Tester Jobs Requirements (VIDEO GAME)

Hello, Im Mark Edmondson and I have the honour of being a Google Developer Expert for Google Analytics, a role that looks to help developers get the most out of Google Analytics. My specialities include Google APIs and data programming, which has prompted the creation of googleAnalyticsR, a new R package to interact with the recently released Google Analytics Reporting API V4.

R is increasingly popular with web analysts due to its powerful data processing, statistics and visualisation capabilities. A large part of R�s strength in data analysis comes from its ever increasing range of open source packages. googleAnalyticsR allows you to download your Google Analytics data straight into an R session, which you could then use with other R packages to create insight and action from your data.

As well as v3 API capabilities, googleAnalyticsR also includes features unique to v4:

•  On the fly calculated metrics 
• Pivot reports 
• Histogram data 
• Multiple and more advanced segments 
• Multi-date requests 
• Cohorts 
• Batched reports 

The library will also take advantage of any new aspects of the V4 API as it develops.

Getting started

To start using googleAnalyticsR, make sure you have the latest versions of R and (optionally) the R IDE, RStudio

Start up RStudio, and install the package via:

install.packages("googleAnalyticsR")

This will install the package on your computer plus any dependencies.

After successful installation, you can load the library via library(googleAnalyticsR), and read the documentation within R via ?googleAnalyticsR, or on the package website.

An example API call � calculated metrics

Once installed, you can get your Google Analytics data similarly to the example below, which fetches an on-the-fly calculated metric:

library(googleAnalyticsR)

# authenticate with your Google Analytics login
ga_auth()

# call google analytics v4
ga4 <- google_analytics_4(viewId = 123456,
                         date_range = c("2016-01-01",
                                       "2016-06-01"),
                         metrics = c(calc1=ga:sessions /
                                            ga:users),
                         dimensions = medium)

See more examples on the v4 help page.

Segment Builder RStudio Addin

One of the powerful new features of the v4 API is enhanced segmentation, however they can be complicated to configure. To help with this, an RStudio Addin has been added which gives you a UI within RStudio to configure the segment object. To use, install the library in RStudio then select the segment builder from the Addin menu. ?

Create your own Google Analytics 

Dashboards googleAnalyticsR has been built to be compatible with Shiny, a web application framework for R.  It includes functions to make Google Analytics dashboards as easy as possible, along with login functions for your end users. ?

Example code for you to create your own Shiny dashboards is on the website.

BigQuery Google Analytics 360 exports 

In addition to the v4 and v3 API functions, BigQuery exports from Google Analytics 360 can also be directly queried, letting you download millions of rows of unsampled data.

Aimed at analysts familiar with Google Analytics but not SQL, it creates the SQL for you to query common standard metrics and dimensions, using a similar interface as the API calls.  See the BigQuery section on the website for more details.

Anti-sampling 

To more easily fetch non-sampled data, googleAnalyticsR also features an anti-sampling flag which splits the API calls into self-adjusting time windows that are under the session sampling limit.  The approach used is described in more detail here.

Get involved 

If you have any suggestions, bug reports or have any ideas you would like to contribute, then you are very welcome to raise an issue or submit a pull request at the googleAnalyticsR Github repository, or ping me on Twitter at @HoloMarkeD.

Posted by Mark Edmondson, Google Developer Expert

By Rajagopal Sathyamurthi (CTO & Co-Founder) and Leta Soza (Director of PR Engineering & Ops) at AirPR

Public Relations constantly grapples with the ability to report meaningful results in a manner that resonates with executives and members of the C-Suite.

Until the emergence of �PRTech� began to enable dot-connecting between PR activities and business goals, the PR industry generally used output driven metrics (impressions, number of press hits, AVEs, etc.) to communicate its value; but these barometers rarely tie back to business goals and don�t do much to inform future decision-making.

All of this is changing as PR shifts its focus from reporting on outputs to data-driven outcomes. Metrics such as visitors to site, engagement, and message �pull-through� are quickly gaining ground as the new gold standards for PR measurement. A widespread adoption of these metrics, however, is predicated on a reporting mechanism that can neatly and efficiently package this data.

To help solve this widespread challenge, AirPR recently launched a Reporting Suite that uses aggregated PR activity output data from our Analyst product to generate automated, actionable reports based on the customized success metrics of any company.

One of the key components to leveraging the power of the Reporting Suite is our solution�s unique integration with Google Analytics. This integration allows us to analyze and visualize specific subsets of Google Analytics data.

The Google Analytics Core Reporting API exposes a few key metrics which have great utility for PR professionals: users and goal/event conversions. Pre-processing and ingesting this metric data into our Reporting Suite allows PR pros to see trends over time as it relates to traffic and engagement driven by PR content.

When PR can quickly survey this data, the question isn�t, �do spikes in coverage volume correspond to spikes in traffic?� Rather it is, �which narratives, outlets or topics drive the most traffic, engagement, or amplification?� Also, �how can successes be replicated?� ?

These key metrics combined with AirPR�s data, classification features, and proprietary algorithms also provide a simple way to slice, dice, and visualize complex data in seconds.

Dimensions such as full referrer (content sources) and date (subset of time) are crucial to PR pros understanding which media outlets and specific pieces of content are more impactful for their business over different time ranges. ?
AirPR Analyst enhances the direct attribution data from Google Analytics with additional sources of PR data to track and display the performance of content that contain links back to company websites, as well as the impact of articles that do not contain links or those with links that do not get clicked. 

Last but not least, our Reporting Suite aims to impact how PR success is defined. Not only will PR have a clearer picture of what delivers for their brand or client, they can quickly and succinctly speak to business leaders in key data points, which highlight success stories.

No matter what technology or methods you are using, here are a few simple tips for communicating success like a PR boss, and ensuring that the executive (to which you report) can clearly see the value of your hard work and effort:

Top-line and bottom-line it.
 � The best of the month in terms of X metric was Y content and what this means is Z.?

Use numbers to tell the story. 
 � We saw an X% change in Y month over month, and what that does for us is Z.?

Speak to business wins.
 � This is what X activity did for business goal Y.?

Share what�s next. 
� With X data, we are going to focus on Y.?

The remaining hurdle for PR pros is to begin thinking differently about what metrics matter in terms of future decision making. While impressions and press hits are certainly important in terms of �activity-based metrics�, they don�t necessarily tell the whole story. Our information-rich environment implores us to continually evaluate which pieces of media or content properly convey key messages, reach our desired audiences, generate top-of-funnel business leads, and ladder up to business goals.

Posted by Google Analytics technology partner AirPR