Step-by-Step: Linking Your Google Analytics and Optimize Accounts and Installing the Optimize Snippet

To begin using Optimize, you must first setup your account and container (see this post with a Step-by-Step of how to do that), and then link your Google Analytics property to your Optimize account. In this post, I’ll show you how to setup your linking. Step 1: After logging into optimize.google.com, and after having setup your account and container, you should see something like the below container, ready for you to create your first experiment. Before we do that, however, I’d recommend linking your Google Analytics property first (note: you can do this in any order, but to launch an experiment you must have a linked GA property, and the Optimize snippet on your page, so I like to do these steps first). On the right hand nav bard, on step number 2,
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Step-by-Step: Setting Up Your Google Optimize Account and Container

Optimize is Google’s new A/B & Multivariate testing tool. The free version is rolling out in Beta now (sign up here to receive your invitation) and is a great tool to start your Optimization efforts. To begin using Optimize, you must first setup your account. This requires a few key steps, which I’ll go through in detail below. Step 1. Follow the link in your email invite to get to optimize.google.com. You’ll be taken to the below screen with the CTA ‘Get Started’. Email invite to Google Optimize Google Optimize Welcome Screen You’ll then be taken through a couple of screens to choose your email and data privacy settings: Email preferences. I suggest saying Yes to all to stay up to date on announcements, training, etc Data sharing preferences. I suggest saying
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A/B Testing Dried Mangoes for Fun and Learning

Recently, a coworker brought back a bag of dried mangoes from the Philippines as a gift for me, telling me that they are the best dried mangoes in Southeast Asia (hereafter, SEA). This coworker happened to be from the Philippines and was for sure a bit biased in her opinion here… Another coworker sitting next to me overheard the convo and piped in, saying that everyone wants to claim that mangoes from their country are the best. Apparently, this was an ongoing and playfully heated convo in the office. I’ve been known to host data-driven social tasting events in Mountain View for the Google Analytics team, such as a Scotch tasting, Craft Beer and Guac-off, and Whiskey and baked goods. These events are always fun, get people talking and interacting,
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Testing with a Rapid Optimization Plan

If you’ve ever set out to A/B test a whole site redesign, you must have come across the question of ‘What do we do if the new site, that we’ve spent so much time and money on, doesn’t win?’ That’s a fair question. A very fair one. In fact, if you are not asking yourself that question before starting down the road of testing a site redesign, you should reevaluate your testing plan, because it’s a very real possibility that the new site will not, in fact, perform better than the old one. That could happen for many reasons: users are used to your old site, and seeing a new one may be a jarring or disorienting experience to them you may have optimized the heck out of the old
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Data Driven Design

Designing a new website is a big task. You must take many things into consideration: ease of use & functionality, mobile responsiveness, content, flow, graphics, etc. On top of that, you need to ensure that all of the analytics tracking is properly setup and collecting the necessary data for you to report on success. With so many considerations, it’s important to look at what your users are already telling you about it’s ease of use and helpfulness before you begin to make decisions about how to redesign and change it. Key metrics to consider when thinking about a website redesign: – number of unique users & sessions in a given time period – top content by pageviews/events/goal conversions/etc – funnel success (newsletter signups, contact form submits, checkouts, etc) – device
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Test Design: The Official Doc

If you are running a testing program, then you’ve more than likely had to think about what should go into a test you are running. This could include the problem statement, your hypothesis, how many variants, how different these variants will be, what your measures of success will be, screen captures, and more. It’s important to create a doc or some sort of accessible page/application for your teams to be able to reference this information. This helps to foster and open and collaborative culture of optimization. It will also help you as you look back to understand what your test objectives were and how the test did compared to those objectives. I track all of this via a Google Doc for each test I run. I used the same template for
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Step by Step: Setting Up a Quick Onsite Survey

We all love hard data. The facts and figures please. BUT those who really love data know that the secret sauce is to combine ‘hard’ data (quantitative) with ‘soft’ data (qualitative) to really understand the whole picture. There are many qualitative survey tools on the market that can help you do just that. For this post, I’ll talk about Google Consumer Surveys (GCS) as it’s the tool I use most frequently, but there are many others that rank high in terms of ease of use, functionality, and data output (Qualaroo, SurveyMonkey, Foresee, and Opinion Lab, to name a few). A couple of use cases are top of mind for me as a practitioner working with teams that are constantly launching new websites and updating offerings: 1. Task completion (tip of
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Step-by-Step: Setting up a Google Content Experiment on Your Website

Setting up a Google Analytics Content Experiment is easy! Follow this four-step process and you’ll be on your way to running your first test. To start, first go to the ‘Experiments’ section of Google Analytics and click on ‘Create Experiment’. Step 1: Setup the test Advanced: if you are working with a high volume page and want to analyze more than one goal at a time, you can set up a ‘fake goal’ so that the test will not optimize towards a single winner. Use a ‘fake goal’ to run the test longer than 2 weeks: Multi-armed bandit: Content Experiments uses a traffic splitting method called Multi-armed bandit (MAB) which essentially weights the traffic towards the variation(s) that appear to be winning, away from losing variations. In theory, this could
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Your Optimization Program’s First Hire

Just starting out in web optimization? I recently spoke on a panel at Optimizely’s Opticon and one of the questions that came up was ‘who would be your first hire’ for a new optimization program. There were a few different opinions on the panel, ranging from someone who gets stuff done, to an analytics rockstar, to that rare unicorn who can do it all. While all of these are good places to start, I tend to take the viewpoint of optimization through a solid analytics background as the best place to start. (Of course there are many optimization all-stars who didn’t come from an analytics background.) Why? Here are a few of the reasons why I’d look at hiring an analytics rockstar as your optimization lead: 1. They know data
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