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Amin Heshmati is an author at 99Ways focused on PostHog, conversion optimization, experimentation, attribution, and analytics implementation. He writes about building reliable measurement systems and using data to improve digital performance.
Business experimentation is more than choosing a winning page. It reveals which customer problems deserve investment, which ideas should stop, and where to commit resources with evidence.
Read moreAn evidence-based Confidence vs PostHog comparison covering data architecture, statistical methods, guardrails, governance, pricing, tradeoffs, and a practical evaluation plan.
Read moreA 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 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 moreA PostHog audit should prove more than event delivery. These 12 checks follow the full measurement chain—from ad click and identity through event semantics, purchase value, attribution, ROAS, and production governance—so you know which decisions the data can safely support.
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 moreIf the gap is understanding behavior and running tests, PostHog fits; if it is governing and routing customer data across tools, Segment fits. Some teams use both.
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