For details, see the Google Developers Site Policies. Using R with Google Analytics are presented.Įxcept as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. The video below outlines the example in this article. Google Analytics Reporting API forum to share some of the exciting analysis you're doing. Hopefully this has whet your appetite forĪnalyzing Google Analytics data in R. Would have led to the incorrect conclusion that Campaign A was more effective. Looking only at transactions that occurred immediately following a visit from Campaign A or B In the long-term, customers from Campaign B completed more cumulative transactions. Therefore, the cumulative transactions plot allows us to see theĪnalyzing these two campaigns, we see that although customers acquired from Campaign AĬompleted more transactions than customers acquired from Campaign B for the first four weeks, Shows the number of total transactions that occurred up to and including eachĭate. Of transactions that occurred on each date, a cumulative transactions plot While an incremental transaction plot shows the number The daily, or incremental transactions and revenue per day can be turned into cumulative numbers The result of this query is transactions and revenue per day for the specified group of users. If you wish to modify this segment or construct your own. Segments Developer Guide for more details on possible segments to create and syntax details ->perSession::ga:transactions>0 specifies the second step of making.Hit of the first session in the given date range. The ^prefix in front of ga:userType=New%20Visitor dateOfSession_Įnsures that the Date of Session, Campaign, and User Type conditions are true for the first.Time, and the second step is to make a purchase. In this case, the first step is to visit from a given campaign in a given set of The sequence:: prefix allows the selection of a set of users that completed a specified.The segment selects users:: in order to include not only the sessions that match the conditions,īut all sessions among users who match the conditions.The segment consists of a few sequence conditions: Who visited the site for the first time and made a transaction between the specified time periods. If the segment is omitted, this query extracts transactions and revenue for all Query.list _ ga:campaign=Campaign%20A ->perSession::ga:transactions>0", September 1 and September 7, 2014, and made a purchase at some point between September 1 and Revenue for all users who first visited the site from Campaign A between The following query below pulls transactions and RGoogleAnalytics sample query can be modified. Property from a marketing campaign made a purchase right away. This is in contrast toĪ more standard analysis, where you might observe whether or not a customer that visited your To see how many transactions a group of customers that were acquired fromĪ given marketing campaign made over the course of a longer period of time. To determine the long-term value of marketing campaigns, you can use R to generateĬumulative revenue and transaction graphs for given cohorts. This requires you to perform cumulative analysis. The short-term, but it can be difficult to determine the long-term value of campaigns since Standard reports in Google Analytics can help you determine if marketing campaigns lead to conversions in What is the long-term value of my marketing campaigns? Verify you have access to a Google Analytics account that contains data that can be usedĮxample code on Github to ensure you can access Google Analytics data withinįor additional setup resources, visit the.SetupĪllows you to retrieve Google Analytics data natively from R. Using the Google Analytics library with R. The remainder of this article describes the steps required to generate some insightful data and graphs To better understand and improve your business. R with your Google Analytics data, you can perform statistical analysis and generate data visualizations R, the popular programming language for statisticalĬomputing, is a powerful tool for analyzing and drawing insights from data. Measure the long-term value of marketing campaigns using This article walks through an example that demonstrates how to The goal of this article is to encourage the great statisticians, researchers,Īnd data scientists currently using R to look to Google Analytics as a usefulĭataset, and likewise, to encourage Google Analytics users to utilize R for Andy Granowitz, Google Analytics Developer Advocate – September 2014
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