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Increasing Your Website's Conversion Rate

Increasing Your Website's Conversion Rate
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? 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 It’s now easier than ever to track and compare performance between articles and blogs. Enter Cohort Analysis. Read More… Read More… Read More…

Kevin Hillstrom: MineThatData 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? 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. Now you're all set!

Conversion Room Our 8 Most Popular Analytics Posts of 2009: The ROI Revolution B Our 8 Most Popular Analytics Posts of 2009 December 29, 2009 The end of the year is a nice time to take a look back over all that was accomplished throughout the year. Enjoy the posts and have a Happy New Year! #1. 6 Tools Every GA User Should Have Shawn Purtell Random Thoughts: This was by far the most popular post of 2009. #2. Jeremy Aube What's up with that frog? #3. Michael Harrison We've seen people do a lot of crazy Google Analytics setups. #4 ga.js code for GWO Yet another update to GARE. #5. 5 Advanced Segments for Ecommerce Another list post, this one dealing with using the fairly-new, enterprise-level feature, advanced segments, to uncover more than ever before about your ecommerce data. #6. 6 Tools to Troubleshoot GA Random: For some reason, I'm reminded of a quote: "So far alls I've come up with is the effects of gasoline. #7. Caitlin Cook Yes, those ecommerce functions sure do ask a lot of you. #8.

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!

The Analytics and Site Intelligence Blog One thing that can be really frustrating in Google Analytics is when you understand the data that you want to see but when you try to drill down into the data, or create a custom report, you aren’t given the right combination of Metrics and Dimensions that you want. Because a lot of Google Analytics users have experienced this, I thought I’d break down both Dimensions and Metrics within GA, and shed some light onto what can and can’t be done with them. Dimensions in Google Analytics Dimensions are characteristics or descriptive attributes of an object. Simply put, they describe the data. Tip: So you don’t confuse them, Dimensions will always be colored green when you add them or create Custom Reports. Metrics in Google Analytics Generally speaking, a Metric is a way to measure your data. Tip: Just like with Dimensions, Metrics will always be colored blue when you add them or create Custom Reports. Why Only Certain Dimensions Appear in Standard Reports Happy Analyzing!

3 Roles in Web Analytics Despite slow economy many companies are hiring web analysts. A quick search on Simplyhired.com, a site that powers the Web Analytics job board on my blog, shows that there are currently 2,007 open positions and indeed.com, another job sites shows over 4800 open positions. That is a huge number. However, many job seekers I have talked to feel frustrated because most of the jobs have a laundry list of requirements and they don’t feel that they are a right fit for most of these open positions. A lot of “Web Analytics” job openings ask for many of the following: Experience in online marketingExperience in Web AnalyticsExperience in – Google analytics, Omniture, Webtrends, Coremetrics etc. Most of the companies looking for a “web analysts” are in one of the following three stages of web analytics staffing Companies who fall in stage 1 and 2 above are the ones who are usually not clear on the role of a “web analyst” and hence create this laundry list of skills. 3 Roles in Web Analytics

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

KAIZEN Analytics 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!

Web Analytics, E-business and Marketing Optimization Blog | WebA This is an article I wrote for Website Magazine in 2011. I recommend that you pick up their latest issue to read many more articles. The American magazine “reaches 142,709 qualified website owners and Internet professionals and the largest audience of website owners and managers in the field.” There are many reasons why your attempts at conversion optimization could fail. If you avoid the mistakes listed in this article, then you will be more likely to succeed — it’s as simple as that. 1. According to renowned psychologist David Keirsey, everyone falls into one of sixteen temperaments. Which temperament are you trying to sell to? My own consultancy firm, inUse Insights (new brand since May 2012: Outfox), has also done similar work, grouping visitors into four types that are illustrated by different birds: owl, penguin, swallow and peacock. 2. Some visitors end up on your website by chance, some because you cater to their interests or needs, and others because of a mistake. 3. 4. 5.

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

Analytics simplify data to amplify its value - James Taylor's De With IBM's announcement this week that it was acquiring SPSS I have been talking to a lot of folks about analytics. Analytics is one of those topics that is often on the edge of what IT people know so I thought a couple of posts on analytics might be useful. Now analytics can mean a lot of things and different people interpret it differently but I always like to go back to one I first heard at FICO: Analytics simplify data to amplify its value This always struck me as going to the core of analytics - the power of analytics to turn huge volumes of data into a much smaller amount of information and insight. People use analytics as a phrase very casually, describing everything from reports to embeddable analytic models built using sophisticated statistical techniques. VisualizationsStatistical analysesData mining resultsPredictive models IT people need to educate themselves on the role of these different kinds of analytics and their potential.

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