientific rules, was the most important thing for us.
Dr. Kadir Kırmızı - General Manager
User growth
Case Studies
Accounts tracked on new versus returning users, not sessions alone.
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.
Yağmur Bayram
Sr. SEO Analyst
Zafer Yıldız
Web Analytics Manager
Abdullah Tanıdır
Performance Marketing Team Lead
İlker Emir
Senior Performance Marketing Executive
İpek Ezer
Performance Marketing Executive
Deniz Çağın Demirci
Frontend Developer
Mirzamin Aghazada
UI/UX Designer
Mehmet Aktuğ
Co-Founder & COO
Serap Yurtvermez
Performance Marketing Team Lead
Sevda Yurtvermez
Performance Marketing Team Lead
Onur Durdağı
Performance Marketing Executive
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.
Product & mobile app analytics
- MixpanelWe build the core product telemetry, custom events, user properties, funnel definitions, in Mixpanel when a client's product analytics needs are the primary job, distinct from a marketing-facing GA4 setup. That event-schema design work is the define-product-telemetry step this service names as its own first stage.
- AmplitudeWe build retention and LTV cohort curves in Amplitude aligned by each cohort's own age rather than the calendar date it started, since comparing a three-week-old cohort against a mature one on the same axis makes an improving trend look worse than it is. That maturity-aligned view is specifically what the cohort-tracking step in this service's process depends on.
- PostHogFor a client whose data policy requires self-hosting or full data ownership, we build the same event-telemetry work in PostHog instead of a vendor-hosted platform, since it can run entirely inside a client's own infrastructure. That deployment flexibility is the deciding factor over Mixpanel or Amplitude for this specific constraint.
- HeapWhen a client needs to answer a product question about a click path or interaction that was never explicitly instrumented, we turn to Heap's autocapture, since it retroactively has the data an event-based platform like Mixpanel would have missed without a prior definition. That autocapture model is the specific reason to reach for it over an event-first platform.
- PendoFor clients whose product-insights step includes launching an in-app walkthrough or NPS survey based on what the analytics find, we set that up in Pendo directly, since it combines the measurement and the in-product action in one tool. That closes the loop faster than a separate analytics platform paired with a separate engagement tool.
- StatsigWe run experiments in Statsig when a client wants feature flagging and the statistical analysis of that same rollout tightly coupled, since a flag that controls exposure and a metric dashboard reading from a separate system can drift out of sync. That combined model is what this service's measure-experimentation step relies on when a client's stack is Statsig-based.
- UserpilotWhen the product-insights work is scoped specifically to onboarding, a new user's first-session flow rather than a mid-lifecycle feature, we use Userpilot, since it pairs the analytics with the actual onboarding UI being measured. That scoping to first-session behavior is what separates it from the broader cohort and experimentation tools in this practice.
Session replay, heatmaps & experience analytics
- FullstoryWhen a cohort or experimentation finding needs a qualitative explanation, we pull the actual user paths in Fullstory's Journey Maps, since a funnel chart collapses backtracking and re-entry into a single exit number that hides what people were actually trying to do. That session-level view is what turns a quantitative anomaly into an understood behavior.
- ContentsquareFor a smaller product team where Fullstory's fuller journey-mapping and analytics suite is more than the engagement calls for, we run the same qualitative session-recording check in Hotjar instead. It answers the same specific question, what did this cohort of users actually do, at a lower commitment.
- ContentsquareFor an enterprise client needing zone-level heatmap and journey analysis across high page-view volume, we bring in Contentsquare rather than a lighter session-replay tool, since it is purpose-built for that scale of aggregate behavioral analysis. That scale threshold is what separates it from Fullstory or Hotjar in this practice.
A/B testing & personalization
- LaunchDarklyFor a client whose engineering org already standardizes on LaunchDarkly for feature-flag management, we keep that rollout mechanism as-is and pair it with a separate analysis platform, Amplitude or Optimizely, for the statistical read. That separation of concerns fits an engineering-led flagging setup better than a combined platform like Statsig would.
- OptimizelyWe run product experiments in Optimizely against a sample size and stopping rule locked before the test starts, since a rule that gets revised once early results look a certain way is what makes an experiment's conclusion unreliable. That discipline is the actual content of this service's measure-experimentation step, not just which tool executes the test.
Next step
Turn measurement into reliable business decisions
Questions