Unmask (Not Provided) Goal Completions in Google Analytics As of late, the SEO blogosphere has been rich with posts from authors seeking to diagnose seeming declines in branded organic traffic. The common culprit (or deceiver, if you will) is Google’s grand obfuscation – (not provided). In the technology industry, the presence of (not provided) in keyword reports has proliferated drastically. Comfort is a commodity, after all – a commodity granted to those who haven’t seen such a stark increase in (not provided). Reporting on (Not Provided) In looking at overall goal conversions from organic search traffic, we might be tempted to postulate that the conversion keywords that show up in our reports are distributed similarly across the (not provided) segment. As SEOs and analysts, we love granularity. Gains in organic search traffic will appear lessened, and losses will be accentuated. Segmenting (Not Provided) Conversions As these segments become more and more clouded, so too do our reports on goal conversions resulting from each of them. 1. 2. 3.
Split Testing With Google Analytics Experiments In today's tutorial, we're going to be looking at one of Google Analytics' most recent additions to its feature set; Experiments. Using this tool, I'll be showing you how to serve up different variations of a page to determine which one is the most successful in converting visitors to the site. Preamble If you've ever created a website, you'll almost certainly be familiar with Google Analytics. From small personal projects to enterprise level sites, Google Analytics has established itself as the market leader for very good reasons; it's free, simple to implement and is suitable for the casual user or even the most battle-hardened marketer. Ready to get started? A Brief Introduction to Split Testing We've covered split testing before as part of Ian's thorough roundup on conversion and online marketing, but let's take a brief look at split testing in the online arena. Variations must be run all at the same time. The Scenario Here's an image of the third page variation: Step 3: Create a Goal
A/B Testing with Google Analytics Content Experiments Google Analytics has announced a new A/B testing feature called Content Experiments. This is a pretty significant evolutionary step for Google Analytics in making it an analytics and optimization tool. Think of this as Google Website Optimizer being baked right into the Google Analytics interface. Using Content Experiments in lieu of GWO will allow you to easily define content URLs and goals for your experiments, analyze your reports more efficiently and will eliminate the need for all those extra GWO tracking codes on your site. I want to review the basics of A/B testing and running GA Content Experiments, as well as discuss some important technical details and advanced considerations. What is A/B Testing? A/B testing takes a lot of forms. A/B testing is very easy (and free) with Google Website Optimizer. How to Setup a Content Experiment Prepare. After you’ve completed an experiment, start the process again with another page and keep trying to make incremental improvements to your site!
Upgrades To Google Analytics Content Experiments A few weeks ago Google Analytics launched Content Experiments, a new testing functionality that can be used to create A/B/N tests to optimize campaigns and overal website experience. Last week Google announced 3 upgrades that will make testing with the tool significantly easier and more powerful. Below I discuss each of the upgrades and how they can enhance testing with Google Analytics 1. Ability To Copy Experiments This new functionality is valuable as it allows marketers to perform additional tests to the same page without modifying the codes, which makes the process much shorter. On the settings page, on the bottom-right corner you will find the “Copy experiment” button, as seen below: After clicking the button, you will get the following message: “Copying an experiment will copy the current settings into a new experiment—where you can adjust as you desire. 2. Relative URLs offer more flexibility in defining the location of the variations. 3. Closing Thoughts
Google Analytics Content Experiments - A Guide To Website Testing | Testing & Usability [Last Updated on November 2013] In this article I discuss Content Experiments, a tool that can be used to create A/B tests from inside Google Analytics. This tool has several advantages over the old Google Website Optimizer, especially if you are just starting the website testing journey. Content Experiments provide a quick way to test your main pages (landing pages, homepage, category pages) and it requires very few code implementations. Here is a quick overview of the most prominent features that will help marketers get up and running with testing: Below is a step-by-step guide on how to use Content Experiments to create A/B tests. Creating Content Experiments In order to create a new experiment, navigate to the Behavior section and click on the Experiments link on the sidebar. Once you define all the information above, click on it you will reach the following page. In this page you can add all the URLs of your original page and the variations you would like to test. Click Next. Yay!
Custom Reporting Using Google Analytics and Google Docs The author's posts are entirely his or her own (excluding the unlikely event of hypnosis) and may not always reflect the views of Moz. Realtime Google Analytics data inside a Google Doc—a panacea! Don't believe me? Check out that screenshot below. Google Analytics is my favorite analytics product. But, despite all the flexibility that Google Analytics offers, sometimes you want to access data in a spreadsheet and create a truly custom report. This blog post is going to show you how to create a custom report by connecting a Google Spreadsheet directly with your data from Google Analytics. Analytics geeks: hold onto your seats! It all started with the Data Feed Query Explorer (Those who want to start accessing data in Google Docs should jump right to the next section.) Before we dive in, a little background. I first discovered Google's excellent Data Feed Query Explorer. The Data Feed Query Explorer is a great way to explore the Google Analytics API, and to understand what data is available.
Blog | Web Analytics, SEO, PPC, & Social Media Experts Understanding Bot and Spider Filtering from Google Analytics On July 30th, 2014, Google Analytics announced a new feature to automatically exclude bots and spiders from your data. In the view level of the admin area, you now have the option to check a box labeled “Exclude traffic from known bots and spiders”. Most of the posts I’ve read on the topic are simply mirroring the announcement, and not really talking about why you want to check the box. Maybe a more interesting question would be why would you NOT want to? Still, for most people you’re going to want to ultimately check this box. 20 Google Facts & Stats that Every Marketer Should Know No company dictates the online marketing industry and all of our careers like Google. This post outlines 20 things that every marketer should know about Google. If we missed any important facts, please let us know in the comments. Easy Cohort Analysis for Blogs and Articles Enter Cohort Analysis. Segmenting Google Analytics by Session Frequency