Confidence vs PostHog: Which Experimentation Platform Fits Your Team?
An evidence-based Confidence vs PostHog comparison covering data architecture, statistical methods, guardrails, governance, pricing, tradeoffs, and a practical evaluation plan.
99Ways
Conversion rate optimization, experimentation & analytics
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PostHog Implementation for Growth Lever Teams
A useful PostHog implementation is not an SDK installation or feature tour. It is a decision system connecting business-aware events, identity, acquisition, revenue, experiments, and QA so a growth team can identify a lever, act on it, and validate the result.
A/B Testing Metrics: Choose the Primary Metric Closest to Profit
The best A/B testing metric is usually the measurable outcome closest to incremental contribution per eligible visitor, user, or account. This guide separates true decision metrics, operational primaries, leading indicators, guardrails, and diagnostics—then maps ecommerce, subscription, pricing, lead-generation, marketplace, and cost-changing experiments to the metrics that should actually determine the winner.
CRO Redesign vs Ongoing A/B Testing: Which to Start With?
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.
How to Evaluate a CRO Agency: Business Impact vs Win Rate
A 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.
Event Tracking Plan for CRO and Product Analytics
An 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.
PostHog Audit: 12 Checks Before You Trust the Data
A 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.
PostHog vs GA4: Analyst’s Guide
PostHog 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.
Experimentation System Audit: Can You Trust Your A/B Test Results?
An 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.
Conversion Tracking Audit: Can You Trust Your Funnel Data?
A 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.
What Is Growth Lever Analytics? A System for Finding Growth Drivers
Growth 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.
How Much Does A/B Testing Cost?
What does it cost to run an A/B test? The obvious answer is the cost of producing it. Someone needs…