Iman combines user psychology, business strategy, and experimentation to uncover what drives action and improves performance. He focuses on hypothesis development, evidence-based decision making, and turning insights into changes that can be confidently tested and scaled.
A tested one-time redesign can be rational when the current experience needs a structural reset. Ongoing A/B testing creates more long-term value when each experiment produces both deployed impact and reusable customer learning. This framework shows how to choose—and why one hypothesis does not mean changing only one UI element.
Read moreA CRO agency should be judged by the complete evidence chain it creates: trustworthy measurement, explicit hypotheses, valid experiments, verified deployments, and cumulative improvement to a meaningful business outcome. Win rate, test count, and isolated uplift are useful diagnostics, but they do not prove realized business value.
Read moreAn event tracking plan is not an event-name spreadsheet. It is the contract that defines how business questions become entities, events, trigger rules, properties, identities, revenue facts, metrics, QA, and governed analytics data. This guide includes a complete paid-subscription example and downloadable Excel template.
Read moreTrace one real outcome back to its source, then reconcile the same relationship across a defined period. The result is a trust decision for each report you rely on.
Read morePostHog is usually better suited than the GA4 reporting interface to be the operational source of truth for growth decisions. It provides direct event-level investigation, explicit identity, flexible SQL, behavioral analysis, and experimentation in one environment. GA4 remains valuable for standardized acquisition and Google Ads reporting; when raw GA4 data is modeled in BigQuery, the warehouse becomes the real source of truth.
Read moreAn A/B result is only as trustworthy as the system that produced it. This audit framework examines eligibility, assignment, bucketing, identity persistence, exposure, sample-ratio mismatch, metric definitions, conversion windows, and statistical analysis. It also introduces the Zero Experiment: a production A/A calibration test designed to reveal whether the system creates lift where no treatment exists.
Read moreA conversion tracking audit should prove more than whether a tag fires. It should verify event meaning, duplicates, identity, cross-domain continuity, attribution, consent behavior, and reconciliation with orders, payments, CRM records, and advertising platforms. This framework shows what to inspect before funnel data is used for optimization.
Read moreGrowth Lever Analytics is a decision process for finding the controllable variable worth acting on, building the measurement needed to trust the decision, and validating the intervention through experimentation. It connects business economics, analytics, and causal evidence so that data ends in a ranked action rather than another dashboard.
Read moreA/B testing costs more than production: an underperforming variation consumes conversions while it receives traffic. Measure that downside against Control and set a pre-agreed loss budget.
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