Ahrefs Web Analytics

This distinction is fundamental for understanding customer acquisition versus retention. Implementation Suggestions: Implement a First-Party Cookie System: The core of this tracking relies on setting a long-lasting, first-party cookie (like Google Analytics' _ga cookie) when a user first visits the site. Track Specific Events: Log first_visit or first_open events to explicitly mark a new user's initial interaction. Subsequent visits within a defined lookback window (e.g., within two years) are then classified as returning visits. Create Dedicated Reports: Acquisition Report: Show a clear breakdown of how many new users are coming from which channels (organic search, paid ads, social media) to measure acquisition effectiveness. Retention Report: Display the percentage of returning users and their return frequency over time to measure loyalty and engagement. Add "User Type" as a Primary Dimension: Allow users to segment any report by "New Visitor" vs. "Returning Visitor" to compare key metrics like conversion rates, bounce rates, and average session duration between the two groups. Cross-Device Identification (User ID): For more accurate tracking across different devices (desktop, phone, tablet), suggest implementing a User ID feature. This involves assigning a unique, persistent ID when users log into an account, allowing the analytics tool to link all their activity to a single user profile.

Provide an easy-to-use API that allows clients to send data from non-traditional sources-like mobile apps, IoT devices, or offline CRM systems-into the main analytics platform for a truly holistic view of product and service interaction.

Integrate deep performance metrics including Google's Core Web Vitals (Largest Contentful Paint, First Input Delay, Cumulative Layout Shift). Flag pages with slow load times and correlate performance data with bounce rates and conversions to prove the business value of technical optimization.

Implement a dedicated report for cohort analysis, allowing clients to track groups of users acquired during the same time period. This measures how engagement and retention rates change over time, providing deep insights into the long-term impact of product updates or marketing campaigns on user loyalty.

Provide detailed, real-time geographic reports showing visitor locations down to the city level (anonymized IP addresses). This helps businesses tailor content for different regions, identify new market opportunities, or understand localized performance issues.

Implement a machine learning-driven system that automatically scans all metrics in real-time, identifies statistical anomalies, and proactively surfaces these insights to the user.

Collecting demographic and interest data requires careful implementation, often relying on integrations with third-party data sources like Google Signals.

The best thing about Universal analytics that does not exist in GA4 is the ability to create segments based on multiple criteria (ie - segement ppl who landed on this page, who stayed for more than 2 minutes, using this device, on this OS, etc) .

It looks like bot traffic is included in the data by default, and it is tedious to exclude the bots individually with the filters. Thanks :)

It would be nice if there was a way to specify a list of IPs to fillter out. This would reduce instances of the count being inflated due to testing by the internal teams of an org.