Explore Page/Screen Views using your PostHog data
Page/Screen Views in PostHog
Page/Screen Views analysis becomes particularly powerful with PostHog data because you gain access to granular user behavior tracking that goes far beyond simple visit counts. PostHog captures detailed event data including user properties, session recordings, and feature flags, allowing you to understand not just how to increase page views but also identify specific user segments, traffic sources, and behavioral patterns driving engagement. This rich dataset enables you to make informed decisions about content optimization, user experience improvements, and product development priorities.
However, analyzing this wealth of PostHog data manually creates significant challenges. Spreadsheets quickly become unwieldy when exploring the countless permutations of user segments, time periods, and behavioral filters—leading to formula errors and hours spent maintaining complex calculations. PostHog’s built-in analytics, while useful for basic reporting, provide rigid outputs that can’t adapt when you need to investigate why page views are dropping for specific cohorts or explore nuanced questions about user journeys across different features.
Count transforms your PostHog page view data into an interactive analysis environment where you can instantly segment users, compare time periods, and drill down into anomalies without wrestling with spreadsheet formulas or being constrained by predetermined dashboard views.
Questions You Can Answer
What are my top performing pages by page views this month?
This reveals which content or features are driving the most engagement, helping you understand what resonates with your audience and where to focus optimization efforts.
Why are page views dropping for my landing pages compared to last quarter?
Identifies potential issues with traffic sources, user experience, or content quality that could be impacting conversion funnels and overall growth.
How do page views vary by traffic source and device type in PostHog?
Uncovers performance differences across channels (organic, paid, direct) and platforms (mobile, desktop), enabling targeted improvements for underperforming segments.
Which user cohorts have the highest page views per session, and what’s their common behavior pattern?
Analyzes engagement depth across different user segments, revealing characteristics of your most engaged users and opportunities to increase page views from other cohorts.
How do page views correlate with feature flags and A/B test variants in my PostHog data?
Connects page engagement to specific product changes or experiments, helping you understand how feature releases impact user navigation and content consumption.
What’s the page view journey for users who convert versus those who churn, segmented by acquisition channel?
Provides sophisticated analysis of how different traffic sources lead to varying engagement patterns, revealing optimization opportunities to increase page views and improve conversion rates.
How Count Analyses Page/Screen Views
Count transforms your PostHog page view data into actionable insights through intelligent, bespoke analysis rather than rigid templates. When you ask “how to increase page views” or “why are page views dropping,” Count’s AI agent writes custom SQL and Python logic specifically for your question, automatically segmenting your PostHog data by device type, traffic source, user properties, and page categories in a single comprehensive analysis.
Count runs hundreds of queries in seconds across your PostHog events, uncovering hidden patterns like seasonal trends, user journey drop-offs, or performance variations across different page types that would take hours to discover manually. The platform automatically handles PostHog’s event-based data structure, cleaning inconsistencies in page URLs, filtering out bot traffic, and normalizing screen names across mobile and web platforms.
Every analysis comes with transparent methodology—Count shows you exactly how it processed your PostHog events, what assumptions it made about page groupings, and how it calculated engagement metrics. You receive presentation-ready visualizations and insights that explain not just what’s happening with your page views, but why.
Count’s collaborative features let your team dive deeper together, asking follow-up questions like “which acquisition channels drive the highest-converting page views?” Count can even connect your PostHog data with other sources—your CRM, marketing platforms, or revenue data—to understand how page views translate into business outcomes, giving you the complete picture needed to optimize your content strategy.