Better alternatives to Google Analytics for content teams
There are many technologies available to digital media teams nowadays. While classic platforms like Google Analytics are still the main tools used by everyone in the digital world, specialized analytics products become more and more popular. Here is the list of reasons why Google Analytics does not perform well in a fast-paced media environment:
Reason #1 — Data accuracy. It is quite common for Google Analytics reports to decrease its accuracy, especially when it comes down to solving a particular question by drilling down to a primary or secondary dimension.
For example — generating a breakdown of traffic sources often involves a method that’s called “Sampling”, which turns your report into an estimation that’s based on just a portion of your traffic. This means that the audience patterns present in a small amount of data will be scaled to all your data. The reliability these reports decreases the more data you collect and this often leads to making a decision based on the numbers that are far from the truth and not only that.
Almost every breakdown (drilldown) report will show different summary numbers (e.g. your total pageviews for the article may increase or decrease by a certain amount) and that may confuse the user and sometimes even turn them away from data forever. IO Technologies never samples data and always displays the correct traffic and engagement metrics, regardless of the number of filters applied (i.e. audience loyalty, traffic source, device type, article sections, etc.).
Reason #2 — Data freshness. Both free and 360 versions of Google Analytics have certain limitations when it comes to data freshness:
This is crucial for digital publishers as it severely limits the editor’s abilities to quickly react to traffic spikes and monitor current day performance (intraday data can be delayed by as much as 4 hours with a paid 360 package).
Delayed data pipeline forces the newsroom to split between using instant metrics during the day (Active users) and summary metrics (Behaviour or Audience overview) after the day ends, greatly reducing the data that’s available to them. Numbers may often appear the next day when it’s too late, making the newsroom unable to make correct decisions regarding content placement and optimization (making edits to the article text to increase engagement or adjusting headlines / images to increase click-through rate). Data pipeline developed by IO Technologies allows the newsroom to evaluate article performance almost instantly after publishing and changes in engagement metrics can be monitored in real-time after the stories are edited. Fast data pipeline is a must-have in a fast-paced and ever-changing digital media environment.
Reason #3 — Only necessary data is provided. With specific KPIs set to digital team, editors often are overwhelmed by the amount of data present in classic analytics systems and the time required to get required data.
Data that’s used within the newsroom is scattered across Behaviour, Acquisition and Real-time reports in Google Analytics. Teams are forced to build and maintain custom Data Studio reports which further decreases data freshness. IO Technologies has combined the best practices of integrating data into the daily routine of hundreds of newsrooms and every publisher gets access to an optimized and de-cluttered real-time view of their website stats. This is achieved with absolutely no sacrifices to your data quality and freshness to ensure unprecedented access to the heartbeat of your publication 24 hours a day, 7 days a week.
Reason #4 — Proper engagement calculation. Engagement time and Bounce Rate are industry standards that allow editors to understand user behavior and make weighted decisions.
Due to the way engagement is calculated in Google Analytics (average time is determined as a time between two consecutive hits), these numbers may not reflect what readers actually do on your articles:
1) Periods of inactivity (time spent on other browser tabs or with the browser in background) may twist and increase average time metrics and hide potentially un-engaging stories from sight.
2) Engagement data isn’t provided by bounced users at all. Since there is no consecutive hit to measure the time between, average time spent by these users is taken out of the equation completely. This is a serious issue as articles with high bounce rate may still exhibit pretty normal average time, leaving the editors uncertain whether the article should be edited or not.
Bottom line: Content analytics tools have a plenty of advantages compared to classic systems. Focusing on one niche allows to provide you with the best user experience and data processing times.
Make sure to check out the following tools:IO Technologies: https://www.public.iotechnologies.com/
Chartbeat: https://chartbeat.com/
Parse.ly: https://www.parse.ly/
