Analyzing the Impact of Visitors on Page Views with Google Analytics

TL;DR

This paper presents a flexible time series regression methodology to analyze how different types of visitors influence page views on websites using Google Analytics data.

Contribution

It introduces a comprehensive approach to evaluate multiple visitor variables' effects on page views, considering the distinct impact of referral, direct, and returning visitors.

Findings

Referral visitors have low impact on page views.

Direct visitors significantly increase page views.

Connection speed's effect varies with content and visitor location.

Abstract

This paper develops a flexible methodology to analyze the effectiveness of different variables on various dependent variables which all are times series and especially shows how to use a time series regression on one of the most important and primary index (page views per visit) on Google analytic and in conjunction it shows how to use the most suitable data to gain a more accurate result. Search engine visitors have a variety of impact on page views which cannot be described by single regression. On one hand referral visitors are well-fitted on linear regression with low impact. On the other hand, direct visitors made a huge impact on page views. The higher connection speed does not simply imply higher impact on page views and the content of web page and the territory of visitors can help connection speed to describe user behavior. Returning visitors have some similarities with direct visitors.